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CN116885851A - Monitoring method and system for energy storage power station - Google Patents

Monitoring method and system for energy storage power station Download PDF

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Publication number
CN116885851A
CN116885851A CN202310834791.7A CN202310834791A CN116885851A CN 116885851 A CN116885851 A CN 116885851A CN 202310834791 A CN202310834791 A CN 202310834791A CN 116885851 A CN116885851 A CN 116885851A
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power station
data
risk
energy storage
storage power
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Inventor
雷二涛
金莉
马凯
张浚坤
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Guangdong Power Grid Co Ltd
Electric Power Research Institute of Guangdong Power Grid Co Ltd
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Guangdong Power Grid Co Ltd
Electric Power Research Institute of Guangdong Power Grid Co Ltd
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Priority to CN202310834791.7A priority Critical patent/CN116885851A/en
Publication of CN116885851A publication Critical patent/CN116885851A/en
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    • HELECTRICITY
    • H02GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02JCIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J13/00Circuit arrangements for providing remote indication of network conditions, e.g. an instantaneous record of the open or closed condition of each circuitbreaker in the network; Circuit arrangements for providing remote control of switching means in a power distribution network, e.g. switching in and out of current consumers by using a pulse code signal carried by the network
    • H02J13/00001Circuit arrangements for providing remote indication of network conditions, e.g. an instantaneous record of the open or closed condition of each circuitbreaker in the network; Circuit arrangements for providing remote control of switching means in a power distribution network, e.g. switching in and out of current consumers by using a pulse code signal carried by the network characterised by the display of information or by user interaction, e.g. supervisory control and data acquisition systems [SCADA] or graphical user interfaces [GUI]
    • GPHYSICS
    • G06COMPUTING; CALCULATING OR COUNTING
    • G06QINFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00Administration; Management
    • G06Q10/06Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/063Operations research, analysis or management
    • G06Q10/0635Risk analysis of enterprise or organisation activities
    • GPHYSICS
    • G06COMPUTING; CALCULATING OR COUNTING
    • G06QINFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
    • G06Q50/06Energy or water supply
    • HELECTRICITY
    • H02GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02JCIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J13/00Circuit arrangements for providing remote indication of network conditions, e.g. an instantaneous record of the open or closed condition of each circuitbreaker in the network; Circuit arrangements for providing remote control of switching means in a power distribution network, e.g. switching in and out of current consumers by using a pulse code signal carried by the network
    • H02J13/00002Circuit arrangements for providing remote indication of network conditions, e.g. an instantaneous record of the open or closed condition of each circuitbreaker in the network; Circuit arrangements for providing remote control of switching means in a power distribution network, e.g. switching in and out of current consumers by using a pulse code signal carried by the network characterised by monitoring
    • HELECTRICITY
    • H02GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02JCIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J13/00Circuit arrangements for providing remote indication of network conditions, e.g. an instantaneous record of the open or closed condition of each circuitbreaker in the network; Circuit arrangements for providing remote control of switching means in a power distribution network, e.g. switching in and out of current consumers by using a pulse code signal carried by the network
    • H02J13/00032Systems characterised by the controlled or operated power network elements or equipment, the power network elements or equipment not otherwise provided for
    • HELECTRICITY
    • H02GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02JCIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J15/00Systems for storing electric energy

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  • Engineering & Computer Science (AREA)
  • Business, Economics & Management (AREA)
  • Human Resources & Organizations (AREA)
  • Power Engineering (AREA)
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Abstract

The invention discloses a monitoring method and a system of an energy storage power station, wherein the method comprises the steps of receiving power station data sent by the energy storage power station, carrying out risk association screening according to historical information screening rules and type screening rules to obtain a high risk data set and a high association data set, carrying out data risk prediction to obtain data risk probability of the energy storage power station, carrying out association risk prediction to the high association data set, and obtaining association risk probability corresponding to each power station association set; performing risk comprehensive analysis on the data risk probability and the associated risk probability to obtain a high-risk energy storage power station, generating alarm information according to the high-risk energy storage power station, and performing alarm control; and carrying out risk visualization on the energy storage power station according to the power station association set and the high-risk energy storage power station, generating a risk visualization picture, and pushing the risk visualization picture to the terminal for display. The embodiment realizes effective monitoring and early warning of risks of the energy storage power station, and improves the accuracy and convenience of monitoring of the energy storage power station.

Description

Monitoring method and system for energy storage power station
Technical Field
The invention relates to the field of monitoring data processing, in particular to an energy storage power station monitoring method and system.
Background
As urban electricity demand increases, so does the demand for power generation-related devices, wherein the energy storage power station is used as a core facility in the power generation-related devices or power generation facilities, and the safe operation of the energy storage power station is also paid attention. The monitoring of the energy storage power station is a core technical element for realizing the automatic and efficient operation of the energy storage power station. A large number of high-risk energy storage devices such as a converter and a battery are arranged in the existing energy storage power station, so that the energy storage devices are required to be managed and monitored orderly by combining various information.
In the prior art, most of monitoring systems of energy storage power stations monitor the energy storage power stations according to a traditional power station mode through staff and simple controller data acquisition and according to preset data analysis rules, and the single monitoring mode cannot give consideration to the influence of correlation among all devices in the energy storage power stations, and cannot accurately monitor the energy storage power stations without considering the dangerous situation of comprehensively analyzing the power stations by combining various data effective predictions of the energy storage power stations.
Disclosure of Invention
The invention provides a monitoring method and a system for an energy storage power station, which can effectively monitor and early warn the risk of the energy storage power station, intelligently monitor the energy storage power station and improve the monitoring precision and convenience of the energy storage power station.
In order to solve the above technical problems, an embodiment of the present invention provides a method for monitoring an energy storage power station, including:
receiving power station data sent by a plurality of energy storage power stations, and carrying out risk association screening on each power station data according to historical information screening rules and type screening rules to obtain a plurality of high risk data sets and a plurality of high association data sets; the power station data comprise power station equipment parameters, equipment working parameters and power station internal sensing data;
carrying out data risk prediction on each high risk data set to obtain data risk probability of the energy storage power station corresponding to each high risk data set, and carrying out association risk prediction on each high association data set to obtain association risk probability corresponding to each power station association set; wherein the power station association set comprises a plurality of mutually associated energy storage power stations;
carrying out risk comprehensive analysis on the data risk probability of the energy storage power stations corresponding to each high risk data set and the associated risk probability corresponding to each power station associated set to obtain a plurality of high-risk energy storage power stations, generating alarm information according to each high-risk energy storage power station, and carrying out alarm control according to the alarm information; and carrying out risk visualization on each energy storage power station according to each power station association set and each high-risk energy storage power station, generating a risk visualization picture, and pushing the risk visualization picture to a terminal for display.
According to the embodiment of the invention, power station data sent by a plurality of energy storage power stations are received, and risk association screening is carried out on each power station data according to historical information screening rules and type screening rules to obtain a plurality of high risk data sets and a plurality of high association data sets; the power station data comprise power station equipment parameters, equipment working parameters and power station internal sensing data; carrying out data risk prediction on each high risk data set to obtain data risk probability of the energy storage power station corresponding to each high risk data set, and carrying out association risk prediction on each high association data set to obtain association risk probability corresponding to each power station association set; wherein the power station association set comprises a plurality of mutually associated energy storage power stations; carrying out risk comprehensive analysis on the data risk probability of the energy storage power stations corresponding to each high risk data set and the associated risk probability corresponding to each power station associated set to obtain a plurality of high-risk energy storage power stations, generating alarm information according to each high-risk energy storage power station, and carrying out alarm control according to the alarm information; and carrying out risk visualization on each energy storage power station according to each power station association set and each high-risk energy storage power station, generating a risk visualization picture, and pushing the risk visualization picture to a terminal for display. Through the risk association screening algorithm and the data prediction algorithm, the risk of the energy storage power station can be effectively monitored and early-warned, meanwhile, the intelligent degree and the visual degree of the monitoring of the energy storage power station can be improved, and the accuracy and the convenience of the monitoring are greatly improved.
As a preferred scheme, according to historical information screening rules and type screening rules, risk association screening is carried out on each power station data to obtain a plurality of high risk data sets and a plurality of high association data sets, specifically:
based on the data value of each power station data in the historical time period and the power station dangerous event information, screening each high risk data set from each power station data according to the historical information screening rule;
based on the association parameters among the energy storage power stations corresponding to the power station data, and according to the type screening rule, screening each high association data set from the power station data; each high-association data set comprises at least two power station data belonging to a strong association relationship.
