CN117335570B - Visual monitoring system and method for panoramic information of elastic power distribution network - Google Patents
Visual monitoring system and method for panoramic information of elastic power distribution network Download PDFInfo
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J13/00—Circuit 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
- H02J13/00002—Circuit 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
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J13/00—Circuit 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
- H02J13/00001—Circuit 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]
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Abstract
The invention discloses a panoramic information visual monitoring system and method for an elastic power distribution network, and belongs to the technical field of power distribution network monitoring. A panoramic information visual monitoring system of an elastic power distribution network comprises an information acquisition module, an analysis and evaluation module, a real-time monitoring module and a visual display module. The method solves the problem that in the actual use process of the existing patent, the elasticity of the power distribution network cannot be analyzed and processed according to the extreme event, so that the accuracy of an analysis result is low when the elasticity index of the power distribution network is analyzed. According to the invention, the load response conditions of the disaster-suffering and recovery process of the power distribution network can be completely simulated according to the disaster types, the corresponding risk bearing capacity of the power distribution network is judged, the accuracy of analyzing the elasticity indexes of the power distribution network is improved, the accuracy of analyzing the risk grade bearing capacity is further ensured, the rush-repair efficiency of the power distribution network is improved, and the normal use of the power distribution network is prevented from being influenced by faults.
Description
Technical Field
The invention relates to the technical field of power distribution network monitoring, in particular to a panoramic information visual monitoring system and method for an elastic power distribution network.
Background
The safety and reliability of the power system are the necessary requirements for maintaining normal operation in the modern society. However, in recent years, extreme events have become more frequent, with a significant loss in power supply. Therefore, the capability of the system to cope with the extreme events needs to be actively built to improve the elastic distribution network, and in recent years, related researches on the elastic distribution network have been carried out, so that the system has become global consensus on the extreme events by improving the quick recovery capability of the system under the extreme events, and countries and regions such as the united states, the european union and japan, etc., and the elastic distribution network is built as a main measure to cope with the electric safety threat. Research and practice show that the rapid development of the intelligent power grid ensures that the power system has higher flexibility, higher safety, higher electric energy quality and higher self-healing capacity, and particularly the technologies of distributed power sources, micro-grids, active power distribution networks and the like endow the power distribution networks with more flexible and effective fault coping strategies, so that the active promotion of the restoring force of the power system is possible.
The Chinese patent with the publication number of CN115169874A discloses a comprehensive monitoring platform for a high-elasticity power grid, wherein the power grid index after environmental influence and the power grid index after external influence are monitored through an external monitoring module and an internal environment monitoring module, and then the comprehensive monitoring platform can be combined with an evaluation substitution value calculated as a comprehensive elasticity value to reduce evaluation errors, meanwhile, the index is singly discharged from a monitoring point in a whole row through a short-term ultrahigh electricity demand index and is removed from the evaluation substitution value measured by the internal environment monitoring module through an index removing unit to keep accuracy, and after the evaluation substitution value is calculated through a data distribution calculating module through fuzzy measure, the comprehensive elasticity value is obtained, further, the required elasticity performance improvement index parameter under the combined influence of internal environment factors and external environment factors is judged, and the comprehensive excavation of the power grid elasticity adjustment potential and the performance improvement of the power grid bearing capacity are facilitated.
In the practical use process, the high-elasticity power grid comprehensive monitoring platform cannot analyze and process the elasticity of the power distribution network according to the extreme event, so that the accuracy of an analysis result is low when the elasticity index of the power distribution network is analyzed; therefore, the existing requirements are not met, and a panoramic information visual monitoring system and method for the elastic power distribution network are provided.
Disclosure of Invention
The invention aims to provide a panoramic information visual monitoring system and method for an elastic power distribution network, which can completely simulate the load response conditions of the power distribution network in the disaster recovery process according to disaster types, compare and analyze the real-time elastic indexes of the power distribution network by calculating the historical elastic indexes and risk bearing grades of the power distribution network, judge the corresponding risk bearing capacity of the power distribution network, display the corresponding risk bearing capacity according to the risk grades from high to low, improve the accuracy of analyzing the elastic indexes of the power distribution network, further ensure the accuracy of analyzing the bearing capacity of the risk grades, facilitate maintenance personnel to know the real-time working condition of the power distribution network in time, improve the rush-repair efficiency of the power distribution network, avoid the power distribution network from faults and influence normal use, and solve the problems in the background technology.
In order to achieve the above purpose, the present invention provides the following technical solutions: an elastic power distribution network panoramic information visual monitoring system, comprising:
The information acquisition module is used for respectively acquiring and classifying and storing the historical disaster types of the areas where the power distribution networks are located, simultaneously acquiring the power, the fault rate, the average repair time, the important equipment accident rate, the safety equipment early warning rate, the network safety and the sustainability index of the power distribution networks, and classifying and storing the acquired results.
The analysis and evaluation module is used for analyzing disaster conditions and recovery processes of the power distribution network according to the combination of the historical disaster types of the areas where the power distribution networks are located and the power distribution network data, calculating the elasticity index of the current power distribution network, presetting the elasticity index threshold of the power distribution network according to the calculated elasticity index of the power distribution network, evaluating the risk bearing capacity of the power distribution network, and matching corresponding optimization schemes according to the analysis result of the risk bearing capacity.
