CN117543564B - Power grid regulation and control method and system based on countermeasure type neural network - Google Patents
Power grid regulation and control method and system based on countermeasure type neural 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
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
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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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- 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
- H02J13/00032—Systems characterised by the controlled or operated power network elements or equipment, the power network elements or equipment not otherwise provided for
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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
- H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
- H02J2203/10—Power transmission or distribution systems management focussing at grid-level, e.g. load flow analysis, node profile computation, meshed network optimisation, active network management or spinning reserve management
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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
- H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
- H02J2203/20—Simulating, e g planning, reliability check, modelling or computer assisted design [CAD]
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
- Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
- Y04S10/00—Systems supporting electrical power generation, transmission or distribution
- Y04S10/50—Systems or methods supporting the power network operation or management, involving a certain degree of interaction with the load-side end user applications
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Abstract
The application provides a power grid regulation and control method and a system based on an antagonistic neural network, and relates to the technical field of power grid regulation and control, wherein the method comprises the following steps: firstly, local power grid basic information of a target management and control area is called, a twin topology network is built by combining a digital twin technology, operation and maintenance of a power grid are monitored and predicted, so that a power grid regulation and control scheme is determined, the regulation and control scheme is fed back to a regulation and control center to generate a regulation and control instruction, the instruction is put into equipment of the target area for targeted regulation and control, feedback information is obtained, and the network is updated. The application mainly solves the problems of low efficiency and inaccuracy caused by manual intervention and processing because the automation level is not high enough and automatic control and early warning cannot be completely realized. By adopting the countermeasure neural network, the running state of the power grid is effectively predicted and regulated, the stability and reliability of the power grid are improved, and meanwhile, the energy consumption is reduced.
Description
Technical Field
The invention relates to the technical field of power grid regulation, in particular to a power grid regulation method and system based on an antagonistic neural network.
Background
Along with the grid connection of large-scale new energy, the control foundation is changed greatly, the power electronic power supply does not have the control characteristic of a traditional generator, the control scale is increased greatly, the control objects are expanded to hundreds of thousands of devices from thousands of devices, the control objects are expanded to links of source network load storage from the main sources, local area loads are concentrated, the local installation is seriously insufficient, the voltage stability problem is prominent, the power regulation and control operation system is inevitably faced with various emergency situations in the operation process, such as weather abnormality, the mixing of new energy and traditional energy, and the like, and the situations are likely to cause the instability of the power system and need emergency treatment.
In the prior art, data processing and analysis are carried out by collecting power grid running state data (such as parameters of voltage, current and the like), corresponding power generation plans are formulated by a dispatcher according to the real-time power grid running state, the running state of a generator set is adjusted, and the economical efficiency and the reliability of a power grid are optimized.
The prior art has the problems that the automation level is not high enough, the automation control and the early warning can not be completely realized, and the efficiency is low and the accuracy is inaccurate due to the need of manual intervention and treatment.
Disclosure of Invention
The application mainly solves the problems of low efficiency and inaccuracy caused by manual intervention and processing because the automation level is not high enough and automatic control and early warning cannot be completely realized.
In view of the above problems, the present application provides a method and a system for controlling a power grid based on an antagonistic neural network, and in a first aspect, an embodiment of the present application provides a method for controlling a power grid based on an antagonistic neural network, where the method includes: and calling local power grid basic information of a target management and control area, wherein the local power grid basic information is acquired by taking an electrical system, electrical equipment, grid-connected access and scheduling regulation constraint principle as a reference. And combining a digital twinning technology to build a twinning topology network based on the local power grid basic information, wherein the twinning topology network has high consistency with a local power grid. And carrying out power grid operation and maintenance supervision prediction based on the twin topology network, and carrying out power grid operation and maintenance regulation analysis by combining an countermeasure network model to determine a power grid regulation scheme, wherein the countermeasure network model takes game balance points as training convergence conditions. And feeding the power grid regulation scheme back to a regulation center, generating a power grid regulation instruction with a regulation time sequence node and a regulation sequence identified, and generating communication connection among the local power grid, the twin topology network, the countermeasure network model and the regulation center. And the power grid regulation and control instruction is issued to regional personnel terminals and regional target equipment, targeted regulation and control based on power grid dispatching and operation and maintenance are performed, and regulation and control feedback information is obtained. And updating the twin topology network based on the regulation feedback information, and implementing operation and control synchronous management.
