Distribution Network Fault Recovery Method and System Based on Reconstruction and Island Collaborative Optimization

Abstract

The present invention relates to the technical field of fault recovery, and particularly to a distribution network fault recovery method and system based on collaborative optimization of reconstruction and islanding, including classifying distributed power sources according to the fault response characteristics of distributed power sources in the distribution network to obtain distributed power source classification data; grading and controllability modeling of the distribution network loads according to the power supply continuity and controllability of the distribution network loads to obtain a load characteristic matrix; obtaining a distribution network reconstruction operation state classification diagram according to the real-time topological connection state of the distribution network and the distributed power source classification data, and constructing a multi-objective collaborative optimization model of reconstruction and islanding with the minimization of the total amount of lost load, system voltage deviation, and the number of switch operations as the optimization objectives; using a mixed integer programming algorithm to obtain an optimal fault recovery action strategy to perform fault recovery on the distribution network. The present invention realizes the efficient reconstruction and island division of the distribution network in case of faults, and improves the fault recovery efficiency and power supply reliability of the distribution network.

Description

Technical Field

[0001] The present invention relates to the technical field of fault recovery, and particularly to a distribution network fault recovery method and system based on collaborative optimization of reconstruction and islanding. Background Art

[0002] With the continuous development of the new power system, the access ratio of distributed generation (DG) in the distribution network has increased significantly, providing more optimization space for the fault recovery strategy of the distribution network. Especially for the network-forming distributed generation with black-start capability, it can quickly supply independent power to the surrounding area during a sudden fault, forming an island operation state, thus effectively reducing the load shedding situation and ensuring the reliability of the distribution network power supply. At the same time, the distribution network reconstruction strategy changes the network topology by flexibly adjusting the opening and closing states of the tie lines, thereby optimizing the power flow distribution and realizing the rapid restoration of power to the non-fault area. However, although both reconstruction and islanding division are effective strategies for fault recovery, most of the existing technologies optimize them independently and fail to fully integrate their advantages, which is particularly limited in the face of complex faults.

[0003] The existing reconstruction methods mainly focus on global optimization, that is, maximizing the power supply restoration range by changing the topology of the power grid. The optimization process of this method often ignores the power balance and stability requirements in local areas. In contrast, islanding division is a local optimization strategy, which focuses on ensuring the power supply-demand balance and system stability within the island area. When reconstruction and islanding division are executed independently, conflicts between global optimization and local optimization are likely to occur. This conflict not only reduces the overall effect of fault recovery but also leads to resource waste of distributed generation and unbalanced load problems, making it difficult to achieve the overall optimum. In addition, in the process of fault recovery, the existing technologies often fail to fully consider the reasonable utilization of distributed generation, resulting in unbalanced resource allocation. For example, some distributed generations may be over-relied on due to the islanding division strategy, while other power sources are idle. This unbalanced resource allocation not only reduces the operation efficiency of the power grid but also poses risks of overload or underload in local areas, threatening the stable operation of the power grid.

[0004] In summary, there are obvious deficiencies in the current distribution network fault recovery strategy in the collaborative optimization of reconstruction and islanding division. There is an urgent need for a fault recovery method that can effectively combine the advantages of reconstruction and islanding division to comprehensively improve the recovery ability and power supply stability of the distribution network under complex fault conditions. Summary of the Invention

[0005] To solve the above technical problems, the present invention provides a distribution network fault recovery method and system based on collaborative optimization of reconstruction and islanding.

[0006] In a first aspect, the present invention provides a distribution network fault recovery method based on collaborative optimization of reconstruction and islanding, and the method includes the following steps:

[0007] Classify the distributed power sources in the distribution network according to the fault response characteristics of the distributed power sources to obtain distributed power source classification data; the distributed power source classification data includes black start power source data and non-black start power source data;

[0008] Classify and model the controllability of the distribution network loads according to the power supply continuity and controllability of the distribution network loads to obtain a load characteristic matrix;

[0009] Reconstruct the distribution network according to the real-time topological connection state of the distribution network and the distributed power source classification data, and divide the operating state of the reconstructed distribution network to obtain a classification diagram of the reconstructed distribution network operating state;

[0010] According to the classification diagram of the reconstructed distribution network operating state and the load characteristic matrix, with the minimization of the total load loss, the minimization of the system voltage deviation, and the minimization of the number of switch operations as the optimization objectives, construct a multi-objective collaborative optimization model for reconstruction and islanding;

[0011] According to the real-time fault information of the distribution network, use the mixed integer programming algorithm to solve the multi-objective collaborative optimization model for reconstruction and islanding to obtain an optimal fault recovery action strategy, and perform fault recovery on the distribution network according to the optimal fault recovery action strategy.