The real-time monitoring module is used for monitoring disaster types and distribution network data of areas where the distribution networks are located in real time, calculating elasticity indexes of the distribution networks in real time according to the monitored data, comparing the calculated real-time elasticity indexes of the distribution networks with a preset elasticity index threshold value of the distribution network, judging whether the elasticity indexes of the current distribution network have risks or not, and making corresponding early warning prompts according to the risk grades.
The visual display module is used for carrying out corresponding visual display on the real-time elastic indexes of the power distribution networks, the risk judgment result and the optimization scheme.
Preferably, the information acquisition module includes:
the risk data acquisition module is used for respectively acquiring the historical disaster types of the areas where the power distribution networks are located, wherein the disaster types comprise weather disasters, geological disasters and disasters caused by human factors.
The power distribution network data acquisition module is used for acquiring power, fault rate, average repair time, important equipment accident rate, safety equipment early warning rate, network safety and sustainability indexes of a plurality of power distribution networks, and storing acquired results respectively, wherein the sustainability indexes comprise energy utilization efficiency, environmental pollution control and green power development.
Preferably, the distribution network data acquisition module includes:
The time interval extraction module is used for extracting the acquisition time interval of each data type in the power distribution network data; each data type in the power distribution network data comprises power, fault rate, average repair time, important equipment accident rate and safety equipment early warning rate of the power distribution network;
the maximum time interval extraction module is used for extracting the maximum time interval and the minimum time interval of the acquisition time interval of each data type in the power distribution network data;
The first time interval setting module is used for comparing the maximum time interval of the acquisition time interval of each data type in the power distribution network data with a preset time threshold, and setting the data transmission time interval as a first time interval when the maximum time interval is larger than the preset time threshold; wherein the preset time threshold is 1-3 days; wherein the first time interval is obtained by the following formula:
Wherein T 01 represents a first time interval; n represents the number of each data type in the distribution network data; t p represents an average data acquisition time interval of the power distribution network data; t min and T max respectively represent a maximum time interval and a minimum time interval of the acquisition time interval of each data type in the power distribution network data; t max0 represents a preset time threshold; e represents a constant; t i represents a data acquisition time interval corresponding to the ith data type;
The first exclusive thread establishing module is used for establishing a first communication exclusive thread corresponding to the power distribution network data acquisition, and transmitting the power distribution network data according to the first time interval by utilizing the first communication exclusive thread, wherein the maximum throughput of the first communication exclusive thread in unit time is obtained through the following formula:
Wherein C 01 represents the maximum throughput per unit time of the first communication-specific thread; c 0 denotes a preset initial throughput; t 01 denotes a first time interval; n represents the number of each data type in the distribution network data; t min and T max respectively represent a maximum time interval and a minimum time interval of the acquisition time interval of each data type in the power distribution network data; e represents a constant; c represents the average data volume corresponding to one data acquisition of the power distribution network data, wherein the data acquisition comprises all data types.
Preferably, the power distribution network data acquisition module further includes:
The second time interval setting module is used for comparing the maximum time interval of the acquisition time interval of each data type in the power distribution network data with a preset time threshold, and setting the data transmission time interval as a second time interval when the maximum time interval is smaller than or equal to the preset time threshold; wherein the preset time threshold is 1-3 days; wherein the second time interval is obtained by the following formula:
Wherein T 02 represents a second time interval; n represents the number of each data type in the distribution network data; t p represents an average data acquisition time interval of the power distribution network data; t min and T max respectively represent a maximum time interval and a minimum time interval of the acquisition time interval of each data type in the power distribution network data; t max0 represents a preset time threshold; e represents a constant; t i represents a data acquisition time interval corresponding to the ith data type;
The second exclusive thread establishing module is configured to establish a second communication exclusive thread corresponding to the power distribution network data acquisition, and perform transmission of the power distribution network data according to the second time interval by using the second communication exclusive thread, where a maximum throughput per unit time of the second communication exclusive thread is obtained by using the following formula:
Wherein C 02 represents the maximum throughput per unit time of the second communication-specific thread; c 0 denotes a preset initial throughput; t 01 denotes a first time interval; n represents the number of each data type in the distribution network data; t min and T max respectively represent a maximum time interval and a minimum time interval of the acquisition time interval of each data type in the power distribution network data; e represents a constant; c represents the average data volume corresponding to one data acquisition of the power distribution network data, wherein the data acquisition comprises all data types.
Preferably, the analysis and evaluation module includes:
The analysis module is used for analyzing disaster conditions and recovery processes of the power distribution network according to historical disaster types of areas where the power distribution network is located, and combining power, failure rate, average repair time, important equipment accident rate, safety equipment early warning rate, network safety and sustainability indexes of the power distribution network.
The calculation module is used for calculating the elasticity index of the current power distribution network according to the analyzed disaster situation and recovery process of the power distribution network, and presetting a power distribution network elasticity index threshold according to the calculated power distribution network elasticity index.
The evaluation module is used for evaluating the risk bearing capacity of the power distribution network according to the calculated elastic indexes of the power distribution network and the historical ring disaster type, and the risk bearing capacity is classified according to safety, low risk, high risk and out-of-limit.
And the early warning module is used for carrying out risk early warning on the power distribution network according to the analysis result of the evaluation module and matching a corresponding optimization scheme according to the analysis result of the risk bearing capacity.
