CN116341701A - Prediction method and system for low-voltage distributed photovoltaic power generation system - Google Patents
Prediction method and system for low-voltage distributed photovoltaic power generation system Download PDFInfo
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Abstract
The invention discloses a prediction method and a prediction system for a low-voltage distributed photovoltaic power generation system, and belongs to the technical field of system simulation. The invention comprises the following steps: aiming at a target low-voltage distributed photovoltaic power generation system, acquiring historical sensor data and historical operation data of the target low-voltage distributed photovoltaic power generation system, performing data cleaning on the historical sensor data and the historical operation data to obtain cleaning data, and establishing a simulation model based on the cleaning data; collecting sensor data and operation data in a current preset time period, inputting the sensor data and the operation data into a simulation model, and dynamically simulating the target low-voltage distributed photovoltaic power generation system to obtain simulation data; and obtaining the target low-voltage distributed photovoltaic power generation system based on the simulation data, wherein the predicted access capacity and the pre-generated energy are within a preset time period in the future. The invention has wide engineering application, achievement popularization space and great scientific research value.
Description
Technical Field
The invention relates to the technical field of system simulation, in particular to a prediction method and a prediction system for a low-voltage distributed photovoltaic power generation system.
Background
At present, the unordered access of high-proportion distributed photovoltaic under 380V/220V voltage level causes negative influence on the safe and economic operation level of a transformer area, and the problems of insufficient reactive power, voltage lifting, three-phase unbalance, harmonic interference and the like are primarily revealed.
Aiming at a high-permeability distributed photovoltaic platform, the project provides a distributed photovoltaic platform simulation method for grasping dynamic operation characteristics of the distributed photovoltaic platform through measurement data fusion analysis and establishing energy flow, data flow and benefit; aiming at different platform area scales and photovoltaic operation characteristics, a photovoltaic power generation ultra-short term probability prediction model and a low-voltage platform area load level refined evaluation method are constructed, a distributed photovoltaic differential access strategy is formulated, and economic operation of the platform area is supported; providing access strategies and high-frequency acquisition monitoring schemes of metering devices in different grid-connected modes of full-power-on and self-power-on, formulating a panoramic monitoring and early warning strategy of low-voltage distributed photovoltaic safe operation, and providing a flexible control strategy of a photovoltaic system to realize real-time warning and control of voltage out-of-limit, three-phase imbalance, harmonic over-limit, overvoltage on-line and super-capacity on-line; the photovoltaic transformer area electricity larceny monitoring and early warning method under the energy bidirectional flow scene is provided, the distributed photovoltaic transformer area economic benefit assessment method for reactive cost allocation is explicitly considered, the safe and economic operation level of the transformer area is improved in a multi-dimension mode, and the power-assisted transformer area is operated efficiently and transformed in a low carbon mode.
In this context, it is desirable to provide a method or system for simulation and prediction of low voltage distributed photovoltaic operation.
Disclosure of Invention
In view of the above problems, the present invention proposes a prediction method for a low-voltage distributed photovoltaic power generation system, including:
aiming at a target low-voltage distributed photovoltaic power generation system, acquiring historical sensor data and historical operation data of the target low-voltage distributed photovoltaic power generation system, performing data cleaning on the historical sensor data and the historical operation data to obtain cleaning data, and establishing a simulation model based on the cleaning data;
collecting sensor data and operation data in a current preset time period, inputting the sensor data and the operation data into a simulation model, and dynamically simulating the target low-voltage distributed photovoltaic power generation system to obtain simulation data;
and obtaining the target low-voltage distributed photovoltaic power generation system based on the simulation data, wherein the predicted access capacity and the pre-generated energy are within a preset time period in the future.
Optionally, the historical sensor data and the data type of the sensor data includes at least one of: inverter data, electric energy meter data, cloud layer data, temperature and humidity data and illuminance data.