In a second aspect, the present application provides an antagonistic neural network based grid regulation system, the system comprising: the basic information calling module is used for calling local power grid basic information of a target management and control area, and the local power grid basic information is acquired by taking an electrical system, electrical equipment, grid-connected access and scheduling regulation and control constraint principle as a reference. The network construction module is used for constructing a twin topology network based on the local power grid basic information by combining a digital twin technology, and the twin topology network has high consistency with the local power grid. And determining a power grid regulation scheme module, wherein the power grid regulation scheme determining module is used for carrying out power grid operation and maintenance supervision prediction based on the twin topology network, carrying out power grid operation and maintenance regulation analysis by combining an countermeasure network model, and determining the power grid regulation scheme, wherein the countermeasure network model takes game balance points as training convergence conditions. The regulation scheme feedback module is used for feeding back the power grid regulation scheme to a regulation center, generating a power grid regulation instruction marked with a regulation time sequence node and a regulation sequence, and generating a communication connection among the local power grid, the twin topology network, the countermeasure network model and the regulation center. And the targeted regulation scheme is used for sending the power grid regulation instruction to regional personnel terminals and regional target equipment, performing targeted regulation based on power grid dispatching and operation and maintenance, and acquiring regulation feedback information. And the operation control management module is used for updating the twin topology network based on the regulation feedback information and implementing operation control synchronous management.
One or more technical schemes provided by the application have at least the following technical effects or advantages:
the application provides a power grid regulation and control method and a system based on an antagonistic neural network, and relates to the technical field of power grid regulation and control, wherein the method comprises the following steps: firstly, local power grid basic information of a target management and control area is called, a twin topology network is built by combining a digital twin technology, operation and maintenance of a power grid are monitored and predicted, so that a power grid regulation and control scheme is determined, the regulation and control scheme is fed back to a regulation and control center to generate a regulation and control instruction, the instruction is put into equipment of the target area for targeted regulation and control, feedback information is obtained, and the network is updated.
The application mainly solves the problems of low efficiency and precision caused by insufficient automation level, low information integration level, insufficient intelligent level, lack of standardization and normalization, uneven personnel quality and the like in the prior art. By adopting the countermeasure neural network, the running state of the power grid is effectively predicted and regulated, the stability and reliability of the power grid are improved, and meanwhile, the energy consumption is reduced.
The foregoing description is only an overview of the present application, and is intended to be implemented in accordance with the teachings of the present application in order that the same may be more clearly understood and to make the same and other objects, features and advantages of the present application more readily apparent.
Drawings
For a clearer description of the present disclosure or of the prior art, the drawings used in the description of the embodiments or of the prior art will be briefly described, it being obvious that the drawings in the description below are only exemplary and that other drawings may be obtained, without inventive effort, by a person skilled in the art, from the provided drawings.
Fig. 1 is a schematic flow chart of a power grid regulation method based on an antagonistic neural network according to an embodiment of the present application;
FIG. 2 is a schematic flow chart of a method for generating the power grid regulation command and locking a receiving target in a power grid regulation method based on an antagonistic neural network according to an embodiment of the present application;
FIG. 3 is a schematic flow chart of a method for determining a diffusion regulation scheme with regulation necessity in a power grid regulation method based on an antagonistic neural network according to an embodiment of the present application;
Fig. 4 is a schematic structural diagram of a power grid regulation system based on an antagonistic neural network according to an embodiment of the present application.
Reference numerals illustrate: the system comprises a basic information calling module 10, a network construction module 20, a power grid regulation scheme determining module 30, a regulation scheme feedback module 40, a targeted regulation scheme 50 and a control management module 60.
Detailed Description
The application mainly solves the problems of low efficiency and inaccuracy caused by manual intervention and processing because the automation level is not high enough and automatic control and early warning cannot be completely realized. By adopting the countermeasure neural network, the running state of the power grid is effectively predicted and regulated, the stability and reliability of the power grid are improved, and meanwhile, the energy consumption is reduced.
For a better understanding of the foregoing technical solutions, the following detailed description will be given with reference to the accompanying drawings and specific embodiments of the present invention:
Example 1
A privacy protection method for a road monitoring system as shown in fig. 1, the method comprising:
calling local power grid basic information of a target management and control area, wherein the local power grid basic information is acquired by taking an electrical system, electrical equipment, grid-connected access and scheduling regulation constraint principle as a reference;
specifically, a target area to be explicitly controlled, i.e., a specific area to be grid-controlled, is first required. Determining local power grid basic information: aiming at the established target management and control area, local power grid basic information of the area needs to be acquired. Such information should include all electrical systems, electrical equipment, grid-tie access, and scheduling regulatory constraints within the area. Such information may be obtained in a variety of ways, such as from a grid operation database, performing a field survey, consulting a related technician, etc. Acquiring local power grid basic information by taking an electrical system, electrical equipment and grid connection access and scheduling regulation constraint principle as a reference: in the acquired local power grid basic information, the information is classified and arranged by taking an electric system, electric equipment, grid-connected access and scheduling regulation constraint principle as main references. For example, the classification may be by type of electrical system, power class of device, mode of grid-tie access, scheduling regulatory constraints, and so forth. Analyzing local power grid basic information: after the local power grid basic information is classified and arranged, the local power grid basic information needs to be analyzed. The method is mainly used for evaluating the running state, potential risks, possible optimization schemes and the like of the power grid according to the characteristics of information such as an electrical system, electrical equipment, grid-connected access, scheduling regulation and control constraint principles and the like.