Preferably, the analysis flow of the analysis module specifically includes:
and establishing a disaster scene model, and inputting the historical disaster type and the power distribution network data into the disaster scene model to simulate the disaster.
And analyzing the power grid node condition data, disaster types and fluctuation ranges of the power distribution network according to the simulated disaster scene to obtain disaster-affected conditions and recovery processes of the power distribution network, wherein the power grid node condition data comprise connection and disconnection, and the fluctuation ranges comprise single, multiple and areas.
And drawing a curve of the average state of the power distribution network according to the disaster condition and the recovery process of the power distribution network.
And calculating an elasticity index of the power distribution network according to the average state curve of the power distribution network.
Preferably, the evaluation module includes:
the risk bearing grade evaluation module is used for evaluating the risk bearing capacity of the power distribution network according to the historical environmental data, the historical meteorological data and the historical landform data of the area where the power distribution network is located.
And the restoring force evaluation module is used for evaluating the restoring force of the power distribution network according to the elasticity of the power distribution network and the risk bearing grade of the power distribution network.
The elastic optimization module is used for collecting various index data in the recovery process of the power distribution network and correspondingly optimizing the elastic recovery force of the power distribution network according to the collected recovery data.
Preferably, the monitoring module includes:
The real-time monitoring module is used for monitoring disaster types of areas where the power distribution networks are located and power distribution network data in real time and calculating the elasticity index of the current power distribution network in real time according to the monitored data.
And the comparison module is used for comparing the calculated real-time elasticity indexes of the power distribution network with a preset elasticity index threshold value of the power distribution network.
And the judging module is used for judging whether the current power distribution network elastic index has risk according to the result compared by the comparing module, and making corresponding early warning prompt according to the risk level.
Preferably, the visual display module includes:
And the elastic index display module is used for visually displaying the real-time elastic indexes of the power distribution networks monitored by the monitoring module.
The evaluation display module is used for visually displaying disaster grades born by the power distribution networks evaluated by the evaluation module and preferentially displaying the power distribution network with high risk grade according to the height of the risk grade.
The optimization display module is used for matching corresponding optimization methods according to disaster grades born by the plurality of elastic power distribution networks and visually displaying the optimization methods and the optimization process.
A working method of an elastic power distribution network panoramic information visual monitoring system comprises the following steps:
step one: and respectively acquiring the historical disaster types of the areas where the power distribution networks are located and the data of the power distribution networks, and storing the acquired results in a classified manner.
Step two: and analyzing disaster conditions and recovery processes of the power distribution network according to the historical disaster types of the areas where the power distribution networks are located and combining the power distribution network data, and calculating the elasticity index of the current power distribution network.
Step three: and presetting an elasticity index threshold of the power distribution network according to the calculated elasticity index of the power distribution network, evaluating risk bearing capacity of a plurality of power distribution networks, and matching corresponding optimization schemes according to analysis results of the risk bearing capacity.
Step four: the disaster type and the power distribution network data of the areas where the power distribution networks are located are monitored in real time, real-time elasticity indexes of the power distribution networks are calculated, the calculated real-time elasticity indexes of the power distribution networks are compared with a preset power distribution network elasticity index threshold value, and whether the current power distribution network elasticity indexes have risks is judged.
Step five: if the power distribution network has risks, grading the risks, and visually displaying the risks from high to low according to the levels of the risks.
Compared with the prior art, the invention has the beneficial effects that:
According to the invention, the real-time elasticity index of the power distribution network is calculated by implementing monitoring on the disaster condition of the area where the power distribution network is located, the load response condition of the disaster and recovery process of the power distribution network can be completely simulated according to the disaster type, the real-time elasticity index of the power distribution network is compared and analyzed by calculating the historical elasticity index and the risk bearing grade of the power distribution network, the corresponding risk bearing capacity of the power distribution network is judged, the display is carried out from high to low according to the risk grade, the reference is provided for the elastic lifting measure of the power distribution network, the accuracy of analyzing the elasticity index of the power distribution network is improved, the accuracy of analyzing the risk grade bearing capacity is further ensured, and corresponding risk early warning is provided according to the risk bearing condition while corresponding risk early warning is provided, so that maintenance personnel can conveniently and timely know the real-time working condition of the power distribution network, the rush-repair efficiency of the power distribution network is improved, and the fault of the power distribution network is avoided, and normal use is influenced.
Drawings
FIG. 1 is a schematic diagram of a module of a visual monitoring system for panoramic information of an elastic power distribution network;
Fig. 2 is a working flow chart of the panoramic information visual monitoring system of the elastic power distribution network.
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.
In order to solve the problem that in the actual use process of the existing patent, the elasticity of the power distribution network cannot be analyzed and processed according to the extreme event, so that the accuracy of the analysis result is low when the elasticity index of the power distribution network is analyzed, please refer to fig. 1-2, the present embodiment provides the following technical scheme:
An elastic power distribution network panoramic information visual monitoring system, comprising:
The information acquisition module is used for respectively acquiring and classifying and storing the historical disaster types of the areas where the power distribution networks are located, simultaneously acquiring the power, the fault rate, the average repair time, the important equipment accident rate, the safety equipment early warning rate, the network safety and the sustainability index of the power distribution networks, classifying and storing the acquired results, and acquiring data of the power distribution networks, so that maintenance personnel can know the elasticity index of the power distribution networks at the same time, and the practicability is higher.