[0012] In a further embodiment, the step of classifying the distributed power sources in the distribution network according to the fault response characteristics of the distributed power sources to obtain distributed power source classification data includes:

[0013] Perform fault simulation on the distributed power sources in the distribution network to obtain fault response data of the distributed power sources during the fault;

[0014] According to the fault response data, use the principal component analysis method to extract the fault response characteristics of each distributed power source; the fault response characteristics include a black start identification bit and an island voltage frequency support ability parameter;

[0015] According to the fault response characteristics, divide the distributed power sources into black start power sources and non-black start power sources.

[0016] In a further embodiment, the step of classifying and modeling the controllability of the distribution network loads according to the power supply continuity and controllability of the distribution network loads to obtain a load characteristic matrix includes:

[0017] According to the real-time load data of the distribution network, the entropy weight method is used to evaluate and obtain the load power supply continuity level, and based on the load power supply continuity level, the loads of the distribution network are classified to obtain loads at each supply level;

[0018] A continuous variable model is used to model the load controllability data of loads at each supply level to obtain a controllable load model for loads at each supply level;

[0019] According to the change amount of the load power demand of loads at each supply level and the controllable load model, the corresponding maximum load regulation ratio and the minimum load power demand are obtained;

[0020] Based on the maximum load regulation ratio and the minimum load power demand, the load controllability flag bits of loads at each supply level are identified;

[0021] The load controllability flag bits of loads at each supply level are integrated to construct a load feature vector for each load, and the load feature vectors of all loads are arranged in sequence to generate a load feature matrix.

[0022] In a further implementation, the load feature vector includes a load number, a load name, a load type, a load power supply continuity level, and a load controllability flag bit.

[0023] In a further implementation, the steps of reconstructing the distribution network according to the real-time topological connection state of the distribution network and the distributed power source classification data, and dividing the operating state of the reconstructed distribution network to obtain a classification diagram of the reconstructed operating state of the distribution network include:

[0024] An adjacency matrix of the distribution network is constructed according to the real-time topological connection state of the distribution network, and the adjacency matrix is traversed using a breadth-first search algorithm to analyze the connectivity state between each distributed power source and the main power grid to obtain the distributed power source connectivity;

[0025] According to the distributed power source connectivity, the distribution network switch status quantity, and the fault isolation section information, a topological reconstruction candidate scheme is generated;

[0026] According to the reconstruction tie switch operation sequence in the topological reconstruction candidate scheme, the topological structure change of the distribution network is analyzed to determine the island division range after the distribution network reconstruction;

[0027] According to the distributed power source classification data, the distribution position of the black start power source is determined;

[0028] Taking the distribution location of the black start power supply as the clustering center, combining the island division range, and using a clustering algorithm to dynamically divide the distribution network into different island operation areas, generating a classification map of the reconstructed operation states of the distribution network; wherein, the classification map of the reconstructed operation states of the distribution network at least includes the island operation state of a single distributed power source, the island operation state of multiple distributed power sources, and the main network combined power supply operation state.

[0029] In a further embodiment, the step of generating a topological reconstruction candidate scheme according to the distributed power source connectivity, the switch status quantity of the distribution network, and the fault isolation section information includes:

[0030] Screening out the set of distributed power source nodes disconnected from the main network according to the distributed power source connectivity to form a first set of distributed power source nodes;

[0031] Extracting the distributed power source nodes directly connected to the fault section according to the fault isolation section information to form a second set of distributed power source nodes;

[0032] Merging the first set of distributed power source nodes and the second set of distributed power source nodes to obtain a candidate reconstruction area;

[0033] Traversing all tie switches in the candidate reconstruction area according to the switch status quantity of the distribution network to generate a set of switch operation status combinations;

[0034] Updating the adjacency matrix according to the set of switch operation status combinations to obtain an updated adjacency matrix corresponding to each switch operation status combination;

[0035] Based on the updated adjacency matrix, screening out the switch operation status combinations that meet the effective reconstruction of the distribution network from the set of switch operation status combinations to form a reconstructed tie switch operation sequence;

[0036] Based on the reconstructed tie switch operation sequence, forming a topological reconstruction candidate scheme.