Combining a digital twinning technology to build a twinning topology network based on the local power grid basic information, wherein the twinning topology network has high consistency with a local power grid;
Specifically, the collected data are cleaned and arranged, invalid and error data are removed, the data format and standard are unified, and a digital twin model is constructed: based on the collected data, a digital twin model which is highly consistent with the physical power grid is constructed by utilizing a digital twin technology. The model can comprise mathematical models, simulation models and the like of various devices, and can simulate the actual running condition of the power grid. Building a twin topological network: and assembling and connecting the digital twin model according to the topological structure of the local power grid, and constructing a digital twin topological network with high consistency with the local power grid. The network can reflect the running state and the change condition of the local power grid in real time. Data fusion and optimization: and fusing the collected data with the digital twin model, optimizing the model by utilizing a data analysis and optimization algorithm, and improving the accuracy and response speed of the digital twin model so that the digital twin model is more close to the running condition of an actual power grid. The digital twin technology can be combined to build a twin topology network based on local power grid basic information, so that the real-time monitoring, prediction and regulation of the power grid are realized, and the stability and reliability of the power grid are improved.
Performing power grid operation and maintenance supervision prediction based on the twin topology network, and performing power grid operation and maintenance regulation analysis by combining an countermeasure network model to determine a power grid regulation scheme, wherein the countermeasure network model takes game balance points as training convergence conditions;
Specifically, grid operation and maintenance supervision prediction is performed based on a twin topology network: the digital twin topology network with the height consistent with the physical power network is constructed by utilizing the digital twin technology, and the network can reflect the running state and the change condition of the local power network in real time. By carrying out real-time monitoring and prediction on the digital twin topology network, various problems and risks possibly occurring in the power grid can be found in time, and a basis is provided for subsequent regulation and control decisions. And carrying out power grid operation and maintenance regulation analysis by combining the countermeasure network model: the countermeasure network model is a deep learning model and can be used for power grid regulation analysis. The regulation and control process of the power grid can be simulated and analyzed by regarding various factors (such as power supply, load, circuit and the like) in the power grid as opposite parties and taking game balance points of the factors as training convergence conditions. Through training and application of the countermeasure network model, an optimal regulation and control scheme can be determined so as to ensure safe, stable and economic operation of the power grid. Determining a power grid regulation scheme: and determining a final power grid regulation scheme according to the power grid operation and maintenance supervision prediction result and the output of the countermeasure network model. The solution should include various regulation measures and countermeasures, such as adjusting the power output of the generator set, transferring the load, repairing the fault, etc. Training convergence condition selection of an antagonistic network model: the training convergence condition of the countermeasure network model is a game balance point, and the selection of the balance point is determined according to the running condition and regulation experience of an actual power grid. By comparing and analyzing different equalization points, the optimal equalization point can be found to obtain a more accurate regulation scheme. The method can be used for carrying out power grid operation and maintenance supervision prediction and regulation analysis based on a digital twin technology and an countermeasure network model, determining a final power grid regulation scheme, realizing real-time monitoring, prediction and regulation of the power grid, and improving the stability and reliability of the power grid.
Feeding back the power grid regulation scheme to a regulation center, generating a power grid regulation instruction with a regulation time sequence node and a regulation sequence identified, and generating a communication connection among the local power grid, the twin topology network, the countermeasure network model and the regulation center;
Specifically, a communication connection is established with a local power grid, a twin topology network and a regulation and control center: through proper communication technology and interfaces, stable communication connection is established with a local power grid, a digital twin topology network and a regulation and control center, and real-time transmission and reception of data and instructions are ensured. Transmission regulation scheme: the determined power grid regulation scheme is sent to a regulation center in the form of data, and can comprise information such as regulation strategies, time sequence nodes, regulation sequences and the like. Analysis and regulation scheme: after receiving the regulation scheme, the regulation center needs to analyze the scheme, understand the regulation tasks and the regulation sequences of each time sequence node, and determine the corresponding operation instruction. Generating a regulation instruction: according to the analysis result of the regulation scheme, the regulation center can generate a power grid regulation command with an explicit operation command, wherein the power grid regulation command comprises detailed information such as a specific command for operating equipment, an operation time sequence, an operation sequence and the like. Sending a regulation command: and sending the generated power grid regulation and control instruction to corresponding equipment such as a local power grid, a digital twin topology network and the like, and preparing for actual operation and regulation and control.
