A real-time dynamic simulation and analysis system for self-healing capability of distribution network

Abstract

The present invention belongs to the field of simulation analysis technology, specifically a real-time dynamic simulation analysis system for the self-healing capability of a distribution network, including a fault diagnosis signal output module for outputting a distribution network fault diagnosis signal, a distribution network fault judgment module for judging whether there is a fault in the distribution network, a communication system stability analysis module capable of obtaining a stability factor of a distribution network communication system, a distributed energy access analysis module capable of obtaining a distributed energy access factor of a distribution network, an optimized self-healing strategy determination module for determining an optimized self-healing strategy for a distribution network, and a strategy adjustment simulation module for determining an adjustment scheme for an optimized self-healing strategy for a distribution network. The present invention significantly improves the accuracy and adaptability of the self-healing strategy of the distribution network through real-time dynamic simulation analysis, thereby enhancing the reliability and recovery efficiency of the distribution network when facing complex fault scenarios and changes in operating conditions.

Description

Technical Field

[0001] The present invention belongs to the technical field of simulation analysis, and in particular relates to a real-time dynamic simulation analysis system for the self-healing capability of a distribution network. Background Art

[0002] In the distribution network, the large-scale access of distributed power sources (such as solar photovoltaic, wind power generation, etc.) and energy storage devices makes the structure and operation characteristics of the distribution network more complex. The intermittent and uncertain nature of these distributed energy sources has brought new challenges to the operation and control of the distribution network. As the development direction of the future power system, the smart grid emphasizes the intelligent monitoring, control and management of the power system. Distribution network self-healing is one of the important functions of the smart grid, which can realize the rapid detection, isolation and recovery of distribution network faults and improve the operation efficiency and reliability of the distribution network. The distribution network analysis system can provide accurate simulation and analysis results for the planning, design and operation of the smart grid, help power companies better realize the self-healing function of the distribution network, and promote the development of the smart grid.

[0003] However, traditional distribution network analysis systems are mainly based on static models and offline calculations, which makes it difficult to accurately reflect the dynamic characteristics and real-time changes of distribution networks in actual operation. When faced with complex fault scenarios and rapidly changing operating conditions, the analysis results of traditional methods are often not accurate and timely. In addition, the large-scale access of distributed energy in modern distribution networks makes the operating status of distribution networks more complicated, and traditional methods are difficult to effectively handle these complex situations. Therefore, developing a system that can dynamically simulate and analyze the self-healing ability of distribution networks in real time is of great significance to improving the operating efficiency and reliability of distribution networks. Summary of the invention

[0004] In view of the shortcomings of the prior art, the present invention provides a real-time dynamic simulation and analysis system for the self-healing capability of the distribution network. Through real-time dynamic simulation and analysis, the accuracy and adaptability of the self-healing strategy of the distribution network are significantly improved, thereby enhancing the reliability and recovery efficiency of the distribution network when facing complex fault scenarios and changes in operating conditions.

[0005] To achieve the above objectives, the present invention is implemented through the following technical solutions: a real-time dynamic simulation and analysis system for the self-healing capability of a distribution network, comprising:

[0006] The fault diagnosis signal output module collects the electrical parameters of the distribution network, obtains the status information of the distribution network equipment, builds the distribution network fault diagnosis model, and outputs the distribution network fault diagnosis signal;

[0007] The distribution network fault judgment module determines whether there is a fault in the distribution network based on the trained random forest model and the distribution network fault diagnosis signal;

[0008] The communication system stability analysis module analyzes the stability of the distribution network communication system with faults and obtains the stability factor of the distribution network communication system;

[0009] The distributed energy access analysis module collects the distributed energy access data of the distribution network with faults and obtains the distributed energy access factor of the distribution network;

[0010] The module for determining the optimized self-healing strategy determines the distribution network optimized self-healing strategy based on the distribution network fault diagnosis signal of the distribution network with faults, the stability factor of the distribution network communication system and the distributed energy access factor of the distribution network, combined with the particle swarm algorithm;

[0011] The strategy adjustment simulation module conducts simulation experiments on the distribution network with faults based on the distribution network optimization self-healing strategy, analyzes the distribution network simulation results, and determines the adjustment plan for the distribution network optimization self-healing strategy.

[0012] Preferably, the process of the fault diagnosis signal output module outputting the distribution network fault diagnosis signal is as follows:

[0013] Collect electrical parameters of the distribution network, including real-time voltage, real-time current, and power factor of the distribution network;

[0014] Calculate the voltage deviation signal of the distribution network :

[0015] ;

[0016] In the formula, is the real-time voltage of the distribution network, is the rated voltage of the distribution network;

[0017] Calculate the current deviation signal of the distribution network :

[0018] ;

[0019] In the formula, is the real-time current of the distribution network, is the rated current of the distribution network, e is a natural constant;

[0020] Obtain distribution network equipment status information, including distribution network transformer operating oil temperature, distribution network transformer operating winding temperature, and distribution network line insulation resistance;

[0021] Based on the voltage deviation signal of the distribution network, the current deviation signal of the distribution network, the power factor of the distribution network, the operating oil temperature of the distribution network transformer, the operating winding temperature of the distribution network transformer and the insulation resistance of the distribution network line, a distribution network fault diagnosis model is constructed, and a distribution network fault diagnosis signal is output as an analysis basis for determining whether there is a fault in the distribution network;

[0022] The distribution network fault diagnosis model is expressed as:

[0023] ;

[0024] In the formula, is the fault diagnosis signal of the distribution network. It is the electrical fault diagnosis signal of the distribution network. It is the fault diagnosis signal of distribution network equipment. is the power factor of the distribution network, yw is the operating oil temperature of the distribution network transformer, rz is the operating winding temperature of the distribution network transformer, and dz is the insulation resistance of the distribution network line.

[0025] Preferably, the process of the distribution network fault judgment module judging whether there is a fault in the distribution network is:

[0026] Input the distribution network fault diagnosis signal into the trained random forest model;

[0027] If the random forest model output result is 0, then there is no fault in the distribution network corresponding to the distribution network fault diagnosis signal;

[0028] If the output result of the random forest model is 1, there is a fault in the distribution network corresponding to the distribution network fault diagnosis signal.