Optionally, the historical operating data and the data type of the operating data include at least one of: equipment failure data, equipment status data, voltage data, current data, power data, frequency data, and power generation data.
Optionally, the historical sensor data and the sensor data are waveform data.
Optionally, the historical operating data and the operating data are synchronous data based on a synchronous clock.
Optionally, the simulation data is further used for autonomous learning of the simulation model, and the obtained autonomous learning result is used for updating the simulation model.
In yet another aspect, the present invention also provides a prediction system for a low-voltage distributed photovoltaic power generation system, including:
the acquisition unit is used for acquiring historical sensor data and historical operation data of the target low-voltage distributed photovoltaic power generation system aiming at the target low-voltage distributed photovoltaic power generation system, performing data cleaning on the historical sensor data and the historical operation data to obtain cleaning data, and establishing a simulation model based on the cleaning data;
the simulation unit is used for collecting sensor data and operation data in a current preset time period, inputting the sensor data and the operation data into a simulation model, and dynamically simulating the target low-voltage distributed photovoltaic power generation system to obtain simulation data;
and the prediction unit is used for obtaining the predicted access capacity and the pre-generated energy of the target low-voltage distributed photovoltaic power generation system in a future preset time period based on the simulation data.
Optionally, the historical sensor data and the data type of the sensor data includes at least one of: inverter data, electric energy meter data, cloud layer data, temperature and humidity data and illuminance data.
Optionally, the historical operating data and the data type of the operating data include at least one of: equipment failure data, equipment status data, voltage data, current data, power data, frequency data, and power generation data.
Optionally, the historical sensor data and the sensor data are waveform data.
Optionally, the historical operating data and the operating data are synchronous data based on a synchronous clock.
Optionally, the simulation data is further used for autonomous learning of the simulation model, and the obtained autonomous learning result is used for updating the simulation model.
In yet another aspect, the present invention also provides a computing device comprising: one or more processors;
a processor for executing one or more programs;
the method as described above is implemented when the one or more programs are executed by the one or more processors.
In yet another aspect, the present invention also provides a computer readable storage medium having stored thereon a computer program which, when executed, implements a method as described above.
Compared with the prior art, the invention has the beneficial effects that:
the invention provides a prediction method for a low-voltage distributed photovoltaic power generation system, which comprises the following steps: aiming at a target low-voltage distributed photovoltaic power generation system, acquiring historical sensor data and historical operation data of the target low-voltage distributed photovoltaic power generation system, performing data cleaning on the historical sensor data and the historical operation data to obtain cleaning data, and establishing a simulation model based on the cleaning data; collecting sensor data and operation data in a current preset time period, inputting the sensor data and the operation data into a simulation model, and dynamically simulating the target low-voltage distributed photovoltaic power generation system to obtain simulation data; and obtaining the target low-voltage distributed photovoltaic power generation system based on the simulation data, wherein the predicted access capacity and the pre-generated energy are within a preset time period in the future. The invention has wide engineering application, achievement popularization space and great scientific research value.
Drawings
FIG. 1 is a flow chart of the method of the present invention;
fig. 2 is a block diagram of the system of the present invention.
Detailed Description
The exemplary embodiments of the present invention will now be described with reference to the accompanying drawings, however, the present invention may be embodied in many different forms and is not limited to the examples described herein, which are provided to fully and completely disclose the present invention and fully convey the scope of the invention to those skilled in the art. The terminology used in the exemplary embodiments illustrated in the accompanying drawings is not intended to be limiting of the invention. In the drawings, like elements/components are referred to by like reference numerals.
Unless otherwise indicated, terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art. In addition, it will be understood that terms defined in commonly used dictionaries should be interpreted as having a meaning that is consistent with their meaning in the context of the relevant art and will not be interpreted in an idealized or overly formal sense.