The power grid regulation and control instruction is issued to regional personnel terminals and regional target equipment, targeted regulation and control based on power grid dispatching and operation and maintenance are carried out, and regulation and control feedback information is obtained;
Specifically, the regulation and control instruction is issued to regional personnel terminals: the regulation center sends the power grid regulation command to regional personnel terminals, such as a work station or mobile equipment of a power dispatcher, in the form of data. Analyzing the regulation and control instruction: after receiving the regulation and control instruction, the regional personnel terminal needs to analyze the instruction and know information such as regulation and control tasks, time sequence nodes, operation requirements and the like. Making a regulation and control plan: according to the requirements of the regulation and control instruction and the actual condition of target equipment, regional personnel make a specific regulation and control plan, including the starting time, duration, operation steps and the like of regulation and control. Issuing a regulation command to the regional target equipment: the regulatory plan is downloaded in the form of data to regional target devices, such as intelligent power devices, automated control systems, and the like. Executing a regulation and control plan: after receiving the regulation and control instruction, the regional target equipment executes corresponding regulation and control operation according to the instruction requirement, and feeds back the operation result in real time. And (3) collecting regulation feedback information: and the regional personnel terminal collects regulation feedback information, such as state change, operation result and the like of the equipment according to the execution condition of the regulation plan. Analyzing the regulation feedback information: regional personnel evaluate and analyze the regulation effect according to the collected regulation feedback information, and make corresponding adjustment and optimization. And (5) feeding back a regulation result: regional personnel feed back the regulation and control result to the regulation and control center in the form of data, including information such as problems, difficulties and obtained results in the regulation and control process. The method can realize that the power grid regulation and control instruction is issued to regional personnel terminals and regional target equipment, and targeted regulation and control based on power grid dispatching and operation and maintenance are performed. Meanwhile, the accuracy and efficiency of power grid regulation and control can be further improved by collecting and analyzing the regulation and control feedback information, and powerful guarantee is provided for safe, stable and economic operation of the power system.
And updating the twin topology network based on the regulation feedback information, and implementing operation and control synchronous management.
Specifically, regulatory feedback information is collected: and collecting feedback information generated in the regulation and control process, wherein the feedback information comprises state change, operation result, regulation and control effect and the like of the equipment. Such information may be collected by regional personnel terminals and regional target devices, as well as from grid monitoring systems and other data sources. Analyzing the regulation feedback information: analyzing the collected regulation feedback information, and knowing the actual regulation effect and possible problems. For example, accuracy and efficiency of regulation may be assessed by comparing actual operating results to expected regulation plans. Updating the digital twin model: and updating the digital twin model according to the analysis result. May include modifying device model parameters, modifying predictions, updating topology, etc. By updating the digital twin model, the digital twin model can be made to be closer to the running condition of the actual power grid. Updating the twin topology network: after the digital twin model is updated, it needs to be assembled and connected into a new twin topology network. The network should remain highly consistent with the actual grid for subsequent monitoring and prediction. And (3) implementing operation and control synchronous management: after the digital twin model is updated and the twin topology network is updated, operation and control synchronous management needs to be implemented. The running condition of the power grid is monitored and predicted in real time, so that problems are found in time and regulated. And simultaneously, recording and analyzing the regulation and control process so as to optimize the regulation and control strategy and countermeasures. The digital twin topology network can be updated based on the regulation feedback information, and the operation and control synchronous management is implemented, so that the high efficiency, the precision and the intellectualization of the power grid regulation are realized.
Furthermore, the method of the application builds the twin topology network based on the local power grid basic information, and the method comprises the following steps:
reading a power grid operation principle, wherein the power grid operation principle comprises a multidimensional operation principle based on grid-connected interactive management, an electric power mutual-aid mode and a consumption scheduling capability;
based on the power grid management principle, carrying out local basic capability configuration and management principle assignment on the twin topology network;
and determining regional power grid management risk, and identifying the twin topology network perfectly.
Specifically, the grid management principle is read: and reading the grid management principle from a database or a file, wherein the principle comprises a multidimensional management principle of grid-connected interactive management, an electric power mutual-aid mode and a consumption scheduling capability. Local basic capability configuration and management principle assignment are carried out based on the power grid management principle: and configuring the digital twin model according to the read power grid management principle so as to enable the digital twin model to have corresponding local basic capability. Meanwhile, according to multidimensional management principles of grid-connected interactive management, power mutual-aid modes and consumption scheduling capacity, corresponding management principles are given to the digital twin model. Determining regional power grid management risk: by analyzing historical operating data, current operating conditions, and other relevant information of the grid, the risk that regional grid management may face may be determined. These risks may include equipment failure, load fluctuations, network security attacks, etc. The unavoidable large risk time, for example, the directional influence of typhoon earthquake frequent regions, needs to be subjected to early prevention decision of power grid dispatching operation. The identification of the twin topology network is perfect: and identifying and perfecting the digital twin model according to the determined regional power grid transportation risk. Information such as risk areas, probability distribution of equipment faults and the like can be marked so as to facilitate subsequent power grid regulation and risk management. The digital twin topology network can be configured and endowed with the management principle based on the grid management principle, and the regional grid management risk is determined and identified perfectly so as to facilitate subsequent grid regulation and risk management.