An information acquisition module comprising:
the risk data acquisition module is used for respectively acquiring the historical disaster types of the areas where the power distribution networks are located, wherein the disaster types comprise weather disasters, geological disasters and disasters caused by human factors.
The power distribution network data acquisition module is used for acquiring power, fault rate, average repair time, important equipment accident rate, safety equipment early warning rate, network safety and sustainability indexes of a plurality of power distribution networks, and storing acquired results respectively, wherein the sustainability indexes comprise energy utilization efficiency, environmental pollution control and green power development.
Specifically, the distribution network data acquisition module includes:
The time interval extraction module is used for extracting the acquisition time interval of each data type in the power distribution network data; each data type in the power distribution network data comprises power, fault rate, average repair time, important equipment accident rate and safety equipment early warning rate of the power distribution network;
the maximum time interval extraction module is used for extracting the maximum time interval and the minimum time interval of the acquisition time interval of each data type in the power distribution network data;
The first time interval setting module is used for comparing the maximum time interval of the acquisition time interval of each data type in the power distribution network data with a preset time threshold, and setting the data transmission time interval as a first time interval when the maximum time interval is larger than the preset time threshold; wherein the preset time threshold is 1-3 days; wherein the first time interval is obtained by the following formula:
Wherein T 01 represents a first time interval; n represents the number of each data type in the distribution network data; t p represents an average data acquisition time interval of the power distribution network data; t min and T max respectively represent a maximum time interval and a minimum time interval of the acquisition time interval of each data type in the power distribution network data; t max0 represents a preset time threshold; e represents a constant; t i represents a data acquisition time interval corresponding to the ith data type;
The first exclusive thread establishing module is used for establishing a first communication exclusive thread corresponding to the power distribution network data acquisition, and transmitting the power distribution network data according to the first time interval by utilizing the first communication exclusive thread, wherein the maximum throughput of the first communication exclusive thread in unit time is obtained through the following formula:
Wherein C 01 represents the maximum throughput per unit time of the first communication-specific thread; c 0 denotes a preset initial throughput; t 01 denotes a first time interval; n represents the number of each data type in the distribution network data; t min and T max respectively represent a maximum time interval and a minimum time interval of the acquisition time interval of each data type in the power distribution network data; e represents a constant; c represents the average data volume corresponding to one data acquisition of the power distribution network data, wherein the data acquisition comprises all data types.
The technical effects of the technical scheme are as follows: time interval extraction and management: the scheme provides a time interval extraction module which can extract the acquisition time interval according to different data types (such as power, fault rate, average repair time and the like) of the power distribution network data. This helps to optimize the time interval for data acquisition to ensure accuracy and efficiency of the data.
Automatically adjusting the data transmission time interval: the first time interval setting module in the scheme compares the maximum time interval with a preset time threshold value and automatically adjusts the data transmission time interval. This ensures that longer time intervals are used when the data changes slowly, thereby reducing communication overhead, and shorter time intervals are used when the data changes quickly, and important data is acquired in time.
Communication thread optimization: by means of the first exclusive thread establishing module, the system establishes an exclusive communication thread, and distribution network data can be transmitted according to a first time interval. The maximum throughput per unit time of this thread can be automatically adjusted according to the number of data types and the time interval to optimize the data transmission performance.
And the data transmission efficiency is improved: the scheme can improve the efficiency of data transmission by automatically optimizing the data transmission time interval and the throughput of the communication thread according to the data type and the time interval. This facilitates timely acquisition of critical power distribution network data while reducing communication costs and resource occupation.
In general, the main technical effect of the technical scheme is that the efficiency and the reliability of data acquisition and transmission of the power distribution network are improved, and meanwhile, the acquisition and transmission parameters are automatically adjusted according to actual conditions so as to meet the requirements of different data types. This may help power system operators to better monitor and manage the distribution grid, improving its operational efficiency and reliability.
Specifically, the distribution network data acquisition module further includes:
The second time interval setting module is used for comparing the maximum time interval of the acquisition time interval of each data type in the power distribution network data with a preset time threshold, and setting the data transmission time interval as a second time interval when the maximum time interval is smaller than or equal to the preset time threshold; wherein the preset time threshold is 1-3 days; wherein the second time interval is obtained by the following formula:
Wherein T 02 represents a second time interval; n represents the number of each data type in the distribution network data; t p represents an average data acquisition time interval of the power distribution network data; t min and T max respectively represent a maximum time interval and a minimum time interval of the acquisition time interval of each data type in the power distribution network data; t max0 represents a preset time threshold; e represents a constant; t i represents a data acquisition time interval corresponding to the ith data type;
The second exclusive thread establishing module is configured to establish a second communication exclusive thread corresponding to the power distribution network data acquisition, and perform transmission of the power distribution network data according to the second time interval by using the second communication exclusive thread, where a maximum throughput per unit time of the second communication exclusive thread is obtained by using the following formula:
Wherein C 02 represents the maximum throughput per unit time of the second communication-specific thread; c 0 denotes a preset initial throughput; t 01 denotes a first time interval; n represents the number of each data type in the distribution network data; t min and T max respectively represent a maximum time interval and a minimum time interval of the acquisition time interval of each data type in the power distribution network data; e represents a constant; c represents the average data volume corresponding to one data acquisition of the power distribution network data, wherein the data acquisition comprises all data types.