[0029] Preferably, the process of the communication system stability analysis module obtaining the stability factor of the distribution network communication system is:

[0030] Analyze the reliability of communication links in distribution networks :

[0031] ;

[0032] In the formula, is the normal working time of the distribution network communication link, is the total working time of the communication link of the distribution network, and e is a natural constant;

[0033] Analyze the data transmission reliability of distribution network communication links :

[0034] ;

[0035] In the formula, The number of correct data transmission times of the distribution network communication link, is the total number of data transmissions in the distribution network communication link, is the data transmission delay of the jth communication link of the distribution network, j is the data transmission number of the communication link of the distribution network, j=1,2,3,..., ;

[0036] Obtain the fault frequency of communication equipment in the distribution network ;

[0037] Based on the reliability of distribution network communication link operation, data transmission reliability of distribution network communication link and the failure frequency of distribution network communication equipment, a comprehensive analysis is conducted to obtain the stability factor of the distribution network communication system, which is used as the analysis basis for determining the distribution network optimization self-healing strategy.

[0038] Distribution network communication system stability factor The way to obtain is:

[0039] .

[0040] Preferably, the process of the distributed energy access analysis module obtaining the distributed energy access factor of the distribution network is:

[0041] Collect the distributed energy access data of the distribution network with faults, including the distributed energy power generation per unit time, the distributed energy power factor, and the distributed energy access voltage;

[0042] Based on the collected distributed energy access data of distribution networks with faults, the distributed energy access factor of the distribution network is obtained as the analysis basis for determining the optimized self-healing strategy of the distribution network;

[0043] Distributed energy access factor in distribution network The way to obtain is:

[0044] ;

[0045] In the formula, is the power generation per unit time of distributed energy, is the distributed energy power factor, Provides voltage access for distributed energy resources.

[0046] Preferably, the process of determining the optimized self-healing strategy of the distribution network by the optimized self-healing strategy determination module is as follows:

[0047] Taking the minimum distribution network restoration time as the goal, minimize the distribution network restoration time;

[0048] Obtain the number of particles n stored in the database, each particle represents a possible distribution network self-healing strategy;

[0049] The position vector of the particle is , where i represents the particle number, i=1,2,3,...,n;

[0050] represents the value of the fault diagnosis signal of the distribution network of the i-th particle; represents the value of the stability factor of the ith particle distribution network communication system; represents the value of the distributed energy access factor of the i-th particle distribution network;

[0051] The velocity vector of the particle is ;

[0052] represents the velocity component of the fault diagnosis signal of the distribution network of the i-th particle; represents the velocity component of the stability factor of the ith particle distribution network communication system; represents the speed component of the distributed energy access factor of the i-th particle distribution network;

[0053] Randomly initialize the particle position and speed. For each particle's initial position, obtain the corresponding distribution network recovery time as the fitness value. For each particle, compare the fitness value of its current position with the fitness value of the best position it has ever experienced. If the current fitness value is better, update the individual optimal position.

[0054] Among the individual optimal positions of all particles, find the position with the best fitness value as the group optimal position;

[0055] When the maximum number of iterations stored in the database is reached, the optimal position of the group is output, and the distribution network self-healing strategy corresponding to the distribution network fault diagnosis signal, distribution network communication system stability factor, and distribution network distributed energy access factor contained in this position is marked as the distribution network optimized self-healing strategy.

[0056] Preferably, the process of determining the distribution network optimization self-healing strategy adjustment scheme by the strategy adjustment simulation module is as follows:

[0057] Based on the distribution network optimization self-healing strategy, a simulation experiment is conducted on the distribution network with faults to obtain the distribution network simulation result data, including the distribution network simulation fault impact range, the distribution network simulation self-healing recovery rate, and the distribution network simulation voltage qualification rate;

[0058] Based on the obtained distribution network simulation result data, a comprehensive analysis is performed to obtain the distribution network simulation result signal, which serves as the analysis basis for determining the distribution network optimization self-healing strategy adjustment plan;

[0059] The distribution network simulation result signal Fz is obtained as follows:

[0060] ;

[0061] In the formula, Simulate the fault impact range for distribution network simulation, Simulate the self-healing recovery rate for the distribution network, is the qualified rate of voltage in distribution network simulation, and e is a natural constant;

[0062] Obtain the distribution network simulation result signal-distribution network optimization self-healing strategy adjustment plan mapping table pre-stored in the database, and find the matching distribution network optimization self-healing strategy adjustment plan according to the distribution network simulation result signal by searching the mapping table.

[0063] Preferably, a simulation experiment is performed on a distribution network with a fault based on a distribution network optimization self-healing strategy to obtain distribution network simulation result data, including the following steps:

[0064] Obtain distribution network data, including line parameters, transformer parameters and load characteristic data;

[0065] Determine simulation software, including MATLAB and Simulink;

[0066] Build a distribution network simulation model based on the determined simulation software;

[0067] Implement faults on the distribution network simulation model after determining the fault type, fault location and fault time;

[0068] The distribution network optimization self-healing strategy is applied to the distribution network simulation model after the fault, the distribution network simulation model is monitored, and the distribution network simulation result data is obtained.

[0069] Preferably, obtaining the distribution network simulation result data includes the following steps:

[0070] For distribution network simulation, the impact range of fault simulation is as follows:

[0071] On the distribution network topology diagram, determine the nodes that need to be monitored, including power supply side nodes, load side nodes, different voltage level conversion nodes and key line intermediate nodes;

[0072] Based on the data acquisition function of MATLAB or Simulink, the electrical quantity data of the nodes to be monitored are stored in time series;

[0073] According to the distribution network operation standards and equipment parameters, the electrical quantity threshold is set to determine whether the electrical quantity data of the node to be monitored exceeds the electrical quantity threshold;

[0074] If it exceeds, the node to be monitored is used as the starting point, and the electrical quantity data of adjacent nodes are searched according to the network topology structure, and the adjacent nodes and connecting lines that exceed the electrical quantity threshold are included in the fault impact range;

[0075] Mark the fault impact range on the distribution network topology diagram based on MATLAB or Simulink drawing functions or visualization software;

[0076] For the distribution network simulation, the self-healing recovery rate is:

[0077] In the steady-state simulation stage before the fault occurs, all load modules in the distribution network simulation model are traversed by programming, and the active power value of each load module is accumulated to obtain the total active power of all load modules in the distribution network simulation model;

[0078] In the simulation phase after the distribution network optimization self-healing strategy is executed, the load module is traversed again to determine whether the load module has restored power supply by judging whether the load node voltage has returned to the normal range. For the load module that has restored power supply, its active power value is read, and the active power values ​​of all restored power loads are accumulated to obtain the total active power after restoration of the load modules that have successfully restored power supply.