Further, the method of the application carries out the supervision and prediction of the operation and maintenance of the power grid based on the twin topological network, and comprises the following steps:
configuring an operation and maintenance prediction mode, wherein the operation and maintenance prediction mode comprises a time flow rate adjustment mode and a risk collision test mode;
Determining a predicted demand and activating a pre-test mode based on the operation and maintenance prediction mode;
And combining the twin topology network, and carrying out demand compliance regulation and control and risk positioning on the network operation condition of the local power grid in the target management and control area based on the pre-test mode to determine wind control prediction data, wherein the wind control prediction data comprises risk time node-risk positioning node-risk occurrence information.
Specifically, the operation and maintenance prediction mode is configured: this includes a time flow rate adjustment mode and a risk collision test mode. May be implemented using machine learning or analog simulation, etc. And (5) performing time flow speed adjustment, and positioning the fault time and the fault position under normal operation and maintenance. And performing scene simulation test to determine the bearing capacity under the current running condition so as to perform wind control prevention. Determining a predicted demand: these requirements may include power demand predictions, equipment failure predictions, network security risk predictions, and the like. These requirements may be determined by analyzing historical data, real-time monitoring data, and other relevant information. Activating a pre-test mode based on the operation and maintenance prediction mode: once the prediction needs are determined, the corresponding pretest patterns may be activated to test and verify the validity and accuracy of the operation and maintenance prediction patterns. Combining with a twin topology network: this network may provide real-time operational data and historical data of the power grid to support the operation and optimization of the pre-test mode. Carrying out demand compliance regulation and risk positioning on network operation conditions of a local power grid in a target management and control area based on a pre-test mode: the method comprises the steps of simulating and predicting the running condition of the power grid by using a pre-test mode, determining whether regulation is needed according to the simulation result, and regulating how to meet the requirement. At the same time, it is also necessary to determine and locate the risk that may occur in the operation of the grid. Determining wind control prediction data: this data includes risk time nodes, risk location nodes, and risk occurrence information. These data can be used for subsequent risk management and regulatory decisions. The operation and maintenance prediction mode can be configured, the prediction demand is determined, the prediction mode is activated, the demand compliance regulation and risk positioning of the network operation condition are carried out on the local power grid of the target management and control area by combining the twin topology network, and wind control prediction data are determined to support subsequent risk management and regulation decision. These steps help to improve the stability and reliability of the grid and reduce the risk that may occur during operation of the grid.
Furthermore, the method of the application combines the countermeasure network model to carry out the operation and maintenance regulation analysis of the power grid, and the method comprises the following steps:
transmitting the wind control prediction data to the countermeasure network model;
calibrating a regulation priority based on the risk time node, the risk grade and the risk expansion efficiency;
And taking the regulation priority as a constraint, taking the twin topological network as a bottom layer framework, and carrying out sequential traversal regulation analysis on the wind control prediction data by combining the countermeasure network model to determine the power grid regulation scheme.
Specifically, the wind control prediction data is transmitted into the antagonistic network model: the determined wind control prediction data is transmitted from the preprocessing module into the antagonistic network model, the data comprising risk time nodes, risk location nodes and risk occurrence information. Calibrating and regulating priority based on the risk time node, the risk grade and the risk expansion efficiency: after the wind control prediction data is received, the regulation priority is required to be calibrated according to the risk time node, the risk grade and the risk expansion efficiency. This can be done by looking up and analyzing the data in the data that has the earliest risk time node, highest risk level, or most efficient risk expansion. Taking the regulation priority as constraint, taking a twin topological network as a bottom layer framework, and carrying out sequential traversal regulation analysis by combining with an countermeasure network model: and sequentially traversing regulation and control analysis by taking the calibrated regulation and control priority as constraint and utilizing the twin topology network as a bottom layer framework and combining the countermeasure network model. This can be done by sequentially applying the antagonistic network model to each node device in the twin topology network according to the level of regulatory priority. Determining a power grid regulation scheme: after the sequential traversal regulation analysis is completed, a final power grid regulation scheme needs to be determined according to the analysis result. This can be determined by finding the most appropriate regulatory strategy, timing nodes, regulatory sequences, etc. information in the analysis results. The wind control prediction data can be transmitted to the countermeasure network model, the regulation priority is calibrated according to the risk time node, the risk grade and the risk expansion efficiency, the regulation analysis is sequentially traversed by the infrastructure twin topology network and the countermeasure network model, and finally the power grid regulation scheme is determined.
Further, the method of the application constructs an antagonistic network model, comprising:
Setting an adaptive training principle and a training convergence condition, wherein the adaptive training principle comprises a random generation principle and a regulation and control judgment principle;
constructing a target generator and a target discriminator by taking the self-adaptive training principle and the training convergence condition as constraints;
and carrying out joint alternate optimization training on the target generator and the target discriminator to generate the countermeasure network model.