The technical effects of the technical scheme are as follows: additional time interval management: and the second time interval setting module is introduced, and the maximum time interval can be compared with the acquisition time interval of each data type in the power distribution network data according to a preset time threshold, so that the data transmission time interval is further refined according to different data types and data change rates.
Adaptive data transmission: after introducing the second time interval, the system can automatically adjust the time interval of data transmission according to different data types. The system will employ the second time interval when the maximum time interval of the data type is less than or equal to the preset time threshold. This helps to achieve a more flexible data acquisition and transmission strategy between different data types.
A second dedicated thread: by means of the second dedicated thread establishing module, the system can establish dedicated communication threads for the second time interval to further optimize data transmission performance. This ensures that there is an appropriate allocation of communication resources at different time intervals.
Re-optimization of data transmission performance: the maximum throughput of the second communication dedicated thread in unit time can also be automatically adjusted according to different data types and time intervals, so as to improve the data transmission performance to the greatest extent.
In general, the expanded technical scheme further improves the flexibility and the efficiency of data acquisition and transmission of the power distribution network, can more finely manage time intervals and communication resources of different data types, ensures timely acquisition of key data, and simultaneously minimizes communication cost and resource waste. This helps the power system operators to better monitor and manage the distribution network, improving their operational efficiency and reliability.
The analysis evaluation module is used for analyzing disaster-stricken conditions and recovery processes of the power distribution network according to historical disaster types of areas where the power distribution network is located by combining the power distribution network data, calculating elastic indexes of the current power distribution network, presetting power distribution network elastic index thresholds according to the calculated power distribution network elastic indexes, evaluating risk bearing capacity of the power distribution network, matching corresponding optimization schemes according to analysis results of the risk bearing capacity, completely simulating load response conditions of the disaster-stricken and recovery processes of the power distribution network according to disaster types, comparing and analyzing real-time elastic indexes of the power distribution network by calculating historical elastic indexes and risk bearing grades of the power distribution network, judging corresponding risk bearing capacity of the power distribution network, displaying from high to low according to the risk grades, further providing reference for elastic lifting measures of the power distribution network, guaranteeing accuracy of analysis of the elastic indexes, and guaranteeing accuracy of analysis of the risk grade bearing capacity.
An analytical evaluation module comprising:
The analysis module is used for analyzing disaster conditions and recovery processes of the power distribution network according to historical disaster types of areas where the power distribution network is located, and combining power, failure rate, average repair time, important equipment accident rate, safety equipment early warning rate, network safety and sustainability indexes of the power distribution network.
The calculation module is used for calculating the elasticity index of the current power distribution network according to the analyzed disaster situation and recovery process of the power distribution network, and presetting a power distribution network elasticity index threshold according to the calculated power distribution network elasticity index.
The evaluation module is used for evaluating the risk bearing capacity of the power distribution network according to the calculated elastic indexes of the power distribution network and the historical ring disaster type, and the risk bearing capacity is classified according to safety, low risk, high risk and out-of-limit.
And the early warning module is used for carrying out risk early warning on the power distribution network according to the analysis result of the evaluation module and matching a corresponding optimization scheme according to the analysis result of the risk bearing capacity.
The analysis flow of the analysis module specifically comprises:
and establishing a disaster scene model, and inputting the historical disaster type and the power distribution network data into the disaster scene model to simulate the disaster.
And analyzing the power grid node condition data, disaster types and fluctuation ranges of the power distribution network according to the simulated disaster scene to obtain disaster-affected conditions and recovery processes of the power distribution network, wherein the power grid node condition data comprise connection and disconnection, and the fluctuation ranges comprise single, multiple and areas.
And drawing a curve of the average state of the power distribution network according to the disaster condition and the recovery process of the power distribution network.
And calculating an elasticity index of the power distribution network according to the average state curve of the power distribution network.
An evaluation module, comprising:
the risk bearing grade evaluation module is used for evaluating the risk bearing capacity of the power distribution network according to the historical environmental data, the historical meteorological data and the historical landform data of the area where the power distribution network is located.
And the restoring force evaluation module is used for evaluating the restoring force of the power distribution network according to the elasticity of the power distribution network and the risk bearing grade of the power distribution network.
The elastic optimization module is used for collecting various index data in the recovery process of the power distribution network and correspondingly optimizing the elastic recovery force of the power distribution network according to the collected recovery data.
The real-time monitoring module is used for monitoring disaster types and distribution network data of areas where the distribution networks are located in real time, calculating elasticity indexes of the distribution networks in real time according to the monitored data, comparing the calculated elasticity indexes of the distribution networks in real time with a preset elasticity index threshold value of the distribution network, judging whether the elasticity indexes of the current distribution network have risks or not, and making corresponding early warning prompts according to risk grades, so that the elasticity indexes of the distribution networks can be monitored in real time, the current risk bearing capacity is judged according to monitoring results, corresponding optimization is provided according to the risk bearing conditions, corresponding risk early warning is given at the same time, maintenance personnel can conveniently know real-time working conditions of the distribution networks in time, the rush-repair efficiency of the distribution networks is improved, faults of the distribution networks are avoided, and normal use is affected.
A monitoring module, comprising:
The real-time monitoring module is used for monitoring disaster types of areas where the power distribution networks are located and power distribution network data in real time and calculating the elasticity index of the current power distribution network in real time according to the monitored data.