As a preferred scheme, based on the data value of each power station data in the historical time period and the power station dangerous event information, and according to the historical information screening rule, each high risk data set is screened out from each power station data, specifically:
inquiring historical data values corresponding to current power station data at a plurality of historical time points in a database;
inquiring whether power station dangerous event information occurs in the energy storage power station corresponding to the current power station data at a plurality of historical time points or not in a database, and determining the dangerous historical time points of the power station dangerous event occurring in the energy storage power station corresponding to the current power station data; wherein, there are at least two dangerous historical time points;
According to the historical data value of the current power station data and the dangerous historical time point, calculating the similarity of different types among the historical data value corresponding to the dangerous historical time point by using the weighted sum value of any two of the numerical similarity, the section data average value similarity and the adjacent time data change degree similarity, and obtaining the data dangerous similarity of different types corresponding to the current power station data; the similarity of the change degree of the adjacent time data is the similarity of the data change value between the dangerous historical time point and the historical data value of the adjacent historical time point; the average value similarity of the section data is the similarity between the average values of the historical data values of all the historical time points in a time interval formed by the preset time length before and after the location of any two dangerous historical time points; the weight of the similarity of the adjacent time data change degree is larger than that of the average value similarity of the section data, and the weight of the average value similarity of the section data is larger than that of the numerical value similarity;
sequencing the power station data from large to small according to the dangerous similarity of the data of different types of the power station data to obtain at least one data sequence;
The first amount of plant data for each data sequence is determined as each high risk data set.
As a preferred scheme, based on the association parameters between the energy storage power stations corresponding to the power station data, and according to the type screening rule, each high association data set is screened out from the power station data, specifically:
randomly selecting a plurality of power station data from the power station data as current power station data;
acquiring the current power station position, power supply chain and power station equipment parameters of an energy storage power station corresponding to the power station data;
calculating the average value of the distances between the power station positions of the current power station data according to the power station positions of the energy storage power station corresponding to the current power station data;
calculating the similarity of power supply chains among the power supply chains of the power stations of the current power station data according to the power supply chains of the power stations of the energy storage power stations corresponding to the current power station data; the power supply chain similarity is the ratio of the number of the power station data in the same power supply chain among the current power station data to the total number;
calculating the equipment type similarity between the power station equipment parameters of any two power station data according to the power station equipment parameters of the energy storage power station corresponding to the current power station data, and calculating the similarity average value of all the equipment type similarities corresponding to the current power station data;
Carrying out weighted summation on the distance average value, the power supply chain similarity and the similarity average value corresponding to the current power station data to obtain a weighted summation value of the current power station data; the sum of the distance average value, the power supply chain similarity and the similarity average value is 1, and the weights of the distance average value, the power supply chain similarity and the similarity average value are sequentially reduced;
judging whether the weighted sum value of the current power station data is larger than a preset parameter threshold value, if so, taking the current power station data as a high-association data set;
if not, randomly selecting a plurality of power station data from the power station data, re-determining the current power station data, calculating the association similarity according to the current power station data, and calculating the weighted sum value of the current power station data until the weighted sum value of the current power station data is larger than a preset parameter threshold value, so as to obtain each high association data set.
As a preferred scheme, carrying out data risk prediction on each high risk data set to obtain the data risk probability of the energy storage power station corresponding to each high risk data set, specifically:
inputting each power station data in the current high-risk data set into a pre-trained first neural network prediction model to obtain a data risk probability corresponding to each power station data of the current high-risk data set; the first neural network prediction model is obtained through training of a plurality of training power station data and a training data set of data risk labels corresponding to the training power station data;
And obtaining the data risk probability of the energy storage power station corresponding to each high risk data set according to the data risk probability corresponding to each power station data of each high risk data set.
As a preferred scheme, carrying out associated risk prediction on each high associated data set to obtain associated risk probability corresponding to each power station associated set, specifically:
all energy storage power stations corresponding to all power station data in each high-association data set are determined as each power station association set;
inputting all power station data in the current high-association data set into a pre-trained second neural network prediction model to obtain association risk probabilities corresponding to each power station association set corresponding to the current high-association data set; the second neural network prediction model is obtained through training of a training data set comprising a plurality of training power station data corresponding to a plurality of training power station association sets and data risk labels corresponding to the training power station association sets;
and obtaining the associated risk probability corresponding to each power station associated set according to the associated risk probability corresponding to each power station associated set corresponding to each high associated data set.
As a preferred scheme, risk comprehensive analysis is carried out on the data risk probability of the energy storage power station corresponding to each high risk data set and the associated risk probability corresponding to each power station associated set to obtain a plurality of high risk energy storage power stations, specifically:
Judging whether the associated risk probability corresponding to the current power station associated set is higher than a preset probability threshold, if so, determining the current power station associated set as a dangerous power station set;
determining each dangerous power station set according to the associated risk probability corresponding to each power station associated set;
according to the data risk probabilities of the energy storage power stations corresponding to the high-risk data sets, determining the data risk probabilities of all the energy storage power stations in the dangerous power station sets, and carrying out risk weight analysis on the data risk probabilities of all the energy storage power stations in the dangerous power station sets to obtain the high-risk energy storage power stations.
As a preferred scheme, risk weight analysis is carried out on the data risk probabilities corresponding to all the energy storage power stations in each dangerous power station set to obtain each high-risk energy storage power station, which comprises the following specific steps:
calculating the average value of the data risk probabilities corresponding to all power station data corresponding to the current energy storage power station according to the data risk probabilities of the current energy storage power station in the current dangerous power station set to obtain the data risk parameters corresponding to the current energy storage power station;
calculating the average value of the weighted sum values of the distance average value, the power supply chain similarity and the similarity average value between the current energy storage power station and all other energy storage power stations in the same dangerous power station set, and obtaining the corresponding association parameters of the current energy storage power station;
Determining the risk weight corresponding to the current energy storage power station according to the associated parameters corresponding to the current energy storage power station and a preset parameter weight rule; wherein the risk weight is proportional to the associated parameter;
calculating the product of the data risk parameter and the risk weight of the current energy storage power station to obtain the risk value of the current energy storage power station;
calculating the risk values of all the energy storage power stations of all the risk power station sets to obtain all the risk values;
sequencing all energy storage power stations of each dangerous power station set from large to small according to all the dangerous degree values to obtain a power station sequence corresponding to each dangerous power station set;
and determining the first second number of energy storage power stations of the power station sequence corresponding to each dangerous power station set as high-risk energy storage power stations to obtain each high-risk energy storage power station.
As a preferred scheme, according to each power station association set and each high-risk energy storage power station, each energy storage power station is subjected to risk visualization, and a risk visualization picture is generated, specifically:
determining other energy storage power stations in a power station association set to which the current energy storage power station belongs to obtain a plurality of association power stations;
judging whether a power station association set to which the current energy storage power station belongs to a dangerous power station set or not, and obtaining a first judgment result corresponding to the current energy storage power station and each association power station;
Judging whether the current energy storage power station belongs to a high-risk energy storage power station or not, and obtaining a second judging result corresponding to the current energy storage power station;
according to each energy storage power station, a first judgment result and a second judgment result corresponding to each energy storage power station are obtained;
constructing an objective function of a dynamic programming algorithm model; the objective function specifically comprises the following steps: in the relation diagram obtained by calculation, the distance between each energy storage power station and the corresponding associated power station is minimum, and the conspicuity degree of the display parameter corresponding to each energy storage power station is highest; the conspicuity level is used for representing the attraction level of the display parameter; the display parameters comprise a display frame size, a display font size and a specific dimension value of a display color;
determining the constraint condition of the dynamic programming algorithm model comprises: in the relation diagram obtained through calculation, the distance between the energy storage power station and the associated power station is smaller than the distance between the energy storage power station and the non-associated power station; the energy storage power station with the first judging result is more conspicuous than the energy storage power station with the first judging result is negative; the energy storage power station with the second judging result is more conspicuous than the energy storage power station with the first judging result;
based on the objective function and the limiting condition, inputting the parameters of all the energy storage power stations into a dynamic programming algorithm model for iterative calculation to obtain an optimal calculation result meeting the objective function and the limiting condition; the optimal calculation result comprises visual relation diagrams corresponding to all the energy storage power stations.
In order to solve the same technical problems, the embodiment of the invention further provides an energy storage power station monitoring system, which comprises: the energy storage power station monitoring system is used for realizing an energy storage power station monitoring method, and comprises the following steps: the system comprises a data receiving and screening device, a data risk association prediction device and an analysis and monitoring device;
the data receiving and screening device is used for receiving power station data sent by a plurality of energy storage power stations, and carrying out risk association screening on each power station data according to historical information screening rules and type screening rules to obtain a plurality of high risk data sets and a plurality of high association data sets; the power station data comprise power station equipment parameters, equipment working parameters and power station internal sensing data;
the data risk association prediction device is used for carrying out data risk prediction on each high risk data set to obtain data risk probability of the energy storage power station corresponding to each high risk data set, and carrying out association risk prediction on each high association data set to obtain association risk probability corresponding to each power station association set; wherein the power station association set comprises a plurality of mutually associated energy storage power stations;
the analysis monitoring device is used for comprehensively analyzing the risks of the data risk probability of the energy storage power stations corresponding to each high-risk data set and the associated risk probability corresponding to each power station associated set to obtain a plurality of high-risk energy storage power stations, generating alarm information according to each high-risk energy storage power station, and performing alarm control according to the alarm information; and carrying out risk visualization on each energy storage power station according to each power station association set and each high-risk energy storage power station, generating a risk visualization picture, and pushing the risk visualization picture to a terminal for display.