Specifically, an adaptive training principle and a training convergence condition are set: firstly, an adaptive training principle and a training convergence condition need to be set. The adaptive training principle can comprise a random generation principle and a regulation and control judgment principle so as to dynamically adjust the training process according to the running condition and regulation and control experience of an actual power grid. The training convergence condition may then include the number of training rounds, the minimum of the loss function, the model performance index, etc., to control the convergence criteria and early stop conditions of the training. And generating a plurality of debugging schemes based on operation control abnormality based on the generator, and determining an optimal scheme based on scheme effect judgment analysis performed by a discriminator. Building a target generator and a target discriminator: according to the set self-adaptive training principle and training convergence condition, a target generator and a target discriminator can be built. The target generator may be configured to generate a series of random grid regulation schemes based on a random generation principle, and the target arbiter may be configured to discriminate and evaluate the regulation schemes based on a regulation decision principle. Performing joint alternating optimization training on the target generator and the target discriminator: after the target generator and the target discriminator are built, joint alternate optimization training can be started. During training, the target generator and target arbiter will alternately update and optimize model parameters to minimize the loss function and achieve the ideal model performance index. Generating an antagonistic network model: after the combined alternate optimization training of a plurality of rounds, the antagonistic network model can be finally generated. The model has excellent power grid regulation and control performance and prediction capability, and can automatically generate a proper regulation and control scheme according to the running condition of an actual power grid.
Further, as shown in fig. 2, the method of the present application generates a power grid regulation command with a regulation timing node and a regulation sequence identified, and the method includes:
setting an instruction effective interval period, wherein the instruction effective interval period is a set push-forward time interval based on the regulation time sequence node;
Combining the instruction effective interval period, and identifying a regulation moment point based on the regulation time sequence node aiming at each power grid regulation scheme;
And generating the power grid regulation and control instruction and locking a receiving target aiming at the regulation and control moment, wherein the receiving target is an regional personnel terminal and regional target equipment.
Specifically, the instruction validation interval period is set: first, a command validation interval period needs to be set, and the time interval is based on a push-forward time interval of the regulation time sequence node. For example, if the regulatory timing nodes are every 10 minutes, the instruction validation interval period may be every 5 minutes. The time interval is set to ensure that the power grid regulation command can be timely and accurately validated. Identifying regulation time points based on regulation time sequence nodes aiming at each power grid regulation scheme: after the command validation interval period is set, for each power grid regulation scheme, a regulation time point can be identified according to a regulation time sequence node. For example, if the regulatory timing nodes are every 10 minutes and the command validation interval period is every 5 minutes, then there may be two corresponding regulatory time points per regulatory scheme, i.e., one regulatory every 5 minutes. Generating a power grid regulation command aiming at a regulation moment and locking a receiving target: after the regulation time points are determined, power grid regulation instructions can be generated according to the time points, and the instructions are locked and sent to the target regional personnel terminal and the regional target equipment. The purpose of locking the receiving target is to ensure that the instruction can be accurately communicated to the target device and regulated at the designated moment.
Further, as shown in fig. 3, the method of the present application includes:
Receiving relay protection response triggered by the local power grid;
Transmitting the relay protection response to the countermeasure network model, performing cooperative association influence analysis based on response diffusion, and determining the local area sweep information of the power grid;
and screening the local sweep information of the power grid to determine a diffusion regulation scheme with regulation necessity.
Specifically, receiving a relay protection response triggered by the local power grid: and when abnormal conditions occur in the local power grid and relay protection responses are triggered, the relay protection responses can be received. These responses may include abnormal changes in parameters such as current, voltage, frequency, etc., as well as information about the operation of the protection device, etc. Transmitting the relay protection response to the antagonistic network model: after receiving the relay protection responses, these responses are transmitted into the antagonistic network model. The antagonistic network model can be used to analyze the diffuse effects of these responses, as well as their synergistic correlated effects on the overall grid. And carrying out cooperative correlation influence analysis based on response diffusion, and determining the local area sweep information of the power grid: the antagonistic network model can analyze the diffusion condition of relay protection responses and the cooperative association influence of the relay protection responses among different devices and different areas. From this analysis, local sweep information of the power grid can be determined, including affected devices, affected areas, affected times, and the like. Relay protection is accompanied with the execution and early warning of fault protection measures. Based on the method, relay protection response is taken as a center, whether the rest range and the abnormal dimension are affected is analyzed, and screening, regulation and control analysis is performed. Screening the electric network local area sweep information to determine a diffusion regulation scheme with regulation necessity: after the local sweep information is obtained, the local sweep information can be screened according to actual needs. The main purpose of screening is to find out the areas and devices where regulatory measures are required, and then to determine the corresponding diffusion regulatory scheme based on this information. These regulation schemes can be applied directly to affected equipment and areas to reduce or eliminate abnormal fluctuations and instabilities of the power grid. The cooperative correlation influence analysis based on response diffusion can be effectively performed by using the countermeasure network model, the regional wave and information of the power grid is determined, and a diffusion regulation scheme with regulation necessity is screened out so as to cope with abnormal conditions in the power grid.