[0037] In a further embodiment, the construction process of the reconstruction and island multi-objective collaborative optimization model includes:

[0038] According to the load characteristic matrix, obtaining the active power demand of the load, the controllable load power supply ratio, and the uncontrollable load power supply status;

[0039] Determining the load power supply weights according to the load power supply continuity level in the load characteristic matrix, where the load power supply weights include the controllable load power supply weight and the uncontrollable load power supply weight;

[0040] Calculating the total load shedding amount by using the load power supply weights according to the active power demand of the load, the controllable load power supply ratio, and the uncontrollable load power supply status;

[0041] Analyze the classified diagram of the reconstructed operating state of the distribution network to obtain the load node voltage data and tie switch status data under different operating states;

[0042] Calculate the system voltage deviation based on the difference sum between the load node voltage data and the reference voltage, and calculate the number of switch operations according to the tie switch status data;

[0043] Construct a multi-objective collaborative optimization model for reconstruction and islanding by minimizing the total load loss, the system voltage deviation, and the number of switch operations.

[0044] In a further embodiment, the constraint conditions of the multi-objective collaborative optimization model for reconstruction and islanding include network topology constraints, power flow constraints, distributed power generation output constraints, load node voltage constraints, and line current constraints.

[0045] In a further embodiment, the optimal fault recovery action strategy includes optimal tie switch opening and closing actions, optimal power output of the power source, optimal load on / off instructions, and optimal controllable load power adjustment instructions.

[0046] In a second aspect, the present invention provides a distribution network fault recovery system based on collaborative optimization of reconstruction and islanding, and the system includes:

[0047] A power source classification module for classifying distributed power sources according to the fault response characteristics of distributed power sources in the distribution network to obtain distributed power source classification data; the distributed power source classification data includes black start power source data and non-black start power source data;

[0048] A load analysis module for grading and controllability modeling of the distribution network load according to the power supply continuity and controllability of the distribution network load to obtain a load characteristic matrix;

[0049] A reconstruction analysis module for reconstructing the distribution network according to the real-time topological connection state of the distribution network and the distributed power source classification data, and dividing the operating state of the reconstructed distribution network to obtain a classified diagram of the reconstructed operating state of the distribution network;

[0050] A model construction module for constructing a multi-objective collaborative optimization model for reconstruction and islanding with the minimum total load loss, minimum system voltage deviation, and minimum number of switch operations as the optimization objectives according to the classified diagram of the reconstructed operating state of the distribution network and the load characteristic matrix;

[0051] A fault recovery module, which is used to solve the reconstruction and islanding multi-objective collaborative optimization model according to the real-time fault information of the distribution network by using a mixed-integer programming algorithm, obtain an optimal fault recovery action strategy, and perform fault recovery on the distribution network according to the optimal fault recovery action strategy.

[0052] The present invention provides a distribution network fault recovery method and system based on reconstruction and islanding collaborative optimization. The method classifies distributed power sources according to the fault response characteristics of distributed power sources in the distribution network to obtain distributed power source classification data; classifies and models the controllability of distribution network loads according to the power supply continuity and controllability of distribution network loads to obtain a load characteristic matrix; reconstructs the distribution network according to the real-time topological connection state and distributed power source classification data of the distribution network, and divides the operation state of the reconstructed distribution network to obtain a distribution network reconstruction operation state classification diagram; constructs a reconstruction and islanding multi-objective collaborative optimization model with the minimization of the total load loss, the minimization of the system voltage deviation, and the minimization of the number of switch operations as optimization objectives; uses a mixed-integer programming algorithm to solve the reconstruction and islanding multi-objective collaborative optimization model to obtain an optimal fault recovery action strategy, and performs fault recovery on the distribution network according to the optimal fault recovery action strategy. Compared with the prior art, by comprehensively considering the fault response characteristics of distributed power sources, etc., the method realizes the efficient reconstruction and islanding division of the distribution network in case of faults, improves the efficiency of fault recovery and the reliability and stability of power supply, optimizes resource allocation at the same time, and avoids the waste of distributed power source resources. Brief Description of the Drawings

[0053] Figure 1 is a schematic flowchart of a distribution network fault recovery method based on reconstruction and islanding collaborative optimization provided by an embodiment of the present invention;

[0054] Figure 2 is a schematic diagram of an example of the distribution network structure provided by an embodiment of the present invention;

[0055] Figure 3 is a schematic diagram of the operation state of the distribution network after fault recovery provided by an embodiment of the present invention;

[0056] Figure 4 is a block diagram of a distribution network fault recovery system based on reconstruction and islanding collaborative optimization provided by an embodiment of the present invention. Detailed Embodiments

[0057] The following specifically illustrates the embodiments of the present invention in conjunction with the drawings. The given embodiments are only for illustrative purposes and should not be construed as limiting the present invention. The drawings are only for reference and illustration and do not constitute a limitation on the scope of patent protection of the present invention, because many changes can be made to the present invention without departing from the spirit and scope of the present invention.