Example 1:
the invention provides a prediction method for a low-voltage distributed photovoltaic power generation system, which is shown in fig. 1 and comprises the following steps:
step 1, aiming at a target low-voltage distributed photovoltaic power generation system, acquiring historical sensor data and historical operation data of the target low-voltage distributed photovoltaic power generation system, performing data cleaning on the historical sensor data and the historical operation data to obtain cleaning data, and establishing a simulation model based on the cleaning data;
step 2, collecting sensor data and operation data in a current preset time period, inputting the sensor data and the operation data into a simulation model, and dynamically simulating the target low-voltage distributed photovoltaic power generation system to obtain simulation data;
and step 3, obtaining the target low-voltage distributed photovoltaic power generation system based on the simulation data, wherein the predicted access capacity and the predicted power generation amount are within a preset time period in the future.
Wherein the historical sensor data and data types of the sensor data include at least one of: inverter data, electric energy meter data, cloud layer data, temperature and humidity data and illuminance data.
Wherein the historical operating data and the data type of the operating data comprise at least one of the following: equipment failure data, equipment status data, voltage data, current data, power data, frequency data, and power generation data.
Wherein, the history sensor data and the sensor data are waveform data.
The historical operation data and the operation data are synchronous data based on a synchronous clock.
The simulation data are also used for autonomous learning of the simulation model, and the obtained autonomous learning result is used for updating the simulation model.
The principle of the invention comprises:
the method comprises the steps of collecting grid-connected inverter data (equipment running state, voltage, current, power, frequency, generating capacity and the like), environment sensing device data (illumination temperature and humidity), cloud sensing device data (cloud layer parameters), direct-current electric meters and distribution box data (alternating-current electric meters running state, voltage, current, power, frequency, electricity consumption, generating capacity and the like), cleaning the data, generating detailed synchronous event data in cooperation with a clock and a node position, uploading the synchronous event data to an intelligent terminal through a multi-protocol module, transmitting the data to a cloud server through the intelligent terminal, establishing and updating a background database for real-time analysis and simulation of edges of photovoltaic access, and realizing photovoltaic access capacity assessment and pre-generating capacity prediction.
The invention is also used for edge real-time analysis: the edge data of the distribution points are captured and analyzed in real time, so that the equipment can respond to the event in real time, the data processing pressure of the cloud platform is relieved, and the function is provided with: timeliness, rapidly changing data sets, and scalability.
Simulation: the distributed photovoltaic power generation has influence on planning, grid connection mode, power quality relay protection and the like of the power grid, and a reasonable distributed photovoltaic power generation management mode needs to be formulated to ensure safe operation of the power grid. The built model is used to reproduce the intrinsic process occurring in the actual system and the system problems existing or in the design are researched through experiments on the system model.
Distributed photovoltaic power generation power prediction API:
the power prediction data is calculated based on a physical model scheme, the power prediction data of the power station at any position is directly obtained through a standardized API interface, and the power generation capacity prediction capability of the distributed photovoltaic can be rapidly obtained by matching with meteorological observation and operation and maintenance management scenes with a plurality of power stations. The forecast aging can reach 15 days and hour by hour, and can support the access of any longitude and latitude in China areas, thereby meeting the requirement of basic operation and maintenance management of distributed photovoltaic operators.
And (3) graph monitoring: and displaying the installed capacity, the generating capacity statistical data and the environmental parameter information of the photovoltaic power station. The information such as the current, the voltage, the power and the like of the inlet and outlet lines of the transformer are monitored and displayed in real time, and a data basis is provided for the management of the transformer.
Combiner box, dc-to-ac converter: the operation data of the combiner box and the inverter are collected in real time and displayed in a graphic mode, so that management staff can conveniently and intuitively know the operation condition of the system in time.
SVG, direct current screen, environmental monitoring, insulation detection: monitoring information such as the picture, the ambient temperature, the illuminance and the humidity monitored by the ammeter, the charging voltage and current of the photovoltaic array, the voltage and the temperature of the storage battery and the like in real time; and carrying out abnormal display and alarm prompt on the fault point.