Example two
Based on the same inventive concept as the grid regulation method based on the antagonistic neural network in the foregoing embodiment, as shown in fig. 4, the present application provides a grid regulation system based on the antagonistic neural network, the system comprising:
The basic information calling module 10 is used for calling local power grid basic information of a target control area, and the local power grid basic information is acquired by taking an electrical system, electrical equipment, grid connection access and scheduling regulation constraint principle as a reference;
The network construction module 20 is used for constructing a twin topology network based on the local power grid basic information by combining a digital twin technology, and the twin topology network has high consistency with a local power grid;
The power grid regulation scheme determining module 30 performs power grid operation and maintenance supervision and prediction based on the twin topology network, and performs power grid operation and maintenance regulation analysis in combination with an countermeasure network model to determine a power grid regulation scheme, wherein the countermeasure network model takes game balance points as training convergence conditions;
The regulation scheme feedback module 40 is configured to feed back the power grid regulation scheme to a regulation center, generate a power grid regulation instruction with a regulation time sequence node and a regulation sequence identified, and generate a communication connection between the local power grid, the twin topology network, the countermeasure network model and the regulation center;
The targeted regulation and control scheme 50 is used for sending the power grid regulation and control instruction to regional personnel terminals and regional target equipment, performing targeted regulation and control based on power grid dispatching and operation and maintenance, and acquiring regulation and control feedback information;
and the operation control management module 60 is used for updating the twin topology network based on the regulation feedback information and implementing operation control synchronous management by the operation control management module 60.
Further, the system further comprises:
the operation rule reading module is used for reading a power grid operation rule, wherein the power grid operation rule comprises a multidimensional operation rule based on grid-connected interaction management, an electric power mutual-aid mode and a consumption scheduling capability;
The capacity configuration module is used for carrying out local basic capacity configuration and management principle assignment on the twin topology network based on the power grid management principle;
and the management risk determining module is used for determining regional power grid management risk and identifying the twin topological network perfectly.
Further, the system further comprises:
The prediction mode configuration module is used for configuring operation and maintenance prediction modes, including a time flow rate adjustment mode and a risk collision test mode;
The prediction requirement determining module is used for predicting and determining the prediction requirement and activating a pre-test mode based on the operation and maintenance prediction mode;
The risk positioning module is used for combining the twin topological network, carrying out demand compliance regulation and control and risk positioning on the network operation condition of the local power grid in the target management and control area based on the pre-test mode, and determining wind control prediction data, wherein the wind control prediction data comprises risk time node-risk positioning node-risk occurrence information.
Further, the system further comprises:
the data transmission module is used for transmitting the wind control prediction data to the countermeasure network model;
The regulation and control priority calibration module is used for calibrating the regulation and control priority based on the risk time node, the risk grade and the risk expansion efficiency;
And the regulation and control scheme determining module is used for determining the power grid regulation and control scheme by taking the regulation and control priority as a constraint, taking the twin topology network as a bottom layer framework and combining the countermeasure network model to carry out sequential traversal regulation and control analysis on the wind control prediction data.
Further, the system further comprises:
the training principle setting module is used for setting an adaptive training principle and a training convergence condition, wherein the adaptive training principle comprises a random generation principle and a regulation and control judgment principle;
the target generator building module is used for building a target generator and a target discriminator by taking the self-adaptive training principle and the training convergence condition as constraints;
and the countermeasure network model generation module is used for carrying out joint alternation optimization training on the target generator and the target discriminator to generate the countermeasure network model.
Further, the system further comprises:
The interval period setting module is used for setting an instruction effective interval period which is a set push-forward time interval based on the regulation and control time sequence node;
the regulation and control time point generation module is used for combining the instruction effective interval time period and identifying regulation and control time points based on the regulation and control time sequence nodes aiming at each power grid regulation and control scheme;
The receiving target generation module is used for generating the power grid regulation and control instruction and locking a receiving target aiming at the regulation and control moment, wherein the receiving target is an regional personnel terminal and regional target equipment.
Further, the system further comprises:
the protection response receiving module is used for receiving relay protection response triggered by the local power grid;
The power grid local area sweep information determining module is used for transmitting the relay protection response to the countermeasure network model, carrying out cooperative association influence analysis based on response diffusion and determining power grid local area sweep information;
and the information screening module is used for screening the electric network local area sweep information and determining a diffusion regulation scheme with regulation necessity.
The foregoing detailed description of the power grid regulation method based on the antagonistic neural network will clearly enable those skilled in the art to know the power grid regulation system based on the antagonistic neural network in this embodiment, and for the system disclosed in the embodiment, since it corresponds to the device disclosed in the embodiment, the description is simpler, and the relevant points refer to the description of the method section.