[0058] Reference Figure 1 , an embodiment of the present invention provides a distribution network fault recovery method based on collaborative optimization of reconstruction and islanding, as Figure 1 shown, the method includes the following steps:

[0059] S1. Classify the distributed power sources in the distribution network according to the fault response characteristics of the distributed power sources to obtain distributed power source classification data; the distributed power source classification data includes black start power source data and non-black start power source data.

[0060] In some embodiments, the step of classifying the distributed power sources in the distribution network according to the fault response characteristics of the distributed power sources to obtain distributed power source classification data includes:

[0061] Conduct a fault simulation on the distributed power sources in the distribution network to obtain the fault response data of the distributed power sources during the fault;

[0062] According to the fault response data, use the principal component analysis method to extract the fault response characteristics of each distributed power source; the fault response characteristics include a black start identification bit and an island voltage frequency support ability parameter;

[0063] According to the fault response characteristics, divide the distributed power sources into black start power sources and non-black start power sources.

[0064] Specifically, in this embodiment, according to the actual topological structure, line parameters and distributed power source types of the distribution network, a corresponding simulation environment is established using power system simulation software. Different types of fault scenarios are set in the simulation environment, such as line short circuits, equipment failures, etc., and the impacts of these faults on the distribution network are simulated. During the fault simulation process, this embodiment records in real time the changes in electrical parameters of the distributed power sources during the fault. The electrical parameters can include voltage, current, frequency, and output power, etc. The fault response data of the distributed power sources during the fault is collected from the simulation results. The fault response data includes parameter comparisons before and after the fault, fault duration, recovery time, etc. Then, preprocessing operations such as denoising and standardization are performed on the collected fault response data to improve the accuracy of data analysis. This embodiment uses the principal component analysis method to perform dimensionality reduction processing on the preprocessed data, extracts the main characteristics that can reflect the fault response characteristics of the distributed power sources, and according to the results of the principal component analysis, extracts the fault response characteristics of each distributed power source. The fault response characteristics include a black start identification bit and an island voltage frequency support ability parameter. Among them, the black start identification bit is used to identify whether the distributed power source has the black start ability, and the island voltage frequency support ability parameter is used to evaluate the voltage and frequency support ability of the distributed power source during island operation.

[0065] In this embodiment, by analyzing the autonomous start-up ability of distributed power sources after a fault occurs and their support ability for island operation, it is determined whether they have the black start function, which is represented by a binary flag bit (0 or 1), where 1 indicates the ability to perform black start and 0 indicates the lack thereof; the parameters of the island voltage and frequency support ability can be determined by analyzing the adjustment ability of distributed power sources to the voltage and frequency within the island during island operation after a fault (i.e., independent operation after disconnecting from the main grid of the distribution network). For example, distributed power source A can quickly recover and provide stable voltage and frequency support during a fault. Therefore, in this embodiment, the black start flag bit of distributed power source A is set to 1 (indicating the ability to perform black start), and the parameters of the island voltage and frequency support ability of distributed power source A are all higher than the threshold. Thus, distributed power source A is classified as a black start power source; while the recovery speed of distributed power source B is slower during a fault and it requires external power support to reconnect to the grid for power generation. Therefore, the black start flag bit of distributed power source B is set to 0 (indicating the lack of the ability to perform black start), and the parameters of the island voltage and frequency support ability of distributed power source C are lower than the threshold. Thus, distributed power sources B and C are classified as non-black start power sources. In this embodiment, according to the value of the black start flag bit (1 or 0) and the parameters of the island voltage and frequency support ability, distributed power sources are classified into black start power sources and non-black start power sources. A black start power source refers to a distributed power source that has the ability to perform black start and whose parameters of the island voltage and frequency support ability are higher than the frequency support threshold; a non-black start power source refers to a distributed power source that does not have the ability to perform black start or whose parameters of the island voltage and frequency support ability are lower than the frequency support threshold.

[0066] S2. Classify and model the controllability of the distribution network load according to the power supply continuity and controllability of the distribution network load to obtain a load characteristic matrix.

[0067] In some embodiments, the step of classifying and modeling the controllability of the distribution network load according to the power supply continuity and controllability of the distribution network load to obtain a load characteristic matrix includes:

[0068] According to the real-time load data of the distribution network, use the entropy weight method to evaluate and obtain the power supply continuity level of the load, and classify the load of the distribution network based on the power supply continuity level of the load to obtain power supply loads at each level;

[0069] Use a continuous variable model to model the load controllability data of power supply loads at each level to obtain a controllable load model for power supply loads at each level;

[0070] According to the change amount of the load power demand of power supply loads at each level and the controllable load model, obtain the corresponding maximum load regulation ratio and the minimum load power demand;

[0071] Based on the maximum load regulation ratio and the minimum load power demand, identify the load controllability flag bits of power supply loads at each level;

[0072] Integrate the load controllability flag bits of each level of power supply load, construct a load feature vector for each load, and arrange the load feature vectors of all loads in sequence to generate a load feature matrix; wherein, the load feature vector includes a load number, a load name, a load type, a load power supply continuity level, and a load controllability flag bit.