Drawings
Fig. 1: a schematic flow chart of an embodiment of a monitoring method of an energy storage power station is provided by the invention;
fig. 2: the invention provides a structural schematic diagram of one embodiment of an energy storage power station monitoring system;
fig. 3: the invention provides a modularized structural schematic diagram of one embodiment of an energy storage power station monitoring system;
fig. 4: the data screening module of one embodiment of the monitoring system of the energy storage power station is structurally schematic;
fig. 5: the invention provides a structural schematic diagram of a risk comprehensive analysis module of an embodiment of an energy storage power station monitoring system.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
The terms first, second and the like in the description and in the claims and in the above-described figures are used for distinguishing between different objects and not necessarily for describing a sequential or chronological order. Furthermore, the terms "comprise" and "have," as well as any variations thereof, are intended to cover a non-exclusive inclusion. For example, a process, method, apparatus, article, or article that comprises a list of steps or modules is not limited to the list of steps or modules but may, in the alternative, include steps or modules not listed or inherent to such process, method, article, or article.
Reference herein to "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment may be included in at least one embodiment of the invention. The appearances of such phrases in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments. Those of skill in the art will explicitly and implicitly appreciate that the embodiments described herein may be combined with other embodiments.
Example 1
Referring to fig. 1, a flow chart of a monitoring method of an energy storage power station according to an embodiment of the invention is shown. The energy storage power station monitoring method is suitable for automatic monitoring of the energy storage power station, effectively monitors and early warns risks of the energy storage power station through a risk correlation screening algorithm and a data prediction algorithm, intelligently monitors the energy storage power station, and improves accuracy and convenience of monitoring of the energy storage power station. The monitoring method of the energy storage power station comprises the following steps 11 to 13:
step 11: receiving power station data sent by a plurality of energy storage power stations, and carrying out risk association screening on each power station data according to historical information screening rules and type screening rules to obtain a plurality of high risk data sets and a plurality of high association data sets; the power station data comprises power station equipment parameters, equipment working parameters and power station internal sensing data.
In this embodiment, a plurality of power station data of a plurality of energy storage power stations are received and acquired, and at least one high risk data set and at least one high association data set, that is, a plurality of high risk data sets and a plurality of high association data sets, are screened out from the power station data based on a historical information screening rule and a type screening rule, respectively.
By implementing the embodiment of the invention, the power station data is screened to obtain the high-risk data set and the high-association data set, so that the intelligent degree and the visual degree of monitoring of the energy storage power station can be further improved in the follow-up process, and the accuracy and the convenience of monitoring are greatly improved.
Optionally, step 11 specifically includes steps 111 to 113, where each step specifically includes the following steps:
step 111: and receiving power station data sent by a plurality of energy storage power stations.
In this embodiment, the power station is connected to a plurality of energy storage power stations, and receives a plurality of power station data sent by each energy storage power station. The plant data may include plant equipment parameters, plant operating parameters, and plant internal sensor data. The plant equipment parameters may include at least one of converter equipment information, battery equipment information, control rocker equipment information, and switch control equipment information. The device operating parameters may include at least one of a converter start-stop parameter, a converter reactive power parameter, a converter operating state parameter, battery system SOC information, battery system SOH information, battery cell voltage information, battery temperature information, alarm fault information, power distribution device switch information, and a rocker operating state parameter. The power station internal sensing data can comprise at least one of equipment water flow data, equipment water quality data, station internal image data, ambient air quality data, ambient temperature data, ambient humidity data, station internal access control data, station internal door and window monitoring data and station internal staff card punching data.
Step 112: and screening each high-risk data set from each power station data based on the data value of each power station data in the historical time period and the power station dangerous event information according to the historical information screening rule.
In this embodiment, at least one high risk data set is screened from the power station data according to the data value of each power station data in the historical time period and whether the corresponding power station dangerous event information occurs; the high risk data set includes at least one power station data belonging to the type of strong risk correlation.
Optionally, step 112 specifically includes: inquiring historical data values corresponding to current power station data at a plurality of historical time points in a database;
inquiring whether power station dangerous event information occurs in the energy storage power station corresponding to the current power station data at a plurality of historical time points or not in a database, and determining the dangerous historical time points of the power station dangerous event occurring in the energy storage power station corresponding to the current power station data; wherein, there are at least two dangerous historical time points;
according to the historical data value of the current power station data and the dangerous historical time point, calculating the similarity of different types among the historical data value corresponding to the dangerous historical time point by using the weighted sum value of any two of the numerical similarity, the section data average value similarity and the adjacent time data change degree similarity, and obtaining the data dangerous similarity of different types corresponding to the current power station data; the similarity of the change degree of the adjacent time data is the similarity of the data change value between the dangerous historical time point and the historical data value of the adjacent historical time point; the average value similarity of the section data is the similarity between the average values of the historical data values of all the historical time points in a time interval formed by the preset time length before and after the location of any two dangerous historical time points; the weight of the similarity of the adjacent time data change degree is larger than that of the average value similarity of the section data, and the weight of the average value similarity of the section data is larger than that of the numerical value similarity;
Sequencing the power station data from large to small according to the dangerous similarity of the data of different types of the power station data to obtain at least one data sequence;
the first amount of plant data for each data sequence is determined as each high risk data set.
In this embodiment, for each power station data, a database is queried for historical data values at a plurality of historical time points corresponding to the power station data;
inquiring whether power station dangerous event information occurs in the energy storage power station corresponding to the power station data at a plurality of historical time points or not in a database so as to determine at least two dangerous historical time points when the power station dangerous event occurs in the energy storage power station;
calculating different types of similarity between historical data values corresponding to at least two dangerous historical time points to obtain different types of data dangerous similarity corresponding to the power station data; the similarity comprises a weighted sum value of any two of numerical similarity, section data average value similarity and adjacent time data change degree similarity; the weight of the adjacent time data change degree similarity, the section data average value similarity and the numerical value similarity is gradually reduced; the degree of similarity of adjacent temporal data change is the similarity between the data change values between the historical data values of two hazard historical time points and adjacent preceding and/or following time points; the section data average value similarity is the similarity between the average values of the historical data values of all the historical time points in a time interval formed by the preset time length before and after the two dangerous historical time points;
Sequencing all power station data from large to small according to the dangerous similarity of different types of data to obtain at least one data sequence;
the first number of plant data of each data sequence is determined as a high risk data set.
Through the step of screening each high risk data set, the risk degree of different power station data can be determined according to various similarities of the historical data values at the time points when the risk time occurs, and the data with high correlation degree with the risk is screened out to conduct subsequent risk prediction, so that the prediction precision can be effectively improved.
Step 113: based on the association parameters among the energy storage power stations corresponding to the power station data, and according to the type screening rule, screening each high association data set from the power station data; each high-association data set comprises at least two power station data belonging to a strong association relationship.
In this embodiment, at least one high-association data set is screened from the power station data according to association parameters between the energy storage power stations corresponding to at least two power station data; the high-association data set comprises at least two power station data belonging to a strong association relationship.
Optionally, step 113 specifically includes: randomly selecting a plurality of power station data from the power station data as current power station data;
Acquiring the current power station position, power supply chain and power station equipment parameters of an energy storage power station corresponding to the power station data;
calculating the average value of the distances between the power station positions of the current power station data according to the power station positions of the energy storage power station corresponding to the current power station data;
calculating the similarity of power supply chains among the power supply chains of the power stations of the current power station data according to the power supply chains of the power stations of the energy storage power stations corresponding to the current power station data; the power supply chain similarity is the ratio of the number of the power station data in the same power supply chain among the current power station data to the total number;
calculating the equipment type similarity between the power station equipment parameters of any two power station data according to the power station equipment parameters of the energy storage power station corresponding to the current power station data, and calculating the similarity average value of all the equipment type similarities corresponding to the current power station data;
carrying out weighted summation on the distance average value, the power supply chain similarity and the similarity average value corresponding to the current power station data to obtain a weighted summation value of the current power station data; the sum of the distance average value, the power supply chain similarity and the similarity average value is 1, and the weights of the distance average value, the power supply chain similarity and the similarity average value are sequentially reduced;
Judging whether the weighted sum value of the current power station data is larger than a preset parameter threshold value, if so, taking the current power station data as a high-association data set;
if not, randomly selecting a plurality of power station data from the power station data, re-determining the current power station data, calculating the association similarity according to the current power station data, and calculating the weighted sum value of the current power station data until the weighted sum value of the current power station data is larger than a preset parameter threshold value, so as to obtain each high association data set.
In this embodiment, for any plurality of power station data, determining a power station position of an energy storage power station, a power supply chain where the power station is located, and a power station equipment parameter corresponding to the plurality of power station data;
calculating a distance average between the plant locations of the plurality of plant data;
calculating the similarity of power supply chains among the power supply chains of the power stations of the power station data; the power supply chain similarity is the ratio of the number of the power station data in the same power supply chain among the plurality of power station data to the total number;
calculating the equipment type similarity between the power station equipment parameters of any two power station data, and calculating the similarity average value of all the equipment type similarities corresponding to the plurality of power station data;
Calculating a weighted sum value of distance average values, power supply chain similarity and similarity average values corresponding to the power station data; wherein, the sum of the distance average value, the power supply chain similarity and the similarity average value is 1; the distance average value, the power supply chain similarity and the weight of the similarity average value are sequentially reduced;
judging whether the weighted sum value is larger than a preset parameter threshold value, if so, determining the plurality of power station data as a high-association data set; if not, the plurality of power station data are not determined to be a high-association data set, and the plurality of power station data can be selected again and arbitrarily, and corresponding calculation is performed to judge whether the plurality of power station data belong to the high-association data set or not so as to obtain a plurality of high-association data sets.