And (3) data query: the method can draw and display an inverter voltage time curve, a power time curve and the like, a direct-current side input current real-time curve and an alternating-current side inversion output current curve, and collect and display electric parameters such as daily power generation quantity and the like.
Running analysis: analyzing the operation data of the photovoltaic power station equipment according to the time of day, week, month, season and year; the generated energy can be displayed in a moon stick diagram and an annual stick diagram, and is converted into carbon dioxide and sulfur dioxide emission reduction values.
Report query and event alarm management: the method comprises the steps of providing rich reports, providing daily operation reports and energy reports, customizing report templates and energy consumption threshold-exceeding values, and informing a user through various alarm modes such as short messages, mobile phone APP pushing, voice alarm and the like when energy consumption data reach or exceed the threshold-exceeding values.
The quality of electric energy: the method monitors and alarms the power quality parameters such as higher harmonic waves, voltage fluctuation, three-phase unbalance and the like in real time, provides data for analyzing the power quality problems generated by grid connection of the photovoltaic power station, guides management staff to adjust equipment, and reduces the influence caused by the power quality problems during grid connection.
Web, APP remote monitoring: the system supports web data browsing and mobile phone APP push service, and can realize remote monitoring of the photovoltaic power station.
Access capacity assessment: identifying the voltage grade of a photovoltaic access position, acquiring the back side system parameter of the access position, and obtaining the impedance of a downstream line to obtain the configuration information of the current protection parameter; and obtaining the photovoltaic accessible maximum capacity under the current relay protection configuration according to the voltage grade, the back side system parameters, the impedance and the current protection parameter configuration information.
Predicting the pre-generated energy: and predicting the future short-term power generation amount by using numerical weather prediction, longitude and latitude, solar rate, inclination angle, equipment conversion rate and high-resolution historical measurement data.
Photovoltaic energy efficiency metering: sampling points are respectively arranged on the direct current side, the convergence side, the alternating current side and the user demarcation point, real-time data are collected for summarizing and analyzing, and the photovoltaic power generation efficiency is detected more comprehensively and accurately.
Direct current side: on the basis of analyzing conventional alternating current electric energy metering, the accurate metering of the photovoltaic direct current electric energy is improved aiming at harmonic interference of a direct current bus in a photovoltaic direct current power generation system and the analysis of the aperiodic characteristics of a direct current side current voltage waveform.
Ac side: the ac outputs of the single or multiple inverters are individually metered to obtain highly accurate and widely derived raw multi-parameter power data.
Converging side: the convergence side metering mainly aims at metering grid-connected electric energy parameters, including states of all branch devices, and analyzing conversion efficiency of different component devices in cooperation with metering of a direct current side and an alternating current side, so that accuracy of photovoltaic efficiency metering is further improved.