[0079] The ratio of the total active power after recovery to the total active power is calculated to obtain the self-healing recovery rate of the distribution network simulation;

[0080] For the distribution network simulation voltage qualification rate:

[0081] Determine the voltage qualified range;

[0082] During the entire simulation process, the data recording function of the simulation software is used to continuously monitor the voltage of each node that needs to be monitored in the distribution network;

[0083] For each node to be monitored, the time during which its voltage is within the qualified range is counted, and the proportion of the time during which the voltage of each node to be monitored is within the qualified range to the total simulation time is calculated;

[0084] Obtain the weight of each node to be monitored, perform weighted summation on the weight and proportion, and obtain the qualified rate of the simulated voltage of the distribution network.

[0085] The present invention has the following beneficial effects:

[0086] The present invention collects electrical parameters of the distribution network and obtains equipment status information to build a fault diagnosis model, and then judges whether there is a fault in the distribution network based on the trained random forest model, thereby improving the accuracy and reliability of fault judgment. Understand the stability of the communication system under fault conditions to ensure that fault information and control instructions can be transmitted accurately and timely. Distributed energy is connected in large quantities in modern distribution networks, and its operating status has an important impact on the self-healing ability of the distribution network.

[0087] The present invention determines the distribution network optimization self-healing strategy based on the distribution network fault diagnosis signal, the communication system stability factor and the distributed energy access factor, and combines the particle swarm algorithm to quickly find the optimal self-healing strategy. Through simulation, the self-healing strategy is verified and optimized before actual application, and the strategy is adjusted in time to better adapt to different fault scenarios and distribution network operating conditions.

[0088] The present invention conducts a simulation experiment on a distribution network with faults through a distribution network optimization self-healing strategy, analyzes the distribution network simulation results, and determines an adjustment plan for the distribution network optimization self-healing strategy. Before actually applying the distribution network optimization self-healing strategy, the strategy can be fully tested in a virtual environment through the simulation experiment, thereby avoiding the risks that may be caused by directly applying an unverified strategy in an actual distribution network, ensuring that the strategy can play an expected role in actual operation, improving the success rate of strategy implementation, and improving strategy adaptability. Problems in the distribution network self-healing process can be discovered in a timely manner, and the strategy can be optimized according to new needs and situations. BRIEF DESCRIPTION OF THE DRAWINGS

[0089] Figure 1 It is a schematic diagram of module connection of the system of the present invention;

[0090] Figure 2 It is a schematic diagram of the real-time dynamic simulation analysis process of the present invention. DETAILED DESCRIPTION

[0091] The embodiments of the present invention are further described below in conjunction with the accompanying drawings:

[0092] like Figure 1 As shown, a real-time dynamic simulation and analysis system for the self-healing capability of a distribution network includes a fault diagnosis signal output module, a distribution network fault judgment module, a communication system stability analysis module, a distributed energy access analysis module, an optimized self-healing strategy determination module and a strategy adjustment simulation module.

[0093] The fault diagnosis signal output module collects the electrical parameters of the distribution network, obtains the status information of the distribution network equipment, builds the distribution network fault diagnosis model, and outputs the distribution network fault diagnosis signal.

[0094] The specific analysis process is as follows: collecting electrical parameters of the distribution network, including real-time voltage, real-time current and power factor of the distribution network;

[0095] Calculate the voltage deviation signal of the distribution network :

[0096] ;

[0097] In the formula, is the real-time voltage of the distribution network, is the rated voltage of the distribution network; represents the logarithm with base 2; represents the natural logarithm;

[0098] Calculate the current deviation signal of the distribution network :

[0099] ;

[0100] In the formula, is the real-time current of the distribution network, is the rated current of the distribution network, e is a natural constant;

[0101] The status information of distribution network equipment is obtained, including the operating oil temperature of the distribution network transformer, the operating winding temperature of the distribution network transformer, and the insulation resistance of the distribution network line. Based on the distribution network voltage deviation signal, the distribution network current deviation signal, the distribution network power factor, the distribution network transformer operating oil temperature, the distribution network transformer operating winding temperature and the distribution network line insulation resistance, a distribution network fault diagnosis model is constructed, and a distribution network fault diagnosis signal is output. The distribution network fault diagnosis signal is used as an analysis basis for determining whether there is a fault in the distribution network.

[0102] The distribution network fault diagnosis model is expressed as:

[0103] ;

[0104] In the formula, is the fault diagnosis signal of the distribution network. It is the electrical fault diagnosis signal of the distribution network. It is the fault diagnosis signal of distribution network equipment. is the power factor of the distribution network, yw is the operating oil temperature of the distribution network transformer, rz is the operating winding temperature of the distribution network transformer, dz is the insulation resistance of the distribution network line, and tanh is the hyperbolic tangent function.

[0105] By collecting electrical parameters such as real-time voltage, real-time current, power factor, and other electrical parameters of the distribution network, as well as equipment status information such as transformer operating oil temperature, winding temperature, and line insulation resistance, key aspects of distribution network operation are covered. For example, real-time voltage and current can reflect the load condition and power transmission status of the line, power factor can reflect the utilization efficiency of electric energy, transformer oil temperature, winding temperature, etc. are related to the health status and operating stability of the equipment, and line insulation resistance is directly related to the insulation performance and fault risk of the line. This comprehensive data collection provides a rich information basis for accurate diagnosis of distribution network faults.

[0106] The formulas for calculating voltage deviation signals and current deviation signals can quantify the degree of deviation of voltage and current from the rated values. These quantitative indicators can more accurately reflect the changes in the operating status of the distribution network and can detect potential fault signs earlier and more accurately than simple qualitative judgments.