Through the step of screening out each high-association data set, the association degree between different power stations can be determined according to different association information of the power stations, such as the distance between the power stations and the similarity of power supply chains or the similarity of equipment types, and the data of the power stations with high association with each other are screened out to conduct subsequent risk prediction, so that the prediction precision and the association degree can be effectively improved, and the association degree between dangerous events can be found.
Step 12: carrying out data risk prediction on each high risk data set to obtain data risk probability of the energy storage power station corresponding to each high risk data set, and carrying out association risk prediction on each high association data set to obtain association risk probability corresponding to each power station association set; wherein the power plant association set comprises a plurality of interrelated energy storage power plants.
In this embodiment, according to any high-risk data set, the data risk probability corresponding to any energy storage power station is predicted, that is, the data risk probability of the energy storage power station corresponding to each high-risk data set. Predicting the associated risk probability corresponding to any power station associated set according to any high associated data set; the power station association set comprises a plurality of mutually associated energy storage power stations.
Optionally, step 12 specifically includes steps 121 to 122, where each step specifically includes the following steps:
step 121: inputting each power station data in the current high-risk data set into a pre-trained first neural network prediction model to obtain a data risk probability corresponding to each power station data of the current high-risk data set; the first neural network prediction model is obtained through training of a plurality of training power station data and a training data set of data risk labels corresponding to the training power station data;
And obtaining the data risk probability of the energy storage power station corresponding to each high risk data set according to the data risk probability corresponding to each power station data of each high risk data set.
In this embodiment, each power station data in the high risk data set is input into a pre-trained first neural network prediction model to obtain a data risk probability corresponding to each power station data; the first neural network prediction model is trained from a training dataset that may include a plurality of training power plant data and corresponding data risk annotations.
As an example of this embodiment, the first neural network prediction model may be at least one or a combination of a CNN structure network model, an RNN structure network model, an LTSM structure network model, or other models such as a random forest algorithm model, which is not limited by the present invention.
By implementing the embodiment of the invention, the data risk probability corresponding to each power station data is determined through the first neural network prediction model, so that the prediction accuracy and the correlation degree can be effectively improved.
Step 122: all energy storage power stations corresponding to all power station data in each high-association data set are determined as each power station association set;
Inputting all power station data in the current high-association data set into a pre-trained second neural network prediction model to obtain association risk probabilities corresponding to each power station association set corresponding to the current high-association data set; the second neural network prediction model is obtained through training of a training data set comprising a plurality of training power station data corresponding to a plurality of training power station association sets and data risk labels corresponding to the training power station association sets;
and obtaining the associated risk probability corresponding to each power station associated set according to the associated risk probability corresponding to each power station associated set corresponding to each high associated data set.
In this embodiment, all energy storage power stations corresponding to all power station data in each high-association data set are determined as one power station association set;
inputting all power station data in each high-association data set into a pre-trained second neural network prediction model to obtain association risk probability corresponding to each corresponding power station association set; the second neural network prediction model is obtained through training of a training data set comprising a plurality of training power station data corresponding to a plurality of training power station association sets and corresponding data risk labels.
As an example of this embodiment, the second neural network prediction model may be at least one or a combination of a CNN structure network model, an RNN structure network model, an LTSM structure network model, or other models such as a random forest algorithm model, which is not limited by the present invention.
According to the embodiment of the invention, the associated risk probability corresponding to each power station associated set is determined through the second neural network prediction model, so that the prediction precision and the correlation degree can be effectively improved, meanwhile, the training of the second neural network prediction model is actually trained by training data related to the first neural network prediction model, and the model parameters of the second neural network prediction model are also extracted to obtain the parameter characteristics related to the first neural network prediction model, so that the analysis effect is more practical value and the prediction effect is better when the prediction results of the two models are used for cross analysis in the follow-up.
Step 13: carrying out risk comprehensive analysis on the data risk probability of the energy storage power stations corresponding to each high risk data set and the associated risk probability corresponding to each power station associated set to obtain a plurality of high-risk energy storage power stations, generating alarm information according to each high-risk energy storage power station, and carrying out alarm control according to the alarm information; and carrying out risk visualization on each energy storage power station according to each power station association set and each high-risk energy storage power station, generating a risk visualization picture, and pushing the risk visualization picture to a terminal for display.
In this embodiment, according to the data risk probability and the associated risk probability, at least one high-risk energy storage power station is determined from all the energy storage power stations, alarm information is sent to a terminal corresponding to the high-risk energy storage power station to alarm, and according to the data risk probability and the associated risk probability, risk visualization pictures corresponding to a plurality of energy storage power stations are generated to be pushed to the terminal to display, so that risk monitoring and early warning risk display are realized.
Optionally, performing risk comprehensive analysis on the data risk probability of the energy storage power station corresponding to each high risk data set and the associated risk probability corresponding to each power station associated set to obtain a plurality of high risk energy storage power stations, which specifically includes:
judging whether the associated risk probability corresponding to the current power station associated set is higher than a preset probability threshold, if so, determining the current power station associated set as a dangerous power station set;
determining each dangerous power station set according to the associated risk probability corresponding to each power station associated set;
according to the data risk probabilities of the energy storage power stations corresponding to the high-risk data sets, determining the data risk probabilities of all the energy storage power stations in the dangerous power station sets, and carrying out risk weight analysis on the data risk probabilities of all the energy storage power stations in the dangerous power station sets to obtain the high-risk energy storage power stations.
In this embodiment, for each power station association set, it is determined whether the association risk probability corresponding to the power station association set is higher than a preset probability threshold, and if yes, the power station association set is determined to be a dangerous power station set.
Optionally, risk weight analysis is performed on the data risk probabilities corresponding to all the energy storage power stations in each dangerous power station set to obtain each high-risk energy storage power station, which specifically comprises the following steps:
calculating the average value of the data risk probabilities corresponding to all power station data corresponding to the current energy storage power station according to the data risk probabilities of the current energy storage power station in the current dangerous power station set to obtain the data risk parameters corresponding to the current energy storage power station;
calculating the average value of the weighted sum values of the distance average value, the power supply chain similarity and the similarity average value between the current energy storage power station and all other energy storage power stations in the same dangerous power station set, and obtaining the corresponding association parameters of the current energy storage power station;
determining the risk weight corresponding to the current energy storage power station according to the associated parameters corresponding to the current energy storage power station and a preset parameter weight rule; wherein the risk weight is proportional to the associated parameter;
calculating the product of the data risk parameter and the risk weight of the current energy storage power station to obtain the risk value of the current energy storage power station;
Calculating the risk values of all the energy storage power stations of all the risk power station sets to obtain all the risk values;
sequencing all energy storage power stations of each dangerous power station set from large to small according to all the dangerous degree values to obtain a power station sequence corresponding to each dangerous power station set;
and determining the first second number of energy storage power stations of the power station sequence corresponding to each dangerous power station set as high-risk energy storage power stations to obtain each high-risk energy storage power station.
In this embodiment, according to the data risk probabilities corresponding to all the energy storage power stations in the dangerous power station set, at least one high-risk energy storage power station is determined from the dangerous power station set. The process for determining the high-risk energy storage power station comprises the following steps: for any energy storage power station in any dangerous power station set, calculating the average value of all power station data corresponding data risk probabilities corresponding to the energy storage power station to obtain corresponding data risk parameters of the energy storage power station; calculating the average value of the weighted sum values of the distance average value, the power supply chain similarity and the similarity average value between the energy storage power station and all other energy storage power stations in the same dangerous power station set, and obtaining the corresponding association parameters of the energy storage power station; determining a risk weight corresponding to the energy storage power station according to the associated parameter and a preset parameter-weight rule (the parameter-weight rule can determine a formula for the preset weight and limit the risk weight to be in direct proportion to the associated parameter); the risk weight is proportional to the associated parameter; calculating the product of the data risk parameter and the risk weight of the energy storage power station; sequencing all energy storage power stations in each dangerous power station set according to the product from large to small to obtain a power station sequence corresponding to each dangerous power station set; and determining the first second number of energy storage power stations of the power station sequence corresponding to each dangerous power station set as a high-risk energy storage power station.