And the comparison module is used for comparing the calculated real-time elasticity indexes of the power distribution network with a preset elasticity index threshold value of the power distribution network.
And the judging module is used for judging whether the current power distribution network elastic index has risk according to the result compared by the comparing module, and making corresponding early warning prompt according to the risk level.
The visual display module is used for carrying out corresponding visual display on the real-time elastic indexes of the power distribution networks, the risk judgment result and the optimization scheme.
A visual display module, comprising:
And the elastic index display module is used for visually displaying the real-time elastic indexes of the power distribution networks monitored by the monitoring module.
The evaluation display module is used for visually displaying disaster grades born by the power distribution networks evaluated by the evaluation module, and the power distribution network with high risk grade is preferentially displayed according to the height of the risk grade, so that maintenance personnel can rapidly find out abnormal conditions of the power distribution network, can be directed against the preferential display with high risk, and can timely process the abnormal conditions, thereby improving the rush-repair efficiency of the power distribution network and further ensuring the normal use of the power distribution network.
The optimization display module is used for matching corresponding optimization methods according to disaster grades born by a plurality of elastic power distribution networks, visually displaying the optimization methods and the optimization process, facilitating maintenance personnel to clearly know the elastic performance and the bearable risk grades of the power distribution networks, matching corresponding optimization schemes according to the bearable risk grades, enabling the maintenance personnel to rapidly optimize the elastic performance of the power distribution networks, and guaranteeing normal work of the power distribution networks.
In order to better realize the working method of the panoramic information visual monitoring system of the elastic power distribution network, the method comprises the following steps:
Step one: the historical disaster types of the areas where the power distribution networks are located and the data of the power distribution networks are respectively collected, the collected results are classified and stored, the situations of the power distribution networks can be known at the same time, and then the historical elasticity indexes of the power distribution networks are calculated.
Step two: according to the historical disaster types of the areas where the power distribution networks are located, the power distribution network data are combined, disaster conditions and recovery processes of the power distribution networks are analyzed, the elasticity index of the current power distribution network is calculated, and the historical elasticity indexes of the power distribution networks can be known at the same time.
Step three: the power distribution network elastic index threshold value is preset according to the calculated power distribution network elastic index, risk bearing capacity of a plurality of power distribution networks is evaluated, corresponding optimization schemes are matched according to analysis results of the risk bearing capacity, the risk bearing capacity of the power distribution networks can be known, corresponding optimization schemes are provided according to the risk bearing capacity, maintenance personnel can quickly know the risk bearing capacity of the power distribution networks, corresponding maintenance strategies are made according to the optimization schemes, the rush-repair efficiency of the power distribution networks is improved, faults of the power distribution networks are avoided, and normal use is affected.
Step four: the disaster type and the power distribution network data of the areas where the power distribution networks are located are monitored in real time, real-time elasticity indexes of the power distribution networks are calculated, the calculated real-time elasticity indexes of the power distribution networks are compared with a preset power distribution network elasticity index threshold value, and whether the current power distribution network elasticity indexes have risks is judged.
Step five: if the power distribution network has risks, the risks are classified, visual display is carried out from high to low according to the levels of the risks, maintenance personnel can intuitively know the real-time working condition of the power distribution network, and corresponding adjustment is carried out according to the real-time condition of the power distribution network.
It is noted that relational terms such as first and second, and the like are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Moreover, 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.
Although embodiments of the present invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made therein without departing from the principles and spirit of the invention, the scope of which is defined in the appended claims and their equivalents.
Claims (7)
1. A panoramic information visual monitoring system of an elastic power distribution network is characterized in that; comprising the following steps:
The information acquisition module is used for respectively acquiring and classifying and storing the historical disaster types of the areas where the power distribution networks are located, simultaneously acquiring the power, the fault rate, the average repair time, the important equipment accident rate, the safety equipment early warning rate, the network safety and the sustainability index of the power distribution networks, and classifying and storing the acquired results;
An information acquisition module comprising:
The risk data acquisition module is used for respectively acquiring the historical disaster types of the areas where the power distribution networks are located, wherein the disaster types comprise meteorological disasters, geological disasters and disasters caused by human factors;
The power distribution network data acquisition module is used for acquiring power, failure rate, average repair time, important equipment accident rate, safety equipment early warning rate, network safety and sustainability indexes of a plurality of power distribution networks, and storing acquired results respectively, wherein the sustainability indexes comprise energy utilization efficiency, environmental pollution control and green power development;
the power distribution network data acquisition module comprises:
The time interval extraction module is used for extracting the acquisition time interval of each data type in the power distribution network data; each data type in the power distribution network data comprises power, fault rate, average repair time, important equipment accident rate and safety equipment early warning rate of the power distribution network;
the maximum time interval extraction module is used for extracting the maximum time interval and the minimum time interval of the acquisition time interval of each data type in the power distribution network data;
The first time interval setting module is used for comparing the maximum time interval of the acquisition time interval of each data type in the power distribution network data with a preset time threshold, and setting the data transmission time interval as a first time interval when the maximum time interval is larger than the preset time threshold; wherein the preset time threshold is 1-3 days; wherein the first time interval is obtained by the following formula:
Wherein T 01 represents a first time interval; n represents the number of each data type in the distribution network data; t p represents an average data acquisition time interval of the power distribution network data; t min and T max respectively represent a maximum time interval and a minimum time interval of the acquisition time interval of each data type in the power distribution network data; t max0 represents a preset time threshold; e represents a constant; t i represents a data acquisition time interval corresponding to the ith data type;