The invention comprises the following specific working steps:
step 1: the photovoltaic module is connected into a grid-connected inverter through a direct-current ammeter, and direct current and electrical performance parameters are fed into the inverter together;
step 2: then the grid-connected inverter is connected to a distribution box, and the electric energy converted by the photovoltaic is sent to the distribution box;
step 3: the distribution box is connected with a user load, a power grid and a data acquisition box;
step 4: the intelligent terminal collects grid-connected inverter data (equipment running state, voltage, current, power, frequency, power generation capacity and the like), environment sensing device data (illumination temperature and humidity), cloud sensing device data (cloud layer parameters), direct-current electric meters and distribution box data (alternating-current electric meter running state, voltage, current, power, frequency, power consumption, power generation capacity and the like) through the multi-protocol module and the data collection box;
step 5: cleaning the data to form a waveform database, and generating detailed synchronous event data and data of the overall operation condition of the low-voltage distributed photovoltaic power generation system by matching with a clock and node positions;
step 6: uploading the data to the intelligent terminal through the multi-protocol module;
step 7: the intelligent terminal sends the data to the cloud server, a background database is built and updated, and a simulation model and a physical model are generated;
step 8: the established model is utilized to reproduce the intrinsic process occurring in the actual system, the system problems existing in the system model or in the design are researched through experiments on the system model, a new model database is issued, and the machine autonomous learning of the edge equipment is completed;
step 9: the equipment can respond to the event in real time by capturing and analyzing the edge data of the distribution points in real time, so that the data processing pressure of the cloud platform is reduced;
step 10: calculating power prediction data based on the generated physical model scheme, directly acquiring the power prediction data of the power station at any position through a standardized API interface, and rapidly acquiring the power generation capacity prediction capacity of the distributed photovoltaic by matching with meteorological observation and numerous operation and maintenance management scenes of the power station;
step 11: the method comprises the steps of capturing and analyzing edge data of distribution points in real time, displaying and analyzing and recording installed capacity, generating capacity statistical data and environmental parameter information of a photovoltaic power station;
step 12: the information of the current, the voltage, the power and the like of the inlet and outlet lines of the transformer is monitored and displayed in real time, and a data basis is provided for the management of the transformer;
step 13: the operation data of the combiner box and the inverter are collected in real time and displayed in a graphical mode, so that management staff can conveniently and intuitively know the operation condition of the system in time;
step 14: monitoring information such as the picture, the ambient temperature, the illuminance and the humidity monitored by the ammeter, the charging voltage and current of the photovoltaic array, the voltage and the temperature of the storage battery and the like in real time; abnormal display and alarm prompt are carried out on the fault points;
step 15: drawing and displaying an inverter voltage time curve, a power time curve and the like, inputting a current real-time curve at a direct current side, inverting and outputting a current curve at an alternating current side, and collecting and displaying electric parameters such as daily power generation quantity and the like;
step 16: analyzing the operation data of the photovoltaic power station equipment according to the time of day, week, month, season and year; the generated energy can be displayed in a moon stick diagram and an annual stick diagram, and is converted into carbon dioxide and sulfur dioxide emission reduction values;
step 17: providing rich reports, providing daily operation reports and energy reports, customizing report templates and energy consumption threshold-exceeding values, and informing a user through various alarm modes such as short messages, mobile phone APP pushing, voice alarm and the like when energy consumption data reach or exceed the threshold-exceeding values;
step 18: the method has the advantages that the real-time monitoring and alarming of the power quality parameters such as higher harmonic waves, voltage fluctuation, three-phase unbalance and the like are carried out, data are provided for analyzing the power quality problems generated by grid connection of the photovoltaic power station, management staff are guided to adjust equipment, and the influence caused by the power quality problems during grid connection is reduced;
step 19: identifying the voltage grade of a photovoltaic access position, acquiring the back side system parameter of the access position, and obtaining the impedance of a downstream line to obtain the configuration information of the current protection parameter; obtaining the photovoltaic accessible maximum capacity under the current relay protection configuration according to the voltage class, the back side system parameters, the impedance and the current protection parameter configuration information;
step 20: and predicting the future short-term power generation amount by using numerical weather prediction, longitude and latitude, solar rate, inclination angle, equipment conversion rate and high-resolution historical measurement data.
The invention collects the data of inverter, electric energy meter, cloud layer, temperature and humidity and illuminance by the up communication and down communication, completes the data cleaning, forms a waveform database, synchronously records and saves the data of the whole operation condition of the low-voltage distributed photovoltaic power generation system, and comprises the following steps: equipment failure, equipment status, voltage, current, power, frequency, power generation, etc.
And establishing a simulation model, and carrying out dynamic characteristic analysis and energy flow, data flow and benefit simulation on the distributed photovoltaic platform region. Information such as the earth's edge, sunlight and the like (can be accessed into the existing mature database of a third party) is collected, a simulation model is established, and analysis is carried out on the trend direction, fault calculation (steady state), state estimation, environmental factors, line loss and voltage quality, so that the photovoltaic access capacity assessment and the pre-generated energy prediction of the low-voltage transformer area are realized.