Optionally, according to each power station association set and each high-risk energy storage power station, each energy storage power station is subjected to risk visualization, and a risk visualization picture is generated, specifically:
determining other energy storage power stations in a power station association set to which the current energy storage power station belongs to obtain a plurality of association power stations;
judging whether a power station association set to which the current energy storage power station belongs to a dangerous power station set or not, and obtaining a first judgment result corresponding to the current energy storage power station and each association power station;
judging whether the current energy storage power station belongs to a high-risk energy storage power station or not, and obtaining a second judging result corresponding to the current energy storage power station;
according to each energy storage power station, a first judgment result and a second judgment result corresponding to each energy storage power station are obtained;
constructing an objective function of a dynamic programming algorithm model; the objective function specifically comprises the following steps: in the relation diagram obtained by calculation, the distance between each energy storage power station and the corresponding associated power station is minimum, and the conspicuity degree of the display parameter corresponding to each energy storage power station is highest; the conspicuity level is used for representing the attraction level of the display parameter; the display parameters comprise a display frame size, a display font size and a specific dimension value of a display color;
Determining the constraint condition of the dynamic programming algorithm model comprises: in the relation diagram obtained through calculation, the distance between the energy storage power station and the associated power station is smaller than the distance between the energy storage power station and the non-associated power station; the energy storage power station with the first judging result is more conspicuous than the energy storage power station with the first judging result is negative; the energy storage power station with the second judging result is more conspicuous than the energy storage power station with the first judging result;
based on the objective function and the limiting condition, inputting the parameters of all the energy storage power stations into a dynamic programming algorithm model for iterative calculation to obtain an optimal calculation result meeting the objective function and the limiting condition; the optimal calculation result comprises visual relation diagrams corresponding to all the energy storage power stations.
In this embodiment, for each energy storage power station, determining other energy storage power stations in a power station association set to which the energy storage power station belongs, to obtain a plurality of associated power stations; judging whether a power station association set to which the energy storage power station belongs to a dangerous power station set or not, and obtaining a first judgment result corresponding to the energy storage power station and the association power station; judging whether the energy storage power station belongs to a high-risk energy storage power station or not, and obtaining a second judging result corresponding to the energy storage power station; determining the objective function of the dynamic programming algorithm model may include: in the relation diagram obtained by calculation, the distance between each energy storage power station and the corresponding associated power station is minimum, and the conspicuity degree of the display parameter corresponding to each energy storage power station is highest; the conspicuity level is used for representing the attraction level of the display parameter; the display parameters may include a display frame size, a display font size, and/or a particular dimension value of a display color; determining the constraints of the dynamic programming algorithm model may include: in the relation diagram obtained through calculation, the distance between the energy storage power station and the associated power station is smaller than the distance between the energy storage power station and the non-associated power station; the energy storage power station with the first judging result is more conspicuous than the energy storage power station with the first judging result is negative; the energy storage power station with the second judging result is more conspicuous than the energy storage power station with the first judging result; based on the objective function and the limiting condition, inputting parameters of all the energy storage power stations into a dynamic programming algorithm model for iterative calculation so as to obtain an optimal calculation result meeting the objective function and the limiting condition; the optimal calculation result can comprise a visual relation diagram corresponding to all the energy storage power stations; and pushing the visual relation diagram to the terminal for display.
As an example of this embodiment, the visual relationship chart may include a map of an area where a plurality of energy storage power stations are located, and a position where each energy storage power station is located in the map, then, the display parameters such as the icon size or color of each energy storage power station include the dynamic programming algorithm, for example, a particle swarm algorithm, and by adjusting the icon size, the distance between the energy storage power stations, that is, the distance refers to the distance of the map on the visual relationship chart, and this distance may be the distance between the closest edge points of two icons, so that the calculation of this distance also includes the calculation of the icon size.
In other embodiments, since the positions between the energy storage power stations may be fixed, the calculation results in a distance that does not achieve a good effect, and the visual relationship diagram is simply calculated into a drawing that only includes a plurality of energy storage power stations, where the distance or the display parameter between the energy storage power stations matches the above-mentioned limitation condition.
According to the embodiment of the invention, power station data sent by a plurality of energy storage power stations are received, and risk association screening is carried out on each power station data according to historical information screening rules and type screening rules to obtain a plurality of high risk data sets and a plurality of high association data sets; the power station data comprise power station equipment parameters, equipment working parameters and power station internal sensing data; carrying out data risk prediction on each high risk data set to obtain data risk probability of the energy storage power station corresponding to each high risk data set, and carrying out association risk prediction on each high association data set to obtain association risk probability corresponding to each power station association set; wherein the power station association set comprises a plurality of mutually associated energy storage power stations; carrying out risk comprehensive analysis on the data risk probability of the energy storage power stations corresponding to each high risk data set and the associated risk probability corresponding to each power station associated set to obtain a plurality of high-risk energy storage power stations, generating alarm information according to each high-risk energy storage power station, and carrying out alarm control according to the alarm information; and carrying out risk visualization on each energy storage power station according to each power station association set and each high-risk energy storage power station, generating a risk visualization picture, and pushing the risk visualization picture to a terminal for display. Through the data screening algorithm and the data prediction algorithm, risks of the energy storage power station can be effectively monitored and early-warned, and meanwhile, through the data visualization processing algorithm, the intelligent degree and the visualization degree of the monitoring of the energy storage power station can be improved, and the accuracy and the convenience of the monitoring are greatly improved.
Example two
Correspondingly, referring to fig. 2, fig. 2 is a schematic structural diagram of a second embodiment of the monitoring system for an energy storage power station according to the present invention. As shown in fig. 2, the energy storage power station monitoring system is used for implementing an energy storage power station monitoring method, and the energy storage power station monitoring system comprises a data receiving and screening device 201, a data risk association prediction device 202 and an analysis and monitoring device 203;
in this embodiment, as shown in fig. 3, the energy storage power station monitoring system includes a data receiving module 101, a data screening module 102, a data risk prediction module 103, an associated risk prediction module 104, a risk comprehensive analysis module 105, and a risk visualization module 106. Specifically, the data receiving and screening device 201 includes a data receiving module 101 and a data screening module 102, the data risk association prediction device 202 includes a data risk prediction module 103 and an association risk prediction module 104, and the analysis and monitoring device 203 includes a risk comprehensive analysis module 105 and a risk visualization module 106.
The data receiving and screening device 201 is configured to receive power station data sent by a plurality of energy storage power stations, and perform risk association screening on each power station data according to historical information screening rules and type screening rules to obtain a plurality of high risk data sets and a plurality of high association data sets; the power station data comprises power station equipment parameters, equipment working parameters and power station internal sensing data.
Optionally, the data receiving and screening device 201 includes a data receiving module 101 and a data screening module 102, where the data receiving module 101 is configured to receive power station data sent by a plurality of energy storage power stations, and the data screening module 102 is configured to perform risk association screening on each power station data according to historical information screening rules and type screening rules, so as to obtain a plurality of high risk data sets and a plurality of high association data sets.
In this embodiment, as shown in fig. 4, the data filtering module 102 includes: a history screening unit 1021 and a type screening unit 1022. The history screening unit 1021 is configured to screen each high risk data set from each power station data based on the data value of each power station data in the history period and the power station dangerous event information, and according to the history information screening rule. The type screening unit 1022 is configured to screen each high-association data set from each power station data based on association parameters between energy storage power stations corresponding to each power station data, and according to a type screening rule; each high-association data set comprises at least two power station data belonging to a strong association relationship.
Optionally, the history screening unit 1021 specifically performs the following steps to screen out the high risk data set: inquiring historical data values corresponding to current power station data at a plurality of historical time points in a database;
Inquiring whether power station dangerous event information occurs in the energy storage power station corresponding to the current power station data at a plurality of historical time points or not in a database, and determining the dangerous historical time points of the power station dangerous event occurring in the energy storage power station corresponding to the current power station data; wherein, there are at least two dangerous historical time points;
according to the historical data value of the current power station data and the dangerous historical time point, calculating the similarity of different types among the historical data value corresponding to the dangerous historical time point by using the weighted sum value of any two of the numerical similarity, the section data average value similarity and the adjacent time data change degree similarity, and obtaining the data dangerous similarity of different types corresponding to the current power station data; the similarity of the change degree of the adjacent time data is the similarity of the data change value between the dangerous historical time point and the historical data value of the adjacent historical time point; the average value similarity of the section data is the similarity between the average values of the historical data values of all the historical time points in a time interval formed by the preset time length before and after the location of any two dangerous historical time points; the weight of the similarity of the adjacent time data change degree is larger than that of the average value similarity of the section data, and the weight of the average value similarity of the section data is larger than that of the numerical value similarity;
Sequencing the power station data from large to small according to the dangerous similarity of the data of different types of the power station data to obtain at least one data sequence;
the first amount of plant data for each data sequence is determined as each high risk data set.
Optionally, the type filtering unit 1022 determines the high-association data set by performing the following steps: randomly selecting a plurality of power station data from the power station data as current power station data;
acquiring the current power station position, power supply chain and power station equipment parameters of an energy storage power station corresponding to the power station data;
calculating the average value of the distances between the power station positions of the current power station data according to the power station positions of the energy storage power station corresponding to the current power station data;
calculating the similarity of power supply chains among the power supply chains of the power stations of the current power station data according to the power supply chains of the power stations of the energy storage power stations corresponding to the current power station data; the power supply chain similarity is the ratio of the number of the power station data in the same power supply chain among the current power station data to the total number;
calculating the equipment type similarity between the power station equipment parameters of any two power station data according to the power station equipment parameters of the energy storage power station corresponding to the current power station data, and calculating the similarity average value of all the equipment type similarities corresponding to the current power station data;
Carrying out weighted summation on the distance average value, the power supply chain similarity and the similarity average value corresponding to the current power station data to obtain a weighted summation value of the current power station data; the sum of the distance average value, the power supply chain similarity and the similarity average value is 1, and the weights of the distance average value, the power supply chain similarity and the similarity average value are sequentially reduced;
judging whether the weighted sum value of the current power station data is larger than a preset parameter threshold value, if so, taking the current power station data as a high-association data set;
if not, randomly selecting a plurality of power station data from the power station data, re-determining the current power station data, calculating the association similarity according to the current power station data, and calculating the weighted sum value of the current power station data until the weighted sum value of the current power station data is larger than a preset parameter threshold value, so as to obtain each high association data set.