The first exclusive thread establishing module is used for establishing a first communication exclusive thread corresponding to the power distribution network data acquisition, and transmitting the power distribution network data according to the first time interval by utilizing the first communication exclusive thread, wherein the maximum throughput of the first communication exclusive thread in unit time is obtained through the following formula:
Wherein C 01 represents the maximum throughput per unit time of the first communication-specific thread; c 0 denotes a preset initial throughput; c represents average data quantity corresponding to primary data acquisition of all data types of the power distribution network data;
the power distribution network data acquisition module further comprises:
The second time interval setting module is used for comparing the maximum time interval of the acquisition time interval of each data type in the power distribution network data with a preset time threshold, and setting the data transmission time interval as a second time interval when the maximum time interval is smaller than or equal to the preset time threshold; wherein the preset time threshold is 1-3 days; wherein the second time interval is obtained by the following formula:
wherein T 02 represents a second time interval;
The second exclusive thread establishing module is configured to establish a second communication exclusive thread corresponding to the power distribution network data acquisition, and perform transmission of the power distribution network data according to the second time interval by using the second communication exclusive thread, where a maximum throughput per unit time of the second communication exclusive thread is obtained by using the following formula:
Wherein C 02 represents the maximum throughput per unit time of the second communication-specific thread;
The analysis and evaluation module is used for analyzing disaster conditions and recovery processes of the power distribution network according to the combination of the historical disaster types of the areas where the power distribution networks are located and the power distribution network data, calculating the elasticity index of the current power distribution network, presetting the elasticity index threshold of the power distribution network according to the calculated elasticity index of the power distribution network, evaluating the risk bearing capacity of the power distribution network, and matching corresponding optimization schemes according to the analysis result of the risk bearing capacity;
The real-time monitoring module is used for monitoring disaster types and power distribution network data of areas where the power distribution networks are located in real time, calculating elasticity indexes of the power distribution networks in real time according to the monitored data, comparing the calculated real-time elasticity indexes of the power distribution networks with a preset elasticity index threshold value of the power distribution network, judging whether the elasticity indexes of the current power distribution networks have risks or not, and making corresponding early warning prompts according to the risk grades;
The visual display module is used for carrying out corresponding visual display on the real-time elastic indexes of the power distribution networks, the risk judgment result and the optimization scheme.
2. The visual monitoring system for panoramic information of an elastic power distribution network according to claim 1, wherein: the analysis and evaluation module comprises:
The analysis module is used for analyzing disaster conditions and recovery processes of the power distribution network according to historical disaster types of areas where the power distribution network is located, and combining power, failure rate, average repair time, important equipment accident rate, safety equipment early warning rate, network safety and sustainability indexes of the power distribution network;
the calculation module is used for calculating the elasticity index of the current power distribution network according to the analyzed disaster situation and recovery process of the power distribution network, and presetting an elasticity index threshold of the power distribution network according to the calculated elasticity index of the power distribution network;
The evaluation module is used for evaluating the risk bearing capacity of the power distribution network according to the calculated elastic indexes of the power distribution network and the historical ring disaster type, and the risk bearing capacity is classified according to safety, low risk, high risk and out-of-limit;
and the early warning module is used for carrying out risk early warning on the power distribution network according to the analysis result of the evaluation module and matching a corresponding optimization scheme according to the analysis result of the risk bearing capacity.
3. The visual monitoring system for panoramic information of an elastic power distribution network according to claim 2, wherein: the analysis flow of the analysis module specifically comprises:
Establishing a disaster scene model, and inputting the historical disaster type and the power distribution network data into the disaster scene model to simulate the disaster;
Analyzing power grid node condition data, disaster types and fluctuation ranges of the power distribution network according to the simulated disaster scene to obtain disaster conditions and recovery processes of the power distribution network, wherein the power grid node condition data comprise connection and disconnection, and the fluctuation ranges comprise single, multiple and areas;
drawing a curve of the average state of the power distribution network according to the disaster condition and the recovery process of the power distribution network;
And calculating an elasticity index of the power distribution network according to the average state curve of the power distribution network.
4. The visual monitoring system for panoramic information of an elastic power distribution network according to claim 2, wherein: the evaluation module comprises:
The risk bearing grade evaluation module is used for evaluating the risk bearing capacity of the power distribution network according to the historical environmental data, the historical meteorological data and the historical landform data of the area where the power distribution network is located;
the restoring force evaluation module is used for evaluating the restoring force of the power distribution network according to the elasticity of the power distribution network and the risk bearing grade of the power distribution network;
The elastic optimization module is used for collecting various index data in the recovery process of the power distribution network and correspondingly optimizing the elastic recovery force of the power distribution network according to the collected recovery data.
5. The visual monitoring system for panoramic information of an elastic power distribution network according to claim 1, wherein: the monitoring module comprises:
the real-time monitoring module is used for monitoring disaster types of areas where the power distribution networks are located and power distribution network data in real time, and calculating the elasticity index of the current power distribution network in real time according to the monitored data;
The comparison module is used for comparing the calculated real-time elasticity indexes of the power distribution network with a preset elasticity index threshold value of the power distribution network;
And the judging module is used for judging whether the current power distribution network elastic index has risk according to the result compared by the comparing module, and making corresponding early warning prompt according to the risk level.