The previous description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the application. Thus, the present application is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims (8)
1. The power grid regulation and control method based on the antagonistic neural network is characterized by comprising the following steps of:
calling local power grid basic information of a target management and control area, wherein the local power grid basic information is acquired by taking an electrical system, electrical equipment, grid-connected access and scheduling regulation constraint principle as a reference;
Combining a digital twinning technology to build a twinning topology network based on the local power grid basic information, wherein the twinning topology network has high consistency with a local power grid;
Performing power grid operation and maintenance supervision prediction based on the twin topology network, and performing power grid operation and maintenance regulation analysis by combining an countermeasure network model to determine a power grid regulation scheme, wherein the countermeasure network model takes game balance points as training convergence conditions;
Feeding back the power grid regulation scheme to a regulation center to generate a power grid regulation instruction with a regulation time sequence node and a regulation sequence, wherein communication connection is established between the local power grid, the twin topology network, the countermeasure network model and the regulation center;
The power grid regulation and control instruction is issued to regional personnel terminals and regional target equipment, targeted regulation and control based on power grid dispatching and operation and maintenance are carried out, and regulation and control feedback information is obtained;
and updating the twin topology network based on the regulation feedback information, and implementing operation and control synchronous management.
2. The method according to claim 1, wherein the building of a twin topology network based on the local power grid basis information comprises:
reading a power grid operation principle, wherein the power grid operation principle comprises a multidimensional operation principle based on grid-connected interactive management, an electric power mutual-aid mode and a consumption scheduling capability;
based on the power grid management principle, carrying out local basic capability configuration and management principle assignment on the twin topology network;
and determining regional power grid management risk, and identifying the twin topology network perfectly.
3. The method of claim 1, wherein grid operation and maintenance supervision prediction is performed based on the twinning topology network, the method comprising:
configuring an operation and maintenance prediction mode, wherein the operation and maintenance prediction mode comprises a time flow rate adjustment mode and a risk collision test mode;
Determining a predicted demand and activating a pre-test mode based on the operation and maintenance prediction mode;
And combining the twin topology network, and carrying out demand compliance regulation and control and risk positioning on the network operation condition of the local power grid in the target management and control area based on the pre-test mode to determine wind control prediction data, wherein the wind control prediction data comprises risk time node-risk positioning node-risk occurrence information.
4. A method according to claim 3, wherein grid operation and maintenance regulation analysis is performed in conjunction with an antagonistic network model, the method comprising:
transmitting the wind control prediction data to the countermeasure network model;
calibrating a regulation priority based on the risk time node, the risk grade and the risk expansion efficiency;
And taking the regulation priority as a constraint, taking the twin topological network as a bottom layer framework, and carrying out sequential traversal regulation analysis on the wind control prediction data by combining the countermeasure network model to determine the power grid regulation scheme.
5. The method of claim 4, wherein constructing an antagonistic network model comprises:
Setting an adaptive training principle and a training convergence condition, wherein the adaptive training principle comprises a random generation principle and a regulation and control judgment principle;
constructing a target generator and a target discriminator by taking the self-adaptive training principle and the training convergence condition as constraints;
and carrying out joint alternate optimization training on the target generator and the target discriminator to generate the countermeasure network model.
6. The method of claim 1, wherein generating the grid regulation command identifying the regulation timing node and the regulation sequence comprises:
setting an instruction effective interval period, wherein the instruction effective interval period is a set push-forward time interval based on the regulation time sequence node;
Combining the instruction effective interval period, and identifying a regulation moment point based on the regulation time sequence node aiming at each power grid regulation scheme;
And generating the power grid regulation and control instruction and locking a receiving target aiming at the regulation and control moment, wherein the receiving target is an regional personnel terminal and regional target equipment.
7. The method of claim 1, characterized in that the method comprises:
Receiving relay protection response triggered by the local power grid;
Transmitting the relay protection response to the countermeasure network model, performing cooperative association influence analysis based on response diffusion, and determining the local area sweep information of the power grid;
and screening the local sweep information of the power grid to determine a diffusion regulation scheme with regulation necessity.
8. Grid regulation and control system based on antagonism neural network, characterized by that, this system includes:
The basic information calling module is used for calling local power grid basic information of a target control area, and the local power grid basic information is acquired by taking an electrical system, electrical equipment, grid connection access and scheduling regulation constraint principle as a reference;
The network construction module is used for constructing a twin topology network based on the local power grid basic information by combining a digital twin technology, and the twin topology network has high consistency with the local power grid;
the power grid regulation scheme determining module is used for performing power grid operation and maintenance supervision prediction based on the twin topology network, and performing power grid operation and maintenance regulation analysis in combination with the countermeasure network model to determine a power grid regulation scheme, wherein the countermeasure network model takes game balance points as training convergence conditions;
The regulation scheme feedback module is used for feeding back the power grid regulation scheme to a regulation center and generating a power grid regulation instruction marked with a regulation time sequence node and a regulation sequence, wherein the local power grid, the twin topology network, the countermeasure network model and the regulation center are in communication connection;
The targeted regulation and control scheme is used for lowering the power grid regulation and control instruction to regional personnel terminals and regional target equipment, performing targeted regulation and control based on power grid dispatching and operation and maintenance, and acquiring regulation and control feedback information;
And the operation control management module is used for updating the twin topology network based on the regulation feedback information and implementing operation control synchronous management.
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