The data risk association prediction device 202 is configured to perform data risk prediction on each high risk data set to obtain data risk probabilities of energy storage power stations corresponding to each high risk data set, and perform association risk prediction on each high association data set to obtain association risk probabilities corresponding to each power station association set; wherein the power plant association set comprises a plurality of interrelated energy storage power plants.
Optionally, the data risk association prediction device 202 includes a data risk prediction module 103 and an association risk prediction module 104, where the data risk prediction module 103 is configured to perform data risk prediction on each high risk data set to obtain a data risk probability of an energy storage power station corresponding to each high risk data set, and the association risk prediction module 104 is configured to perform association risk prediction on each high association data set to obtain an association risk probability corresponding to each power station association set.
Optionally, the data risk prediction module 103 is specifically configured to perform the following steps:
inputting each power station data in the current high-risk data set into a pre-trained first neural network prediction model to obtain a data risk probability corresponding to each power station data of the current high-risk data set; the first neural network prediction model is obtained through training of a plurality of training power station data and a training data set of data risk labels corresponding to the training power station data;
and obtaining the data risk probability of the energy storage power station corresponding to each high risk data set according to the data risk probability corresponding to each power station data of each high risk data set.
Optionally, the associated risk prediction module 104 is specifically configured to perform the following steps:
All energy storage power stations corresponding to all power station data in each high-association data set are determined as each power station association set;
inputting all power station data in the current high-association data set into a pre-trained second neural network prediction model to obtain association risk probabilities corresponding to each power station association set corresponding to the current high-association data set; the second neural network prediction model is obtained through training of a training data set comprising a plurality of training power station data corresponding to a plurality of training power station association sets and data risk labels corresponding to the training power station association sets;
and obtaining the associated risk probability corresponding to each power station associated set according to the associated risk probability corresponding to each power station associated set corresponding to each high associated data set.
The analysis monitoring device 203 is configured to perform risk comprehensive analysis on the data risk probability of the energy storage power station corresponding to each high risk data set and the associated risk probability corresponding to each power station associated set, obtain a plurality of high risk energy storage power stations, generate alarm information according to each high risk energy storage power station, and perform alarm control according to the alarm information; and carrying out risk visualization on each energy storage power station according to each power station association set and each high-risk energy storage power station, generating a risk visualization picture, and pushing the risk visualization picture to a terminal for display.
Optionally, the analysis monitoring device 203 includes a risk comprehensive analysis module 105 and a risk visualization module 106, where the risk comprehensive analysis module 105 is configured to perform risk comprehensive analysis on a data risk probability of an energy storage power station corresponding to each high risk data set and an associated risk probability corresponding to each power station association set, obtain a plurality of high-risk energy storage power stations, generate alarm information according to each high-risk energy storage power station, perform alarm control according to the alarm information, and the risk visualization module 106 is configured to perform risk visualization on each energy storage power station according to each power station association set and each high-risk energy storage power station, generate a risk visualization picture, and push the risk visualization picture to the terminal for display.
In the present embodiment, as shown in fig. 5, the risk integrated analysis module 105 includes a first analysis unit 1051, a second analysis unit 1052, and an alarm unit 1053.
The first analysis unit 1051 is configured to determine, for each power station association set, whether an association risk probability corresponding to the power station association set is higher than a preset probability threshold, and if yes, determine the power station association set as a dangerous power station set;
optionally, the first analysis unit 1051 is specifically configured to perform the following steps:
Judging whether the associated risk probability corresponding to the current power station associated set is higher than a preset probability threshold, if so, determining the current power station associated set as a dangerous power station set;
determining each dangerous power station set according to the associated risk probability corresponding to each power station associated set;
according to the data risk probabilities of the energy storage power stations corresponding to the high-risk data sets, determining the data risk probabilities of all the energy storage power stations in the dangerous power station sets, and carrying out risk weight analysis on the data risk probabilities of all the energy storage power stations in the dangerous power station sets to obtain the high-risk energy storage power stations.
The second analysis unit 1052 is configured to perform risk weight analysis on the data risk probabilities corresponding to all the energy storage power stations in each dangerous power station set, so as to obtain each high-risk energy storage power station;
optionally, the second analysis unit 1052 is specifically configured to perform the following steps:
calculating the average value of the data risk probabilities corresponding to all power station data corresponding to the current energy storage power station according to the data risk probabilities of the current energy storage power station in the current dangerous power station set to obtain the data risk parameters corresponding to the current energy storage power station;
calculating the average value of the weighted sum values of the distance average value, the power supply chain similarity and the similarity average value between the current energy storage power station and all other energy storage power stations in the same dangerous power station set, and obtaining the corresponding association parameters of the current energy storage power station;
Determining the risk weight corresponding to the current energy storage power station according to the associated parameters corresponding to the current energy storage power station and a preset parameter weight rule; wherein the risk weight is proportional to the associated parameter;
calculating the product of the data risk parameter and the risk weight of the current energy storage power station to obtain the risk value of the current energy storage power station;
calculating the risk values of all the energy storage power stations of all the risk power station sets to obtain all the risk values;
sequencing all energy storage power stations of each dangerous power station set from large to small according to all the dangerous degree values to obtain a power station sequence corresponding to each dangerous power station set;
and determining the first second number of energy storage power stations of the power station sequence corresponding to each dangerous power station set as high-risk energy storage power stations to obtain each high-risk energy storage power station.
And the alarm unit 1053 is used for generating alarm information according to each high-risk energy storage power station and performing alarm control according to the alarm information.
In this embodiment, the risk visualization module 106 is specifically configured to perform the following steps:
determining other energy storage power stations in a power station association set to which the current energy storage power station belongs to obtain a plurality of association power stations;
judging whether a power station association set to which the current energy storage power station belongs to a dangerous power station set or not, and obtaining a first judgment result corresponding to the current energy storage power station and each association power station;
Judging whether the current energy storage power station belongs to a high-risk energy storage power station or not, and obtaining a second judging result corresponding to the current energy storage power station;
according to each energy storage power station, a first judgment result and a second judgment result corresponding to each energy storage power station are obtained;
constructing an objective function of a dynamic programming algorithm model; the objective function specifically comprises the following steps: in the relation diagram obtained by calculation, the distance between each energy storage power station and the corresponding associated power station is minimum, and the conspicuity degree of the display parameter corresponding to each energy storage power station is highest; the conspicuity level is used for representing the attraction level of the display parameter; the display parameters comprise a display frame size, a display font size and a specific dimension value of a display color;
determining the constraint condition of the dynamic programming algorithm model comprises: in the relation diagram obtained through calculation, the distance between the energy storage power station and the associated power station is smaller than the distance between the energy storage power station and the non-associated power station; the energy storage power station with the first judging result is more conspicuous than the energy storage power station with the first judging result is negative; the energy storage power station with the second judging result is more conspicuous than the energy storage power station with the first judging result;
based on the objective function and the limiting condition, inputting the parameters of all the energy storage power stations into a dynamic programming algorithm model for iterative calculation to obtain an optimal calculation result meeting the objective function and the limiting condition; the optimal calculation result comprises visual relation diagrams corresponding to all the energy storage power stations;
And pushing the visual relation diagram to the terminal for display.
According to the application, the risk of the energy storage power station can be effectively monitored and early-warned through the data screening algorithm and the data prediction algorithm, and meanwhile, the intelligent degree and the visual degree of the monitoring of the energy storage power station can be improved through the data visual processing algorithm, so that the accuracy and the convenience of the monitoring are greatly improved.
The energy storage power station monitoring system can implement the energy storage power station monitoring method of the method embodiment. The options in the method embodiments described above are also applicable to this embodiment and will not be described in detail here. The rest of the embodiments of the present application may refer to the content of the above method embodiments, and in this embodiment, no further description is given.
The foregoing describes certain embodiments of the present disclosure, other embodiments being within the scope of the following claims. In some cases, the actions or steps recited in the claims can be performed in a different order than in the embodiments and still achieve desirable results. Furthermore, the processes depicted in the accompanying drawings do not necessarily have to be in the particular order shown, or sequential order, to achieve desirable results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.
In this specification, each embodiment is described in a progressive manner, and identical and similar parts of each embodiment are all referred to each other, and each embodiment mainly describes differences from other embodiments. In particular, for apparatus, devices, non-transitory computer readable storage medium embodiments, the description is relatively simple, as it is substantially similar to method embodiments, with reference to portions of the description of method embodiments being relevant.