6. The visual monitoring system for panoramic information of an elastic power distribution network according to claim 1, wherein: the visual display module comprises:
The elastic index display module is used for visually displaying the real-time elastic indexes of the power distribution networks monitored by the monitoring module;
the evaluation display module is used for visually displaying disaster grades born by the power distribution networks evaluated by the evaluation module and preferentially displaying the power distribution network with high risk grade according to the height of the risk grade;
the optimization display module is used for matching corresponding optimization methods according to disaster grades born by the plurality of elastic power distribution networks and visually displaying the optimization methods and the optimization process.
7. The working method of the elastic power distribution network panoramic information visual monitoring system according to claim 6, wherein the working method comprises the following steps of: the method comprises the following steps:
Step one: respectively acquiring the historical disaster types of the areas where the power distribution networks are located and the data of the power distribution networks, and classifying and storing the acquired results;
Step two: analyzing disaster conditions and recovery processes of the power distribution network according to the historical disaster types of the areas where the power distribution networks are located and combining the power distribution network data, and calculating the elasticity index of the current power distribution network;
Step three: presetting an elasticity index threshold of the power distribution network according to the calculated elasticity index of the power distribution network, evaluating risk bearing capacity of a plurality of power distribution networks, and matching corresponding optimization schemes according to analysis results of the risk bearing capacity;
step four: real-time monitoring of disaster types and power distribution network data of areas where the power distribution networks are located, calculating real-time elasticity indexes of the power distribution networks, comparing the calculated real-time elasticity indexes of the power distribution networks with preset power distribution network elasticity index thresholds, and judging whether the current power distribution network elasticity indexes have risks or not;
Step five: if the power distribution network has risks, grading the risks, and visually displaying the risks from high to low according to the levels of the risks.
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Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113191687A (en) * | 2021-05-25 | 2021-07-30 | 广东电网有限责任公司广州供电局 | Elastic power distribution network panoramic information visualization method and system |
| CN116777264A (en) * | 2023-05-23 | 2023-09-19 | 国网北京市电力公司 | Novel power distribution network toughness index determination method and evaluation system considering energy storage |
Family Cites Families (15)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| FR2865333B1 (en) * | 2004-01-20 | 2006-04-28 | Atmel Nantes Sa | METHOD FOR AUTOMATICALLY DETECTING THE RATE OF A NETWORK, IN PARTICULAR CAN BUS TYPE, AND DETERMINING THE FLOW RATE DETECTED BY TRANSITION ANALYSIS, CORRESPONDING DEVICE |
| CN105842581B (en) * | 2016-03-23 | 2019-05-03 | 国网山东省电力公司章丘市供电公司 | A kind of distribution network failure method for early warning and system |
| CN106875105B (en) * | 2017-01-23 | 2020-11-03 | 东北大学 | Power distribution network differentiation planning method considering composite fault risk |
| CN109034521B (en) * | 2018-06-07 | 2021-11-16 | 国电南瑞科技股份有限公司 | Intelligent operation and maintenance architecture design method of power grid dispatching control system |
| CN109447330B (en) * | 2018-10-12 | 2021-10-26 | 东北大学 | Power distribution network risk early warning method considering power grid elasticity and adaptability |
| CN114598333A (en) * | 2020-12-03 | 2022-06-07 | 华为技术有限公司 | Channel coding and decoding method and related device |
| US20220344936A1 (en) * | 2021-04-26 | 2022-10-27 | One Concern, Inc. | Estimation of distribution network recovery after disaster |
| WO2022241056A1 (en) * | 2021-05-11 | 2022-11-17 | Electricfish Energy Inc. | System and method for electrical grid management, risk mitigation, and resilience |
| KR20230007748A (en) * | 2021-07-06 | 2023-01-13 | 한국전력공사 | Apparatus and method for analyzing microgrid to improve resilience |
| CN113674099B (en) * | 2021-08-27 | 2022-07-05 | 中国农业科学院农业环境与可持续发展研究所 | Comprehensive risk assessment method for drought and waterlogging disasters of apples |
| CN113810792B (en) * | 2021-11-19 | 2022-02-18 | 南京绛门信息科技股份有限公司 | Edge data acquisition and analysis system based on cloud computing |
| CN114358619A (en) * | 2022-01-08 | 2022-04-15 | 西安交通大学 | Double-layer assessment method and system for elastic power distribution network resilience assessment |
| CN115640936A (en) * | 2022-09-05 | 2023-01-24 | 浙江华电器材检测研究院有限公司 | Power distribution network elasticity evaluation method, device, equipment and readable storage medium |
| CN116090819A (en) * | 2022-12-27 | 2023-05-09 | 贵州电网有限责任公司 | Power distribution network risk situation prediction method based on association rule |
| CN116388161A (en) * | 2023-03-21 | 2023-07-04 | 国网冀北电力有限公司电力科学研究院 | Toughness determination method and device for power distribution network |
-
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Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113191687A (en) * | 2021-05-25 | 2021-07-30 | 广东电网有限责任公司广州供电局 | Elastic power distribution network panoramic information visualization method and system |
| CN116777264A (en) * | 2023-05-23 | 2023-09-19 | 国网北京市电力公司 | Novel power distribution network toughness index determination method and evaluation system considering energy storage |
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