[0073] Specifically, in this embodiment, real-time load data of each load in the distribution network at different time points is obtained from the smart meters of the distribution network. The real-time load data includes parameters such as the power demand, current, and voltage of the load. Key factors affecting the load power supply continuity are selected according to historical data, such as load type, power consumption period, geographical location, etc. Then, the entropy weight method is used to calculate the weights of each key factor. The entropy weight method determines its weight by calculating the information entropy of each factor. The smaller the information entropy, the greater the weight, indicating that the key factor has a greater impact on the load power supply continuity. According to the weights and real-time load data, the power supply continuity level score of each load is calculated, and the loads are divided into different levels according to the scores. For example, the loads are divided into first-level loads, second-level loads, and third-level loads. Among them, the first-level loads have the highest score and the highest requirement for power supply continuity, usually including key loads such as hospitals and fire protection facilities; the second-level loads have the second-highest score and a relatively high requirement for power supply continuity, such as commercial areas and schools; the third-level loads have the lowest score and the lowest requirement for power supply continuity, such as ordinary residential electricity. In this embodiment, a corresponding level label is assigned to each load, such as first-level load, second-level load, or third-level load.

[0074] In this embodiment, controllability data of each level of load is obtained. The controllability data may include load adjustment records, response time, adjustable ratio, etc., and the data is cleaned and standardized to ensure data consistency. For each level of load, a linear regression model is selected as the continuous variable model for modeling in this embodiment. This continuous variable model can describe the ability of the load to reduce power demand by a certain proportion after a fault occurs. In this embodiment, the load controllability data is used as the input of the continuous variable model, and the load adjustment ability is used as the output of the continuous variable model to train the model. After a fault occurs, according to the operating state of the distribution network and the real-time data of the load, this embodiment calculates the change in power demand of each level of load, that is, the power fluctuation range of the load in different time periods, and inputs the change in load power demand into the controllable load model of each level of load to calculate the maximum power ratio that each load can adjust after a fault occurs and the minimum power demand of each load after adjustment (the minimum power demand of the load). Among them, the maximum adjustment ratio represents the maximum power ratio that the load can adjust on the premise of meeting the requirements of power supply continuity; the minimum power demand represents the minimum power required by the load on the premise of meeting the basic power supply requirements. Then, this embodiment sets the load controllability flag according to its load maximum adjustment ratio and load minimum power demand. For example, if the maximum adjustment ratio of the load is greater than the adjustment ratio threshold and the minimum power demand is less than the power demand threshold, the flag is set to controllable; otherwise, it is set to uncontrollable. This embodiment constructs a load feature vector for each load. The load feature vector includes the load number, load name, load type (such as industrial, commercial, residential, etc.), load power supply continuity level (such as first level, second level, third level), and load controllability flag. The load feature vectors of all loads are arranged in order to generate a load feature matrix, which can comprehensively reflect the power supply continuity and controllability characteristics of each load in the distribution network and provide an important basis for subsequent optimization calculations and fault recovery strategies.

[0075] S3. Reconstruct the distribution network according to the real-time topological connection state of the distribution network and the distributed power source classification data, and divide the operating state of the reconstructed distribution network to obtain a classification diagram of the reconstructed operating state of the distribution network.

[0076] In some embodiments, the step of reconstructing the distribution network according to the real-time topological connection state of the distribution network and the distributed power source classification data, and dividing the operating state of the reconstructed distribution network to obtain a classification diagram of the reconstructed operating state of the distribution network includes:

[0077] Construct an adjacency matrix of the distribution network according to the real-time topological connection state of the distribution network, and traverse the adjacency matrix using the breadth-first search algorithm to analyze the connectivity state between each distributed power source and the main grid to obtain the distributed power source connectivity;

[0078] Generate a candidate topology reconstruction plan based on the distributed power connectivity, distribution network switch status variables, and fault isolation section information;

[0079] Analyze the changes in the distribution network topology structure according to the reconstruction tie switch operation sequence in the candidate topology reconstruction plan, and determine the island division range after the distribution network reconstruction;

[0080] Determine the distribution locations of the black start power sources according to the distributed power classification data;

[0081] Take the distribution locations of the black start power sources as the clustering centers, combine with the island division range, and use the clustering algorithm to dynamically divide the distribution network into different island operation areas to generate a classification map of the reconstructed operation states of the distribution network; wherein, the classification map of the reconstructed operation states of the distribution network includes at least the single distributed power island operation state, the multi-distributed power island operation state, and the main grid joint power supply operation state.