The apparatus, the device, the nonvolatile computer readable storage medium and the method provided in the embodiments of the present disclosure correspond to each other, and therefore, the apparatus, the device, and the nonvolatile computer storage medium also have similar advantageous technical effects as those of the corresponding method, and since the advantageous technical effects of the method have been described in detail above, the advantageous technical effects of the corresponding apparatus, device, and nonvolatile computer storage medium are not described herein again.
In the 90 s of the 20 th century, improvements to one technology could clearly be distinguished as improvements in hardware (e.g., improvements to circuit structures such as diodes, transistors, switches, etc.) or software (improvements to the process flow). However, with the development of technology, many improvements of the current method flows can be regarded as direct improvements of hardware circuit structures. Designers almost always obtain corresponding hardware circuit structures by programming improved method flows into hardware circuits. Therefore, an improvement of a method flow cannot be said to be realized by a hardware entity module. For example, a programmable logic device (Programmable Logic Device, PLD) (e.g., a field programmable gate array (Field Programmable gate array, FPGA)) is an integrated circuit whose logic function is determined by the user programming the device. A designer programs to "integrate" a digital system onto a PLD without requiring the chip manufacturer to design and fabricate application-specific integrated circuit chips. Moreover, nowadays, instead of manually manufacturing integrated circuit chips, such programming is mostly implemented by using "logic compiler" software, which is similar to the software compiler used in program development and writing, and the original code before the compiling is also written in a specific programming language, which is called hardware description language (Hardware Description Language, HDL), but not just one of the hdds, but a plurality of kinds, such as ABEL (Advanced Boolean Expression Language), AHDL (Altera Hardware DescriptionLanguage), confluence, CUPL (Cornell University Programming Language), HDCal, JHDL (Java Hardware Description Language), lava, lola, myHDL, PALASM, RHDL (RubyHardware Description Language), etc., VHDL (Very-High-SpeedIntegrated Circuit Hardware Description Language) and Verilog are currently most commonly used. It will also be apparent to those skilled in the art that a hardware circuit implementing the logic method flow can be readily obtained by merely slightly programming the method flow into an integrated circuit using several of the hardware description languages described above.
The controller may be implemented in any suitable manner, for example, the controller may take the form of, for example, a microprocessor or processor and a computer readable medium storing computer readable program code (e.g., software or firmware) executable by the (micro) processor, logic gates, switches, application specific integrated circuits (Application Specific Integrated Circuit, ASIC), programmable logic controllers, and embedded microcontrollers, examples of which include, but are not limited to, the following microcontrollers: ARC 625D, atmel AT91SAM, microchip PIC18F26K20, and Silicone Labs C8051F320, the memory controller may also be implemented as part of the control logic of the memory. Those skilled in the art will also appreciate that, in addition to implementing the controller in a pure computer readable program code, it is well possible to implement the same functionality by logically programming the method steps such that the controller is in the form of logic gates, switches, application specific integrated circuits, programmable logic controllers, embedded microcontrollers, etc. Such a controller may thus be regarded as a kind of hardware component, and means for performing various functions included therein may also be regarded as structures within the hardware component. Or even means for achieving the various functions may be regarded as either software modules implementing the methods or structures within hardware components.
The system, apparatus, module or unit set forth in the above embodiments may be implemented in particular by a computer chip or entity, or by a product having a certain function. One typical implementation is a computer. In particular, the computer may be, for example, a personal computer, a laptop computer, a cellular telephone, a camera phone, a smart phone, a personal digital assistant, a media player, a navigation device, an email device, a game console, a tablet computer, a wearable device, or a combination of any of these devices.
For convenience of description, the above devices are described as being functionally divided into various units, respectively. Of course, the functions of each element may be implemented in one or more software and/or hardware elements when implemented in the present specification.
It will be appreciated by those skilled in the art that the present description may be provided as a method, system, or computer program product. Accordingly, the present specification embodiments may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present description embodiments may take the form of a computer program product on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) having computer-usable program code embodied therein.
The present description is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the specification. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
In one typical configuration, a computing device includes one or more processors (CPUs), input/output interfaces, network interfaces, and memory.
The memory may include volatile memory in a computer-readable medium, random Access Memory (RAM) and/or nonvolatile memory, such as Read Only Memory (ROM) or flash memory (flash RAM). Memory is an example of computer-readable media.
Computer readable media, including both non-transitory and non-transitory, removable and non-removable media, may implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of storage media for a computer include, but are not limited to, phase change memory (PRAM), static Random Access Memory (SRAM), dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), read Only Memory (ROM), electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), digital Versatile Disks (DVD) or other optical storage, magnetic cassettes, magnetic tape disk storage or other magnetic storage devices, or any other non-transmission medium, which can be used to store information that can be accessed by a computing device. Computer-readable media, as defined herein, does not include transitory computer-readable media (transmission media), such as modulated data signals and carrier waves.
It should also be noted that the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article or apparatus that comprises an element.
The description may be described in the general context of computer-executable instructions, such as program modules, being executed by a computer. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform particular tasks or implement particular abstract data types. The specification may also be practiced in distributed computing environments where tasks are performed by remote processing devices that are linked through a communications network. In a distributed computing environment, program modules may be located in both local and remote computer storage media including memory storage devices.
In this specification, each embodiment is described in a progressive manner, and identical and similar parts of each embodiment are all referred to each other, and each embodiment mainly describes differences from other embodiments. In particular, for system embodiments, since they are substantially similar to method embodiments, the description is relatively simple, as relevant to see a section of the description of method embodiments.
The foregoing embodiments have been provided for the purpose of illustrating the general principles of the present invention, and are not to be construed as limiting the scope of the invention. It should be noted that any modifications, equivalent substitutions, improvements, etc. made by those skilled in the art without departing from the spirit and principles of the present invention are intended to be included in the scope of the present invention.

Claims (10)

1. The monitoring method of the energy storage power station is characterized by comprising the following steps of:
receiving power station data sent by a plurality of energy storage power stations, and carrying out risk association screening on each power station data according to historical information screening rules and type screening rules to obtain a plurality of high risk data sets and a plurality of high association data sets; the power station data comprise power station equipment parameters, equipment working parameters and power station internal sensing data;
Carrying out data risk prediction on each high-risk data set to obtain data risk probability of an energy storage power station corresponding to each high-risk data set, and carrying out association risk prediction on each high-association data set to obtain association risk probability corresponding to each power station association set; wherein the power station association set comprises a plurality of interrelated energy storage power stations;
performing risk comprehensive analysis on the data risk probability of the energy storage power stations corresponding to the high risk data sets and the associated risk probability corresponding to the power station associated sets to obtain a plurality of high-risk energy storage power stations, generating alarm information according to the high-risk energy storage power stations, and performing alarm control according to the alarm information; according to the power station association sets and the high-risk energy storage power stations, performing risk visualization on the energy storage power stations, generating risk visualization pictures, and pushing the risk visualization pictures to a terminal for display.
2. The method for monitoring energy storage power stations according to claim 1, wherein the risk association screening is performed on each power station data according to historical information screening rules and type screening rules to obtain a plurality of high risk data sets and a plurality of high association data sets, specifically:
Screening each high-risk data set from each power station data based on the data value of each power station data in a historical time period and power station dangerous event information and according to the historical information screening rule;
based on the association parameters among the energy storage power stations corresponding to the power station data, and according to the type screening rule, screening each high-association data set from the power station data; each high-association data set comprises at least two power station data belonging to a strong association relationship.
3. The method for monitoring an energy storage power station according to claim 2, wherein the screening of each high risk data set from each power station data based on the data value of each power station data in a historical time period and power station dangerous event information according to the historical information screening rule is specifically as follows:
inquiring historical data values corresponding to current power station data at a plurality of historical time points in a database;
inquiring whether the power station dangerous event information occurs in the energy storage power station corresponding to the current power station data at the plurality of historical time points or not in the database, and determining a dangerous historical time point when the power station dangerous event occurs in the energy storage power station corresponding to the current power station data; wherein there are at least two of the hazard history time points;
According to the historical data value of the current power station data and the dangerous historical time point, calculating the similarity of different types among the historical data value corresponding to the dangerous historical time point by using weighted summation values of any two of the numerical similarity, the section data average value similarity and the adjacent time data change degree similarity, so as to obtain the data dangerous similarity of different types corresponding to the current power station data; the similarity of the adjacent time data change degree is the similarity of the data change value between the dangerous historical time point and the historical data value of the adjacent historical time point; the section data average value similarity is the similarity between the average values of the historical data values of all the historical time points in a time interval formed by the preset time length before and after the dangerous historical time points; the weight of the similarity of the adjacent time data change degree is larger than that of the section data average value similarity, and the weight of the section data average value similarity is larger than that of the numerical value similarity;
sequencing the power station data from large to small according to the dangerous data similarity of different types of the power station data to obtain at least one data sequence;
And determining the first quantity of power station data of each data sequence as each high risk data set.