The built model is used to reproduce the intrinsic process occurring in the actual system and the system problems existing or in the design are researched through experiments on the system model. In conclusion, the distributed photovoltaic platform area simulation and prediction method and system provided by the project have wide engineering application, achievement popularization space and great scientific research value.
Example 2:
the invention also proposes a prediction system 200 for a low-voltage distributed photovoltaic power generation system, as shown in fig. 2, comprising:
the collecting unit 201 is configured to obtain, for a target low-voltage distributed photovoltaic power generation system, historical sensor data and historical operation data of the target low-voltage distributed photovoltaic power generation system, perform data cleaning on the historical sensor data and the historical operation data to obtain cleaning data, and establish a simulation model based on the cleaning data;
the simulation unit 202 is configured to collect sensor data and operation data in a current preset time period, input the sensor data and the operation data to a simulation model, and dynamically simulate the target low-voltage distributed photovoltaic power generation system to obtain simulation data;
and the prediction unit 203 is configured to obtain, based on the simulation data, a predicted access capacity and a predicted power generation amount of the target low-voltage distributed photovoltaic power generation system in a preset time period in the future.
Wherein the historical sensor data and data types of the sensor data include at least one of: inverter data, electric energy meter data, cloud layer data, temperature and humidity data and illuminance data.
Wherein the historical operating data and the data type of the operating data comprise at least one of the following: equipment failure data, equipment status data, voltage data, current data, power data, frequency data, and power generation data.
Wherein, the history sensor data and the sensor data are waveform data.
The historical operation data and the operation data are synchronous data based on a synchronous clock.
The simulation data are also used for autonomous learning of the simulation model, and the obtained autonomous learning result is used for updating the simulation model.
Example 3:
based on the same inventive concept, the invention also provides a computer device comprising a processor and a memory for storing a computer program comprising program instructions, the processor for executing the program instructions stored by the computer storage medium. The processor may be a central processing unit (Central Processing Unit, CPU), but may also be other general purpose processors, digital signal processors (Digital Signal Processor, DSP), application specific integrated circuits (Application SpecificIntegrated Circuit, ASIC), off-the-shelf Programmable gate arrays (FPGAs) or other Programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc., which are the computational core and control core of the terminal adapted to implement one or more instructions, in particular adapted to load and execute one or more instructions within a computer storage medium to implement the corresponding method flow or corresponding functions to implement the steps of the method in the embodiments described above.
Example 4:
based on the same inventive concept, the present invention also provides a storage medium, in particular, a computer readable storage medium (Memory), which is a Memory device in a computer device, for storing programs and data. It is understood that the computer readable storage medium herein may include both built-in storage media in a computer device and extended storage media supported by the computer device. The computer-readable storage medium provides a storage space storing an operating system of the terminal. Also stored in the memory space are one or more instructions, which may be one or more computer programs (including program code), adapted to be loaded and executed by the processor. The computer readable storage medium herein may be a high-speed RAM memory or a non-volatile memory (non-volatile memory), such as at least one magnetic disk memory. One or more instructions stored in a computer-readable storage medium may be loaded and executed by a processor to implement the steps of the methods in the above-described embodiments.
It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein. The scheme in the embodiment of the invention can be realized by adopting various computer languages, such as object-oriented programming language Java, an transliteration script language JavaScript and the like.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the invention. 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.
While preferred embodiments of the present invention have been described, additional variations and modifications in those embodiments may occur to those skilled in the art once they learn of the basic inventive concepts. It is therefore intended that the following claims be interpreted as including the preferred embodiments and all such alterations and modifications as fall within the scope of the invention.