[0082] Specifically, in this embodiment, the real-time topology connection status of the distribution network is obtained. The real-time topology connection status includes line connection relationships and switch states, etc. According to the collected real-time topology connection status, an adjacency matrix of the distribution network is constructed. The adjacency matrix is used to represent the connection relationships between the nodes in the distribution network. The breadth-first search algorithm (BFS) is used to traverse the adjacency matrix. The breadth-first search algorithm starts from the main grid node and expands layer by layer outward until all nodes are traversed. During the traversal process, the connection status between each distributed power source and the main grid is recorded. If a distributed power source can be connected to the main grid through a series of switches and lines, it is considered to be connected; otherwise, it is considered to be isolated. Then, in this embodiment, according to the distributed power connectivity, distribution network switch status variables, and fault isolation section information, the switch operations that can change the distribution network topology structure are determined to generate all switch operation sequences. Each sequence represents a potential topology reconstruction plan. The feasibility and effectiveness of each plan are evaluated, such as checking whether safety constraints are met and whether the power outage range is reduced, etc., and the candidate plans that meet the conditions are selected.

[0083] For each topology reconstruction candidate, this embodiment analyzes the impact of its reconstructed tie-switch operation sequence on the topology of the distribution network. According to the topology after switch operation, the island division range is determined through connected component analysis. An island refers to a distributed power source and load area separated from the main power grid due to switch operation. Each island area is numbered and marked for subsequent analysis and management. According to the distributed power source classification data, the distributed power sources that can be used as black-start power sources are determined. A black-start power source is a power source that can start first and restore power supply after a system power outage. In this embodiment, the distribution location of the black-start power sources is used as the clustering center, and in combination with the island division range, the K-means clustering algorithm is used to dynamically divide the distribution network into different island operation areas, generating a classification map of the reconstructed operation states of the distribution network. The classification map of the reconstructed operation states of the distribution network at least includes the single-distributed-power-source island operation state, the multi-distributed-power-source island operation state, and the main grid combined power supply operation state.

[0084] In some embodiments, the step of generating a topology reconstruction candidate according to the distributed power source connectivity, the distribution network switch status quantity, and the fault isolation section information includes:

[0085] Filter out the set of distributed power source nodes disconnected from the main grid according to the distributed power source connectivity to form a first set of distributed power source nodes;

[0086] Extract the distributed power source nodes directly connected to the fault section according to the fault isolation section information to form a second set of distributed power source nodes;

[0087] Merge the first set of distributed power source nodes and the second set of distributed power source nodes to obtain a candidate reconstruction area;

[0088] Traverse all tie switches in the candidate reconstruction area according to the distribution network switch status quantity to generate a set of switch operation status combinations;

[0089] Update the adjacency matrix according to the set of switch operation status combinations to obtain the updated adjacency matrix corresponding to each switch operation status combination;

[0090] Based on the updated adjacency matrix, filter out the switch operation status combinations that meet the effective reconstruction of the distribution network from the set of switch operation status combinations to form a reconstructed tie-switch operation sequence;

[0091] Based on the reconstructed tie-switch operation sequence, form a topology reconstruction candidate.

[0092] Specifically, this embodiment collects the access point information of all distributed generation (DG) and its connection status with the main grid, determines the distributed generation disconnected from the main grid by judging whether there is a path between each distributed generation DG node and the main grid, and forms the first distributed generation node set with all distributed generation DG nodes disconnected from the main grid. Then, the fault isolation section information is obtained. The fault isolation section information refers to the area isolated in the distribution network to prevent the spread of faults after a fault occurs. This embodiment determines the section where the fault occurs according to the fault isolation section information, and searches for the distributed generation nodes directly connected to the fault section. These distributed generation nodes directly connected to the fault section may be affected by the fault. Therefore, this embodiment forms the second distributed generation node set with these affected distributed generation stages, and performs a union operation on the first distributed generation node set and the second distributed generation node set to obtain a set containing all distributed generation nodes that may participate in the reconstruction. According to the positions of these distributed generation nodes, the smallest distribution network area containing these nodes is determined as the candidate reconstruction area.