[0107] Based on the above comprehensive data, a distribution network fault diagnosis model is constructed to organically combine electrical parameters and equipment status information. The distribution network electrical fault diagnosis signal and equipment fault diagnosis signal are calculated separately, and finally the distribution network fault diagnosis signal is obtained. This comprehensive model takes into account the mutual influence and synergy of multiple factors, and can diagnose distribution network faults more comprehensively and accurately.

[0108] The system can monitor the operation status of the distribution network in real time by collecting electrical parameters and equipment status information in real time and calculating the corresponding deviation signals and diagnostic signals. By continuously recording and analyzing these data, the changing trends of various parameters can be observed.

[0109] The distribution network fault judgment module, based on the trained random forest model and combined with the distribution network fault diagnosis signal, determines whether there is a fault in the distribution network.

[0110] The specific analysis process is: input the distribution network fault diagnosis signal into the trained random forest model; if the output result of the random forest model is 0, then the distribution network corresponding to the distribution network fault diagnosis signal does not have a fault; if the output result of the random forest model is 1, then the distribution network corresponding to the distribution network fault diagnosis signal does have a fault.

[0111] The random forest model is an integrated learning algorithm composed of multiple decision trees. By integrating the results of multiple decision trees, it can effectively reduce the risk of overfitting and improve the generalization ability and accuracy of the model. In the fault judgment of the distribution network, this model can handle the nonlinear relationship between complex electrical parameters and equipment status information, so as to more accurately judge whether there is a fault in the distribution network.

[0112] The distribution network fault diagnosis signal is input into the trained random forest model, and the state of the distribution network can be quickly determined based on the model output results (0 indicates no fault, 1 indicates fault). This method greatly simplifies the fault diagnosis process, does not require manual analysis of a large amount of complex data and parameters, and saves time and labor costs.

[0113] Timely detection of potential faults allows operation and maintenance personnel to intervene and handle them in advance to prevent the fault from further deteriorating, thereby reducing the impact of the fault on the distribution network and improving the reliability and stability of the distribution network.

[0114] The communication system stability analysis module analyzes the stability of the distribution network communication system with faults and obtains the stability factor of the distribution network communication system.

[0115] The specific analysis process is as follows:

[0116] Analyze the reliability of communication links in distribution networks :

[0117] ;

[0118] In the formula, is the normal working time of the distribution network communication link, The total working time of the communication link of the distribution network;

[0119] Analyze the data transmission reliability of distribution network communication links :

[0120] ;

[0121] In the formula, The number of correct data transmission times of the distribution network communication link, is the total number of data transmissions in the distribution network communication link, is the data transmission delay of the jth communication link of the distribution network, j is the data transmission number of the communication link of the distribution network, j=1,2,3,..., ;

[0122] Obtain the fault frequency of communication equipment in the distribution network ;

[0123] Based on the working reliability of the distribution network communication link, the data transmission reliability of the distribution network communication link and the failure frequency of the distribution network communication equipment, a comprehensive analysis is conducted to obtain the stability factor of the distribution network communication system. The stability factor of the distribution network communication system is used as the analysis basis for determining the optimized self-healing strategy of the distribution network.

[0124] Distribution network communication system stability factor The way to obtain is:

[0125] .

[0126] By analyzing the reliability of the distribution network communication link, the reliability of the communication link data transmission, and the frequency of communication equipment failures, the stability of the distribution network communication system is comprehensively evaluated from different aspects. The reliability of the communication link takes into account the ratio of normal working time to total working time, reflecting the overall availability of the link; the reliability of data transmission combines factors such as the number of correct data transmissions, the total number of data transmissions, and the transmission delay, and can accurately measure the quality and efficiency of data transmission; the frequency of communication equipment failures directly reflects the stability of the equipment. This multi-dimensional evaluation method can more comprehensively and accurately reflect the actual situation of the communication system, avoiding the limitations of a single indicator evaluation.

[0127] In the self-healing process of the distribution network, the communication system plays a vital role. The communication system is responsible for transmitting key data such as fault information and control instructions to ensure timely and accurate interaction between various devices and systems. If the communication system is unstable, it may cause delays or loss of fault information transmission, and control instructions cannot be correctly executed, thus affecting the implementation of the self-healing strategy, and may even cause self-healing failure, expanding the scope and duration of power outages.

[0128] The stability factor of the distribution network communication system is used as an analytical basis for determining the optimized self-healing strategy of the distribution network, which can help optimize the collaborative work between the self-healing strategy and the communication system.

[0129] The distributed energy access analysis module collects the distributed energy access data of the distribution network with faults, and obtains the distributed energy access factor of the distribution network based on the collected distributed energy access data of the distribution network with faults.

[0130] The specific analysis process is as follows: collect the distributed energy access data of the distribution network with faults, including the distributed energy power generation per unit time, the distributed energy power factor, and the distributed energy access voltage; based on the collected distributed energy access data of the distribution network with faults, obtain the distributed energy access factor of the distribution network, and use the distributed energy access factor of the distribution network as the analysis basis for determining the distribution network optimization self-healing strategy.

[0131] Distributed energy access factor in distribution network The way to obtain is:

[0132] ;

[0133] In the formula, is the power generation per unit time of distributed energy, is the distributed energy power factor, Provides voltage access for distributed energy resources.

[0134] The distributed energy access analysis module collects data on distributed energy generation per unit time, power factor, access voltage, and other aspects. These data cover the key factors that affect the operation of the power grid after the distributed energy is connected to the distribution network. The generation per unit time reflects the generation capacity of the distributed energy and directly affects the power balance of the power grid; the power factor is related to the reactive power balance and power quality of the power grid; and the access voltage is closely related to the voltage stability of the power grid. By integrating these data, we can fully and carefully understand the actual operating status of the distributed energy after it is connected to the distribution network and the degree of its impact on the power grid.

[0135] The access factor of distributed energy in the distribution network is used as an analytical basis for determining the optimization self-healing strategy of the distribution network, which can make the self-healing strategy more targeted. When formulating the self-healing strategy, the different performances of distributed energy in the case of faults and their impact on grid recovery can be considered according to the size and change trend of the access factor.