4. The energy storage power station monitoring method as set forth in claim 2, wherein the screening of each high-association data set from each power station data based on association parameters between energy storage power stations corresponding to each power station data according to the type screening rule is specifically as follows:
randomly selecting a plurality of power station data from the power station data as current power station data;
acquiring the power station position, the power supply chain and the power station equipment parameters of the energy storage power station corresponding to the current power station data;
calculating a distance average value between the power station positions of the current power station data according to the power station positions of the energy storage power station corresponding to the current power station data;
calculating the similarity of power supply chains among the power supply chains of the power stations of the current power station data according to the power supply chains of the power stations of the energy storage power stations corresponding to the current power station data; the power supply chain similarity is the ratio of the number of the power station data in the same power supply chain among the current power station data to the total number;
Calculating the equipment type similarity between the power station equipment parameters of any two power station data according to the power station equipment parameters of the energy storage power station corresponding to the current power station data, and calculating the similarity average value of all the equipment type similarities corresponding to the current power station data;
carrying out weighted summation on the distance average value, the power supply chain similarity and the similarity average value corresponding to the current power station data to obtain a weighted summation value of the current power station data; the sum of the distance average value, the power supply chain similarity and the similarity average value is 1, and the weights of the distance average value, the power supply chain similarity and the similarity average value are sequentially reduced;
judging whether the weighted sum value of the current power station data is larger than a preset parameter threshold value, if so, taking the current power station data as one high-association data set;
if not, randomly selecting a plurality of power station data from the power station data, re-determining the current power station data, calculating the weighted sum value of the current power station data according to the correlation similarity of the current power station data until the weighted sum value of the current power station data is greater than the preset parameter threshold value, and obtaining each high correlation data set.
5. The method for monitoring an energy storage power station according to claim 1, wherein the step of predicting the data risk of each high risk data set to obtain the data risk probability of the energy storage power station corresponding to each high risk data set specifically comprises:
inputting each power station data in the current high-risk data set into a pre-trained first neural network prediction model to obtain a data risk probability corresponding to each power station data of the current high-risk data set; the first neural network prediction model is obtained through training of a plurality of training power station data and training data sets of data risk labels corresponding to the training power station data;
and obtaining the data risk probability of the energy storage power station corresponding to each high risk data set according to the data risk probability corresponding to each power station data of each high risk data set.
6. The method for monitoring an energy storage power station according to claim 1, wherein the performing associated risk prediction on each high-association data set to obtain associated risk probabilities corresponding to each power station associated set specifically comprises:
all the energy storage power stations corresponding to all the power station data in each high-association data set are determined to be each power station association set;
Inputting all power station data in the current high-association data set into a pre-trained second neural network prediction model to obtain association risk probabilities corresponding to each power station association set corresponding to the current high-association data set; the second neural network prediction model is obtained through training a training data set comprising a plurality of training power station data corresponding to a plurality of training power station association sets and data risk labels corresponding to the training power station association sets;
and obtaining the associated risk probability corresponding to each power station associated set according to the associated risk probability corresponding to each power station associated set corresponding to each high associated data set.
7. The method for monitoring energy storage power stations according to claim 1, wherein the risk comprehensive analysis is performed on the data risk probability of the energy storage power station corresponding to each high risk data set and the associated risk probability corresponding to each power station associated set, so as to obtain a plurality of high risk energy storage power stations, which specifically are:
judging whether the associated risk probability corresponding to the current power station associated set is higher than a preset probability threshold, if so, determining the current power station associated set as a dangerous power station set;
Determining each dangerous power station set according to the associated risk probability corresponding to each power station associated set;
according to the data risk probabilities of the energy storage power stations corresponding to the high-risk data sets, determining the data risk probabilities corresponding to all the energy storage power stations in the dangerous power station sets, and carrying out risk weight analysis on the data risk probabilities corresponding to all the energy storage power stations in the dangerous power station sets to obtain the high-risk energy storage power stations.
8. The method for monitoring energy storage power stations according to claim 1, wherein the risk weight analysis is performed on the data risk probabilities corresponding to all the energy storage power stations in the dangerous power station set to obtain each high-risk energy storage power station, specifically:
calculating the average value of the data risk probabilities corresponding to all power station data corresponding to the current energy storage power station according to the data risk probabilities of the current energy storage power station in the current dangerous power station set, and obtaining the data risk parameters corresponding to the current energy storage power station;
calculating the average value of the weighted sum values of the distance average value, the power supply chain similarity and the similarity average value between the current energy storage power station and all other energy storage power stations in the same dangerous power station set, and obtaining the corresponding association parameter of the current energy storage power station;
Determining a risk weight corresponding to the current energy storage power station according to the associated parameters corresponding to the current energy storage power station and a preset parameter weight rule; wherein the risk weight is proportional to the associated parameter;
calculating the product of the data risk parameter and the risk weight of the current energy storage power station to obtain a risk value of the current energy storage power station;
calculating the risk values of the energy storage power stations of the risk power station sets to obtain all risk values;
sequencing all energy storage power stations of each dangerous power station set from large to small according to all the dangerous degree values to obtain a power station sequence corresponding to each dangerous power station set;
and determining the first second number of energy storage power stations of the power station sequence corresponding to each dangerous power station set as the high-risk energy storage power station to obtain each high-risk energy storage power station.
9. The energy storage power station monitoring method as set forth in claim 1, wherein the risk visualization is performed on each energy storage power station according to each power station association set and each high-risk energy storage power station, so as to generate a risk visualization picture, specifically:
determining other energy storage power stations in a power station association set to which the current energy storage power station belongs to obtain a plurality of association power stations;
Judging whether the power station association set to which the current energy storage power station belongs to a dangerous power station set or not, and obtaining a first judgment result corresponding to the current energy storage power station and each association power station;
judging whether the current energy storage power station belongs to the high-risk energy storage power station or not, and obtaining a second judgment result corresponding to the current energy storage power station;
according to each energy storage power station, a first judgment result and a second judgment result corresponding to each energy storage power station are obtained;
constructing an objective function of a dynamic programming algorithm model; wherein, the objective function specifically is: in the relation diagram obtained by calculation, the distance between each energy storage power station and the corresponding associated power station is minimum, and the conspicuity degree of the display parameter corresponding to each energy storage power station is highest; the conspicuity level is used for representing the attraction level of the display parameter; the display parameters comprise a display frame size, a display font size and a specific dimension value of a display color;
determining the constraint condition of the dynamic programming algorithm model comprises: in the calculated relation diagram, the distance between the energy storage power station and the associated power station is smaller than the distance between the energy storage power station and the associated power station; the conspicuity degree of the energy storage power station with the first judging result is larger than that of the energy storage power station with the first judging result; the conspicuity degree of the energy storage power station with the second judging result is larger than that of the energy storage power station with the first judging result;
Based on the objective function and the limiting condition, inputting parameters of all energy storage power stations into the dynamic programming algorithm model for iterative calculation to obtain an optimal calculation result meeting the objective function and the limiting condition; and the optimal calculation result comprises a visual relation diagram corresponding to all the energy storage power stations.
10. An energy storage power station monitoring system for implementing the energy storage power station monitoring method as claimed in any one of claims 1 to 9, comprising: the system comprises a data receiving and screening device, a data risk association prediction device and an analysis and monitoring device;
the data receiving and screening device is used for receiving power station data sent by a plurality of energy storage power stations, and carrying out risk association screening on each power station data according to historical information screening rules and type screening rules to obtain a plurality of high risk data sets and a plurality of high association data sets; the power station data comprise power station equipment parameters, equipment working parameters and power station internal sensing data;
the data risk association prediction device is used for carrying out data risk prediction on each high-risk data set to obtain data risk probability of the energy storage power station corresponding to each high-risk data set, and carrying out association risk prediction on each high-association data set to obtain association risk probability corresponding to each power station association set; wherein the power station association set comprises a plurality of interrelated energy storage power stations;
The analysis monitoring device is used for comprehensively analyzing the risks of the data risk probability of the energy storage power stations corresponding to the high risk data sets and the associated risk probability corresponding to the power station associated sets to obtain a plurality of high-risk energy storage power stations, generating alarm information according to the high-risk energy storage power stations, and performing alarm control according to the alarm information; according to the power station association sets and the high-risk energy storage power stations, performing risk visualization on the energy storage power stations, generating risk visualization pictures, and pushing the risk visualization pictures to a terminal for display.
CN202310834791.7A 2023-07-07 2023-07-07 Monitoring method and system for energy storage power station Pending CN116885851A (en)

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Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN115808634A (en) * 2022-12-28 2023-03-17 华南理工大学 Method, device, equipment and medium for estimating safety state of lithium battery of energy storage power station
CN116128169A (en) * 2023-04-19 2023-05-16 广州云硕科技发展有限公司 Multisystem linkage control method and device for intelligent transportation

Patent Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN115808634A (en) * 2022-12-28 2023-03-17 华南理工大学 Method, device, equipment and medium for estimating safety state of lithium battery of energy storage power station
CN116128169A (en) * 2023-04-19 2023-05-16 广州云硕科技发展有限公司 Multisystem linkage control method and device for intelligent transportation

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
杨夯等: "电化学储能电站主动安全研究", 电力自动化设备, 30 September 2022 (2022-09-30), pages 1 - 11 *

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