[0093] Next, this embodiment reads the current states (open / closed) of all tie switches in the candidate reconstruction area, generates all possible switch operation state combinations by traversing each tie switch. For example, if there are 3 tie switches, there are 8 possible combinations (each switch has two states). At the same time, the adjacency matrix is initialized according to the original structure of the distribution network. The adjacency matrix represents the connection relationship between nodes. For each switch operation state combination, this embodiment updates the corresponding elements in the adjacency matrix according to the closed or open state of the switch. For each updated adjacency matrix, this embodiment can evaluate the difference degree between the two matrices by calculating the Frobenius norm of the updated adjacency matrix and the pre-fault adjacency matrix. Starting from the tie switch near the fault point, the matrix difference increment update algorithm is used to traverse and update the adjacency matrix to quickly find the switch operation state combination that meets the conditions. A hierarchical verification mechanism is adopted to process dynamic constraints, such as topological connectivity, radial constraint, critical load path retention, etc. The above constraints are verified in turn, and the switch operation state combination that meets all constraints is the valid reconstruction switch operation state combination of the distribution network. The selected switch operation state combinations that meet the conditions are sorted in ascending order of the Frobenius norm to form a reconstructed tie switch operation sequence, and thus a topological reconstruction candidate scheme that meets the valid reconstruction of the distribution network is generated according to the reconstructed tie switch operation sequence. The topological reconstruction candidate scheme includes switch operations and the corresponding distribution network topology structure, which is used to restore the power supply of the distribution network after the fault and provide strong support for the rapid recovery after the fault.

[0094] S4. Based on the classified operation state diagram of the distribution network reconstruction and the load characteristic matrix, with the minimization of the total load loss, the minimization of the system voltage deviation, and the minimization of the number of switch operations as the optimization objectives, a multi-objective collaborative optimization model for reconstruction and islanding is constructed.

[0095] In some embodiments, the construction process of the multi-objective collaborative optimization model for reconstruction and islanding includes:

[0096] Based on the load characteristic matrix, obtain the active power demand of the load, the power supply ratio of the controllable load, and the power supply status of the uncontrollable load;

[0097] Based on the load power supply continuity level in the load characteristic matrix, determine the load power supply weights, where the load power supply weights include the controllable load power supply weight and the uncontrollable load power supply weight;

[0098] Based on the active power demand of the load, the power supply ratio of the controllable load, and the power supply status of the uncontrollable load, calculate the total load loss using the load power supply weights.

[0099] Analyze the classified operation state diagram of the distribution network reconstruction to obtain the load node voltage data and the tie switch status data under different operation states;

[0100] Based on the difference sum between the load node voltage data and the reference voltage, calculate the system voltage deviation amount, and calculate the number of switch operations based on the tie switch status data;

[0101] By minimizing the total load loss, the system voltage deviation amount, and the number of switch operations, a multi-objective collaborative optimization model for reconstruction and islanding is constructed.

[0102] Specifically, in this embodiment, traverse the load characteristic matrix, extract the active power demand of each load node from the load characteristic matrix, and at the same time identify the entries marked as controllable loads in the load characteristic matrix, calculate the proportion of them in the total load to obtain the power supply ratio of the controllable load. The controllable load refers to the part of the load that can be adjusted in power supply through dispatching or control means. For the uncontrollable load, check its status mark to confirm whether it is in the power supply state to obtain the power supply status of the uncontrollable load, and assign different power supply weights to the controllable and uncontrollable loads according to the load power supply continuity level. In this embodiment, the weight of the first-level load is set to be the highest, the second-level load is the second, and the third-level load is the lowest. Then, for each load node, in this embodiment, according to the power supply status of the controllable and uncontrollable loads, calculate the difference between the actual power supply and the demand power, multiply the difference by the power supply weight of this load to obtain the weighted load loss amount, sum up the weighted load loss amounts of all load nodes to obtain the total load loss sub-objective function. The mathematical expression of the total load loss sub-objective function is:

[0103]

[0104] In the formula, is the total load shedding quantum objective function; is the set of controllable loads; is the active power demand of load i; is the power supply ratio of controllable load i; is the power supply status of uncontrollable load j, 1 means power supply, 0 means cut-off; is the importance level of load i, the larger it is, the more important the load is; is the importance level of load j; is the active power demand of load j.

[0105] Meanwhile, in this embodiment, data parsing technology is used to read the voltage and switch status information in the reconstructed operation status classification diagram, and the information is sorted into structured data for subsequent calculation. Then, for the voltage data of each load node in this embodiment, the difference between it and the reference voltage is calculated, and the sum is obtained to get the system voltage deviation quantum objective function. The mathematical expression of the system voltage deviation quantum objective function is:

[0106]

[0107] In the formula, is the system voltage deviation quantum objective function; is the set of all load nodes in the system; is the reference voltage; is the voltage of load node i.