It will be apparent to those skilled in the art that various modifications and variations can be made to the present invention without departing from the spirit or scope of the invention. Thus, it is intended that the present invention also include such modifications and alterations insofar as they come within the scope of the appended claims or the equivalents thereof.
Claims (14)
1. A predictive method for a low voltage distributed photovoltaic power generation system, the method comprising:
aiming at a target low-voltage distributed photovoltaic power generation system, acquiring historical sensor data and historical operation data of the target low-voltage distributed photovoltaic power generation system, performing data cleaning on the historical sensor data and the historical operation data to obtain cleaning data, and establishing a simulation model based on the cleaning data;
collecting sensor data and operation data in a current preset time period, inputting the sensor data and the operation data into a simulation model, and dynamically simulating the target low-voltage distributed photovoltaic power generation system to obtain simulation data;
and obtaining the target low-voltage distributed photovoltaic power generation system based on the simulation data, wherein the predicted access capacity and the pre-generated energy are within a preset time period in the future.
2. The prediction method according to claim 1, wherein the historical sensor data and data types of sensor data include at least one of: inverter data, electric energy meter data, cloud layer data, temperature and humidity data and illuminance data.
3. The prediction method according to claim 1, wherein the historical operating data and the data type of the operating data include at least one of: equipment failure data, equipment status data, voltage data, current data, power data, frequency data, and power generation data.
4. The method of claim 1, wherein the historical sensor data and sensor data are waveform data.
5. The prediction method according to claim 1, wherein the historical operation data and the operation data are synchronization data based on a synchronization clock.
6. The prediction method according to claim 1, wherein the simulation data is further used for autonomous learning of a simulation model, and the obtained autonomous learning result is used for updating the simulation model.
7. A prediction system for a low voltage distributed photovoltaic power generation system, the system comprising:
the acquisition unit is used for acquiring historical sensor data and historical operation data of the target low-voltage distributed photovoltaic power generation system aiming at the target low-voltage distributed photovoltaic power generation system, performing data cleaning on the historical sensor data and the historical operation data to obtain cleaning data, and establishing a simulation model based on the cleaning data;
the simulation unit is used for collecting sensor data and operation data in a current preset time period, inputting the sensor data and the operation data into a simulation model, and dynamically simulating the target low-voltage distributed photovoltaic power generation system to obtain simulation data;
and the prediction unit is used for obtaining the predicted access capacity and the pre-generated energy of the target low-voltage distributed photovoltaic power generation system in a future preset time period based on the simulation data.
8. The predictive system of claim 7, wherein the historical sensor data and data types of sensor data include at least one of: inverter data, electric energy meter data, cloud layer data, temperature and humidity data and illuminance data.
9. The predictive system of claim 7, wherein the historical operational data and data type of operational data comprises at least one of: equipment failure data, equipment status data, voltage data, current data, power data, frequency data, and power generation data.
10. The predictive system of claim 7, wherein the historical sensor data and sensor data are waveform data.
11. The predictive system of claim 7, wherein the historical operating data and operating data are synchronous data based on a synchronous clock.
12. The prediction system of claim 7, wherein the simulation data is further used for autonomous learning of a simulation model, and the obtained autonomous learning result is used for updating the simulation model.
13. A computer device, comprising:
one or more processors;
a processor for executing one or more programs;
the method of any of claims 1-6 is implemented when the one or more programs are executed by the one or more processors.
14. A computer readable storage medium, characterized in that a computer program is stored thereon, which computer program, when executed, implements the method according to any of claims 1-6.
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| CN117458604A (en) * | 2023-10-10 | 2024-01-26 | 南京大全自动化科技有限公司 | System frame suitable for photovoltaic grid-connected collaborative autonomy in whole county |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN117458604A (en) * | 2023-10-10 | 2024-01-26 | 南京大全自动化科技有限公司 | System frame suitable for photovoltaic grid-connected collaborative autonomy in whole county |
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