[0136] Detailed analysis and quantitative evaluation of distributed energy access will help ensure the stable operation of the distribution network with distributed energy access.

[0137] The module for determining the optimized self-healing strategy determines the distribution network optimized self-healing strategy based on the distribution network fault diagnosis signal of the distribution network with faults, the stability factor of the distribution network communication system and the distributed energy access factor of the distribution network, combined with the particle swarm algorithm.

[0138] The specific analysis process is as follows: taking the minimum distribution network recovery time as the goal, minimize the distribution network recovery time; obtain the number of particles n stored in the database, each particle represents a possible distribution network self-healing strategy;

[0139] The position vector of the particle is , where i represents the particle number, i=1,2,3,...,n;

[0140] represents the value of the fault diagnosis signal of the distribution network of the i-th particle; represents the value of the stability factor of the ith particle distribution network communication system; represents the value of the distributed energy access factor of the i-th particle distribution network;

[0141] The velocity vector of the particle is ;

[0142] represents the velocity component of the fault diagnosis signal of the distribution network of the i-th particle; represents the velocity component of the stability factor of the ith particle distribution network communication system; represents the speed component of the distributed energy access factor of the i-th particle distribution network;

[0143] The particle positions and velocities are randomly initialized. For each particle's initial position, the corresponding distribution network recovery time is obtained as the fitness value. For each particle, the fitness value of its current position is compared with the fitness value of the best position it has experienced before. If the current fitness value is better (closer to the value of the optimization target), the individual optimal position is updated; among the individual optimal positions of all particles, the position with the best fitness value is found as the group optimal position; when the maximum number of iterations stored in the database is reached, the group optimal position is output, and the distribution network self-healing strategy corresponding to the distribution network fault diagnosis signal, the distribution network communication system stability factor, and the distribution network distributed energy access factor contained in the position is marked as the distribution network optimization self-healing strategy.

[0144] The optimization self-healing strategy is determined based on the distribution network fault diagnosis signal, the communication system stability factor and the distributed energy access factor, covering the key aspects of the distribution network operation. The fault diagnosis signal reflects whether there is a fault in the power grid and the general situation of the fault, which is the basis for formulating the self-healing strategy; the communication system stability factor reflects the reliability of the communication system in transmitting information during the fault handling process, and the stability of the communication directly affects the execution effect of the self-healing strategy; the distributed energy access factor takes into account the impact of distributed energy on the power grid, which is crucial in modern distribution networks because the access of distributed energy changes the power flow and distribution of the power grid. Combining these factors, a more comprehensive self-healing strategy that adapts to the actual situation can be formulated, avoiding the one-sidedness and limitations of the strategy caused by considering only a single factor.

[0145] Combined with the particle swarm algorithm, the optimization is carried out with the goal of minimizing the recovery time of the distribution network. The particle swarm algorithm is an efficient intelligent optimization algorithm that simulates the foraging behavior of bird flocks to find the optimal solution in the search space. In the process of determining the self-healing strategy, each particle represents a possible self-healing strategy, and its position vector contains the values ​​of the fault diagnosis signal, the communication system stability factor, and the distributed energy access factor, while the velocity vector represents the changing trend of these factors.

[0146] By randomly initializing the particle position and speed, continuously iterating and updating, comparing the fitness value of each particle's current position (i.e., the distribution network recovery time) with the fitness value of the best position previously experienced, the optimal position of the group is found, thereby determining the optimal self-healing strategy. This method can accurately locate the strategy with the shortest recovery time among many possible strategies, improve self-healing efficiency, improve the adaptability and flexibility of self-healing strategies, and reduce failure time and impact on users.

[0147] The strategy adjustment simulation module conducts simulation experiments on the distribution network with faults based on the distribution network optimization self-healing strategy, analyzes the distribution network simulation results, and determines the adjustment plan for the distribution network optimization self-healing strategy.

[0148] The specific analysis process is as follows: based on the distribution network optimization self-healing strategy, a simulation experiment is conducted on a distribution network with a fault, and distribution network simulation result data is obtained, including the distribution network simulation fault impact range, the distribution network simulation self-healing recovery rate, and the distribution network simulation voltage qualification rate; based on the obtained distribution network simulation result data, a comprehensive analysis is performed to obtain the distribution network simulation result signal, and the distribution network simulation result signal is used as the analysis basis for determining the distribution network optimization self-healing strategy adjustment plan; a distribution network simulation result signal-distribution network optimization self-healing strategy adjustment plan mapping table pre-stored in the database is obtained, and a matching distribution network optimization self-healing strategy adjustment plan is found according to the distribution network simulation result signal by searching the mapping table.

[0149] The distribution network simulation result signal Fz is obtained as follows:

[0150] ;

[0151] In the formula, Simulate the fault impact range for distribution network simulation, Simulate the self-healing recovery rate for the distribution network, Simulate the voltage qualification rate for distribution network simulation.

[0152] Obtaining the simulation result data of the distribution network includes the following steps: obtaining the distribution network data, including line parameters, transformer parameters and load characteristic data; determining the simulation software, including MATLAB and Simulink; building the distribution network simulation model based on the determined simulation software; in MATLAB / Simulink, selecting the corresponding components from the power system module library. Add the line module, set the resistance, reactance and susceptance values ​​according to the collected parameters; select the transformer module, input the rated capacity, transformation ratio, short-circuit impedance and other parameters; for the load module, set the active and reactive power according to the load characteristics, and also set the load model parameters (such as constant power, constant current and constant impedance models); the distributed energy module sets the relevant attributes according to the type and parameters, and the photovoltaic module sets the parameters that affect the power generation such as light intensity and temperature.

[0153] Connect each component module according to the distribution network topology diagram. When connecting, ensure that the electrical connection is correct, pay attention to the line head and tail nodes, the connection position of the high and low voltage sides of the transformer, and the load and power access points. After the connection is completed, conduct a preliminary inspection to check whether the connection lines are crossed and whether the component parameters are complete and accurate. Set the simulation time and step size. The simulation time is determined according to the research problem. For example, if you study the transient process of a fault, it can be set to 0.1-1s; if you analyze the long-term operation characteristics of the distribution network, it can be set to several hours or even days. The simulation step size takes into account the calculation efficiency while ensuring accuracy. Generally, the electromagnetic transient simulation takes 0.001-0.01s, and the electromechanical transient simulation takes 0.01-0.1s.

[0154] After determining the fault type, fault location and fault time, implement faults on the distribution network simulation model; select fault types according to research priorities, such as studying fault detection and isolation strategies, setting single-phase grounding and phase-to-phase short circuit faults; analyzing the impact of distributed energy on fault recovery, and adding distributed energy fault types (such as local faults in photovoltaic arrays and wind turbine faults). Setting faults at the outgoing line of the substation can examine the action of the protection device; setting faults on the lines in the load concentration area to study the impact on important loads; setting faults near the distributed energy access point to analyze the interaction between distributed energy and the distribution network. For example, in the distribution network of an industrial park, the middle position of the line connecting the large factory and the access point close to the distributed photovoltaic power station are selected as the fault location.

[0155] The fault start time is selected during the peak, valley or normal load period. Peak faults can test the distribution network's ability to cope with heavy loads; valley faults can analyze the system's recovery characteristics when lightly loaded; normal faults simulate faults under normal operating conditions. The duration is set according to the fault type and actual conditions. Transient faults (such as flashovers caused by lightning) last for 0.01-0.1s; permanent faults (such as line short circuits, equipment damage) last for 0.5-10s. When studying the effect of the reclosing action, the fault duration is set in combination with the reclosing time.

[0156] The distribution network optimization self-healing strategy is applied to the distribution network simulation model after the fault, the distribution network simulation model is monitored, and the distribution network simulation result data is obtained.

[0157] For the impact range of distribution network simulation faults: on the distribution network topology diagram, determine the nodes to be monitored, including power supply side nodes (substation busbars), load side nodes (different types of load centers), different voltage level conversion nodes (both sides of the distribution transformer) and key line intermediate nodes; determine the number of monitoring nodes according to the scale and complexity of the distribution network. For a distribution network with a small scale and simple structure, select 20-50 nodes; for a distribution network with a large scale and complex structure, select 100-500 nodes to ensure that the monitoring data can accurately reflect the operating status of the distribution network.

[0158] Based on the data acquisition function of MATLAB or Simulink, the electrical quantity data of the nodes to be monitored are stored in time series; the voltage amplitude, phase, current size, and power (active and reactive) of the nodes are mainly monitored. Voltage reflects the quality of power and the operating status of the equipment, current reflects the load change and the size of the fault current, and power is used to analyze power balance and energy flow. Set the monitoring frequency according to research needs and simulation step size. When studying fast transient processes, the monitoring frequency is set to 10-100 times the simulation step size; when analyzing steady-state processes, the monitoring frequency can be the same as or lower than the simulation step size. For example, if the simulation step size is 0.01s, when studying transient processes, the monitoring frequency is set to 1kHz.

[0159] According to the distribution network operation standards and equipment parameters, the electrical quantity threshold is set, and based on the pre-written data analysis program (based on the known technology, the subsequent pre-written program is the same), it is judged whether the electrical quantity data of the node to be monitored exceeds the electrical quantity threshold; according to the distribution network operation standards and equipment parameters, the electrical quantity threshold is set. The lower limit of the voltage amplitude is generally 90%-95% of the rated voltage, the upper limit of the current is 1.2-1.5 times the rated current, and the power deviation range is determined according to the system design requirements.

[0160] If it exceeds, the node to be monitored is used as the starting point, and the electrical quantity data of adjacent nodes are searched according to the network topology structure, and the adjacent nodes and connecting lines that exceed the electrical quantity threshold are included in the fault impact range; the fault impact range is marked on the distribution network topology diagram based on the drawing function of MATLAB or Simulink or professional power system visualization software (which can be implemented based on the Power Systems Toolbox, etc.);

[0161] For the self-healing recovery rate of distribution network simulation: In the steady-state simulation stage before the fault occurs, all load modules in the distribution network simulation model are traversed by programming, and the active power value of each load module is accumulated to obtain the sum of the active power of all load modules in the distribution network simulation model; In the steady-state simulation stage before the fault occurs, all load modules in the distribution network simulation model are traversed by programming. In MATLAB / Simulink, the model traversal function is used to obtain the handle of each load module, and then its active power attribute is read.

[0162] In the simulation phase after the optimization self-healing strategy of the distribution network is executed, the load module is traversed again to determine whether the load module has restored power supply by judging whether the voltage of the load node has returned to the normal range (generally set to 95%-105% of the rated voltage). For the load module that has restored power supply, its active power value is read, and the active power values ​​of all restored power loads are accumulated to obtain the total active power after restoration of the load modules that have successfully restored power supply; the ratio of the total active power after restoration to the total active power is calculated to obtain the distribution network simulation self-healing recovery rate;

[0163] For the qualified rate of distribution network simulation voltage: determine the qualified range of voltage according to national standards or relevant industry standards (such as GB / T12325-2008 "Power Quality Supply Voltage Deviation"); during the entire simulation process, use the data recording function of the simulation software to continuously monitor the voltage of each node to be monitored in the distribution network; for each node to be monitored, count the time its voltage is within the qualified range based on a pre-written program, and calculate the proportion of the time that the voltage of each node to be monitored is within the qualified range to the total simulation time; obtain the weight of each node to be monitored, and perform weighted summation of the weight and the proportion to obtain the qualified rate of distribution network simulation voltage.

[0164] Through simulation experiments based on the self-healing strategy for distribution network optimization, we can obtain distribution network simulation result data including fault impact range, self-healing recovery rate, voltage qualification rate, etc. These data comprehensively reflect the effect of self-healing strategy in different dimensions. For example, the fault impact range can intuitively show the ability of the strategy to isolate faults and limit the spread of faults; the self-healing recovery rate reflects the efficiency of the strategy in restoring power supply; and the voltage qualification rate reflects the degree to which the strategy guarantees the voltage quality of the power grid. Combining these data, we can conduct a comprehensive and detailed evaluation of the self-healing strategy, avoiding the limitation of judging the pros and cons of the strategy based on a single indicator or subjective experience.

[0165] By pre-storing the distribution network simulation result signal-distribution network optimization self-healing strategy adjustment plan mapping table in the database, after obtaining the simulation result signal, the matching strategy adjustment plan can be quickly found by searching the mapping table. This greatly improves the efficiency of determining the strategy adjustment plan and avoids the tedious process of re-analyzing and formulating the adjustment plan every time.

[0166] Before applying the self-healing strategy to the actual distribution network, through simulation experiments and strategy adjustments, possible problems and risks in the strategy can be discovered in advance, and optimization and improvement can be carried out. This can reduce the risks that may arise when applying new strategies or adjusting strategies in the actual power grid, such as avoiding large-scale power outages and equipment damage caused by improper strategies.

[0167] like Figure 2 As shown, the complete real-time dynamic simulation analysis process of this embodiment is:

[0168] Step 1: Collect the electrical parameters of the distribution network, obtain the status information of the distribution network equipment, build a distribution network fault diagnosis model, and output the distribution network fault diagnosis signal;

[0169] Step 2: Based on the trained random forest model and combined with the distribution network fault diagnosis signal, determine whether there is a fault in the distribution network;

[0170] Step 3: Analyze the stability of the distribution network communication system with faults to obtain the stability factor of the distribution network communication system;

[0171] Step 4: Collect the distributed energy access data of the distribution network with faults to obtain the distributed energy access factor of the distribution network;

[0172] Step 5: Based on the distribution network fault diagnosis signal of the distribution network with faults, the stability factor of the distribution network communication system and the distributed energy access factor of the distribution network, combined with the particle swarm algorithm, determine the distribution network optimization self-healing strategy;

[0173] Step 6: Based on the distribution network optimization self-healing strategy, a simulation experiment is conducted on the distribution network with faults, the distribution network simulation results are analyzed, and an adjustment plan for the distribution network optimization self-healing strategy is determined.

Claims

1. A real-time dynamic simulation and analysis system for the self-healing capability of a distribution network, characterized in that: include: The fault diagnosis signal output module collects the electrical parameters of the distribution network, obtains the status information of the distribution network equipment, builds the distribution network fault diagnosis model, and outputs the distribution network fault diagnosis signal; The distribution network fault judgment module determines whether there is a fault in the distribution network based on the trained random forest model and the distribution network fault diagnosis signal; The communication system stability analysis module analyzes the stability of the distribution network communication system with faults and obtains the stability factor of the distribution network communication system. , and the way to obtain it is: ； In the formula, e is a natural constant, For the reliability of the communication link of the distribution network, To ensure the reliability of data transmission in the communication link of the distribution network, is the failure frequency of communication equipment in the distribution network; Distributed energy access analysis module collects the distributed energy access data of distribution networks with faults and obtains the distributed energy access factors of distribution networks , and the way to obtain it is: ； In the formula, is the power generation per unit time of distributed energy, is the distributed energy power factor, Provide voltage access for distributed energy resources; The module for determining the optimized self-healing strategy determines the distribution network optimized self-healing strategy based on the distribution network fault diagnosis signal of the distribution network with faults, the stability factor of the distribution network communication system and the distributed energy access factor of the distribution network, combined with the particle swarm algorithm; The strategy adjustment simulation module conducts simulation experiments on the distribution network with faults based on the distribution network optimization self-healing strategy, analyzes the distribution network simulation results, and determines the adjustment plan for the distribution network optimization self-healing strategy.

2. A real-time dynamic simulation and analysis system for the self-healing capability of a distribution network according to claim 1, characterized in that: The process of the fault diagnosis signal output module outputting the distribution network fault diagnosis signal is as follows: Collect electrical parameters of the distribution network, including real-time voltage, real-time current, and power factor of the distribution network; Calculate the voltage deviation signal of the distribution network : ； In the formula, is the real-time voltage of the distribution network, is the rated voltage of the distribution network; Calculate the current deviation signal of the distribution network : ； In the formula, is the real-time current of the distribution network, is the rated current of the distribution network, e is a natural constant; Obtain distribution network equipment status information, including distribution network transformer operating oil temperature, distribution network transformer operating winding temperature, and distribution network line insulation resistance; Based on the voltage deviation signal of the distribution network, the current deviation signal of the distribution network, the power factor of the distribution network, the operating oil temperature of the distribution network transformer, the operating winding temperature of the distribution network transformer and the insulation resistance of the distribution network line, a distribution network fault diagnosis model is constructed, and a distribution network fault diagnosis signal is output as an analysis basis for determining whether there is a fault in the distribution network; The distribution network fault diagnosis model is expressed as: ； In the formula, is the fault diagnosis signal of the distribution network. It is the electrical fault diagnosis signal of the distribution network. It is the fault diagnosis signal of distribution network equipment. is the power factor of the distribution network, yw is the operating oil temperature of the distribution network transformer, rz is the operating winding temperature of the distribution network transformer, and dz is the insulation resistance of the distribution network line.

3. A real-time dynamic simulation and analysis system for self-healing capability of distribution network according to claim 1, characterized in that: The process of the distribution network fault judgment module to judge whether there is a fault in the distribution network is as follows: Input the distribution network fault diagnosis signal into the trained random forest model; If the random forest model output result is 0, then there is no fault in the distribution network corresponding to the distribution network fault diagnosis signal; If the output result of the random forest model is 1, there is a fault in the distribution network corresponding to the distribution network fault diagnosis signal.

4. The real-time dynamic simulation and analysis system for the self-healing capability of a distribution network according to claim 1, characterized in that: The process of the communication system stability analysis module obtaining the stability factor of the distribution network communication system is as follows: Analyze the reliability of communication links in distribution networks : ； In the formula, is the normal working time of the distribution network communication link, The total working time of the communication link of the distribution network; Analyze the data transmission reliability of distribution network communication links : ； In the formula, The number of correct data transmission times of the distribution network communication link, is the total number of data transmissions in the distribution network communication link, is the data transmission delay of the jth communication link of the distribution network, j is the data transmission number of the communication link of the distribution network, j=1,2,3,..., ; Obtain the fault frequency of communication equipment in the distribution network ; Based on the working reliability of the distribution network communication link, the data transmission reliability of the distribution network communication link and the failure frequency of the distribution network communication equipment, a comprehensive analysis is performed to obtain the stability factor of the distribution network communication system, which serves as the analysis basis for determining the optimized self-healing strategy of the distribution network.

5. The real-time dynamic simulation and analysis system for the self-healing capability of a distribution network according to claim 1, characterized in that: The process of obtaining the distributed energy access factor of the distribution network by the distributed energy access analysis module is as follows: Collect the distributed energy access data of the distribution network with faults, including the distributed energy power generation per unit time, the distributed energy power factor, and the distributed energy access voltage; Based on the collected distributed energy access data of distribution networks with faults, the distributed energy access factor of the distribution network is obtained as the analysis basis for determining the optimized self-healing strategy of the distribution network.

6. The real-time dynamic simulation and analysis system for the self-healing capability of a distribution network according to claim 1, characterized in that: The process of determining the optimized self-healing strategy of the distribution network by the optimized self-healing strategy determination module is as follows: Taking the minimum distribution network restoration time as the goal, minimize the distribution network restoration time; Obtain the number of particles n stored in the database, each particle represents a possible distribution network self-healing strategy; The position vector of the particle is , where i represents the particle number, i=1,2,3,...,n; represents the value of the fault diagnosis signal of the distribution network of the i-th particle; represents the value of the stability factor of the ith particle distribution network communication system; represents the value of the distributed energy access factor of the i-th particle distribution network; The velocity vector of the particle is ; represents the velocity component of the fault diagnosis signal of the distribution network of the i-th particle; represents the velocity component of the stability factor of the ith particle distribution network communication system; represents the speed component of the distributed energy access factor of the i-th particle distribution network; Randomly initialize the particle position and speed. For each particle's initial position, obtain the corresponding distribution network recovery time as the fitness value. For each particle, compare the fitness value of its current position with the fitness value of the best position it has experienced before. If the current fitness value is better, update the individual optimal position. Among the individual optimal positions of all particles, find the position with the best fitness value as the group optimal position; When the maximum number of iterations stored in the database is reached, the optimal position of the group is output, and the distribution network self-healing strategy corresponding to the distribution network fault diagnosis signal, distribution network communication system stability factor, and distribution network distributed energy access factor contained in this position is marked as the distribution network optimized self-healing strategy.

7. The real-time dynamic simulation and analysis system for the self-healing capability of a distribution network according to claim 1, characterized in that: The process of the strategy adjustment simulation module determining the distribution network optimization self-healing strategy adjustment plan is as follows: Based on the distribution network optimization self-healing strategy, a simulation experiment is conducted on the distribution network with faults to obtain the distribution network simulation result data, including the distribution network simulation fault impact range, the distribution network simulation self-healing recovery rate, and the distribution network simulation voltage qualification rate; Based on the obtained distribution network simulation result data, a comprehensive analysis is performed to obtain the distribution network simulation result signal, which serves as the analysis basis for determining the distribution network optimization self-healing strategy adjustment plan; The distribution network simulation result signal Fz is obtained as follows: ； In the formula, Simulate the fault impact range for distribution network simulation, Simulate the self-healing recovery rate for the distribution network, is the qualified rate of voltage in distribution network simulation, and e is a natural constant; Obtain the distribution network simulation result signal-distribution network optimization self-healing strategy adjustment plan mapping table pre-stored in the database, and find the matching distribution network optimization self-healing strategy adjustment plan according to the distribution network simulation result signal by searching the mapping table.

8. A real-time dynamic simulation and analysis system for self-healing capability of distribution network according to claim 7, characterized in that: Based on the distribution network optimization self-healing strategy, a simulation experiment is conducted on the distribution network with faults to obtain the distribution network simulation result data, including the following steps: Obtain distribution network data, including line parameters, transformer parameters and load characteristic data; Determine simulation software, including MATLAB and Simulink; Build a distribution network simulation model based on the determined simulation software; Implement faults on the distribution network simulation model after determining the fault type, fault location and fault time; The distribution network optimization self-healing strategy is applied to the distribution network simulation model after the fault, the distribution network simulation model is monitored, and the distribution network simulation result data is obtained.

9. A real-time dynamic simulation and analysis system for self-healing capability of distribution network according to claim 8, characterized in that: Obtaining distribution network simulation result data includes the following steps: For distribution network simulation, the impact range of faults is as follows: On the distribution network topology diagram, determine the nodes that need to be monitored, including power supply side nodes, load side nodes, different voltage level conversion nodes and key line intermediate nodes; Based on the data acquisition function of MATLAB or Simulink, the electrical quantity data of the nodes to be monitored are stored in time series; According to the distribution network operation standards and equipment parameters, the electrical quantity threshold is set to determine whether the electrical quantity data of the node to be monitored exceeds the electrical quantity threshold; If it exceeds, the node to be monitored is used as the starting point, and the electrical quantity data of adjacent nodes are searched according to the network topology structure, and the adjacent nodes and connecting lines that exceed the electrical quantity threshold are included in the fault impact range; Mark the fault impact range on the distribution network topology diagram based on MATLAB or Simulink drawing functions or visualization software; For the distribution network simulation, the self-healing recovery rate is: In the steady-state simulation stage before the fault occurs, all load modules in the distribution network simulation model are traversed by programming, and the active power value of each load module is accumulated to obtain the total active power of all load modules in the distribution network simulation model; In the simulation phase after the distribution network optimization self-healing strategy is executed, the load module is traversed again to determine whether the load module has restored power supply by judging whether the load node voltage has returned to the normal range. For the load module that has restored power supply, its active power value is read, and the active power values ​​of all restored power loads are accumulated to obtain the total active power after restoration of the load modules that have successfully restored power supply. The ratio of the total active power after recovery to the total active power is calculated to obtain the self-healing recovery rate of the distribution network simulation; For the distribution network simulation voltage qualification rate: Determine the voltage qualified range; During the entire simulation process, the data recording function of the simulation software is used to continuously monitor the voltage of each node that needs to be monitored in the distribution network; For each node to be monitored, the time during which its voltage is within the qualified range is counted, and the proportion of the time during which the voltage of each node to be monitored is within the qualified range to the total simulation time is calculated; Obtain the weight of each node to be monitored, perform weighted summation on the weight and proportion, and obtain the qualified rate of the simulated voltage of the distribution network.

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