[0108] Meanwhile, in this embodiment, the status data of the tie switches are traversed, and the sub-objective function of the number of switch operations is calculated according to the status changes. The sub-objective function of the number of switch operations is specifically:

[0109]

[0110] In the formula, is the sub-objective function of the number of switch operations; is the set of tie switches; is the initial state of tie switch n before failure, 1 means closed, 0 means open; is the optimized state of tie switch n, 1 means closed, 0 means open.

[0111] Therefore, the reconstruction and island multi-objective collaborative optimization model in this embodiment includes three sub-objective functions, which include the total load shedding minimized, the system voltage deviation, and the number of switching operations. To ensure the comprehensive performance of the model, each of the above sub-objective functions is normalized in this embodiment, and a unified total objective function is obtained through weighted summation. The total objective function comprehensively reflects the importance of each sub-objective. The mathematical expression of the total objective function is:

[0112]

[0113] In the formula, is the total objective function; is the m-th sub-objective function; is the weight of the m-th sub-objective function; is the optimal value of the m-th sub-objective function.

[0114] Finally, this embodiment defines the constraint conditions. The constraint conditions of the reconstruction and island multi-objective collaborative optimization model include network topology constraints, power flow constraints, distributed power generation output constraints, load node voltage constraints, and line current constraints. Among them, the network topology constraints consider the situation of island integration, which mainly include the radial and connectivity constraints of the distribution network network topology. The network topology constraints are specifically:

[0115]

[0116]

[0117]

[0118]

[0119]

[0120] In the formula, C is a 0-1 matrix of distributed power generations or the main grid that can be used as the source nodes for island division, where the distributed power generations that meet the conditions are the classified black-start power sources; indicates the existence of a partition area with the node where the distributed power generation q is located as the root node; is the sum of the number of black-start power sources and the number of main grid substations; A is a 0-1 matrix of the island partition areas to which the nodes belong; means that node i is a node within the island partition area q; is the sum of the number of black-start power sources and the number of island partition areas; is the on-off variable of the line switch; the latter four expressions respectively indicate that each load node of the distribution network belongs to and only belongs to one partition area; there is no isolated node in the partition nodes; the regional dominant node must be within its own partition area; in the island division of the power grid, the number of nodes minus the number of partition areas should be equal to the number of operating branches.

[0121] To ensure the connectivity within each partition area, a connectivity constraint based on virtual power flow is established, and the expression is as follows:

[0122]

[0123] In the formula, is the virtual output of the power source at the root node of the island partition area q; is the virtual power of the line within the partition area q with i as the starting node and j as the ending node; is the virtual load at node k; is the maximum value of the virtual output; is the maximum value of the virtual load; is the binary variable indicating whether node i belongs to island area j; is the virtual power of the line from node j to node k within the partition area q; is the reactive power of node j.

[0124] The power flow constraint is specifically:

[0125]

[0126] In the formula, is the on-off variable of the line switch; and are the active and reactive powers of the power source at load node j; and are the active and reactive powers of the load at load node j respectively; , , and are the active power outputs of the main grid, distributed power source, energy storage discharge, and energy storage charging at load node j respectively; and are the reactive power outputs of the main grid and distributed power source at load node i respectively; and are the sets of the parent node and child nodes of load node j; is the active power of the line with i as the starting node and j as the ending node; is the reactive power of the line with i as the starting node and j as the ending node; is the transmission current of line ij; is the resistance of line ij; is the reactance of line ij; is the active power flow from load node i to load node k; is the reactance of line ij; is the reactive power flow from load node i to load node k; is the voltage of load node j; is the voltage of load node i.

[0127] The constraints on the output of distributed power sources are specifically as follows:

[0128]

[0129] In the formula, is the lower limit of the active power of the distributed power source; is the active power of the distributed power source; is the upper limit of the active power of the distributed power source; is the reactive power of the distributed power source; is the power factor angle.

[0130] The constraints on the load node voltage and line current are specifically as follows:

[0131]

[0132] In the formula, is the lower limit of the voltage at load node i; is the upper limit of the voltage at load node i; is the voltage at load node i; is the maximum allowable transmission current on line ij.

[0133] Through the above steps, this embodiment can construct a multi-objective collaborative optimization model for reconstruction and islanding that comprehensively considers the total amount of load loss, system voltage deviation, and the number of switching operations, providing decision support for the optimal operation of the distribution network.

[0134] S5. According to the real-time fault information of the distribution network, use the mixed-integer programming algorithm to solve the multi-objective collaborative optimization model for reconstruction and islanding, obtain the optimal fault recovery action strategy, and perform fault recovery on the distribution network according to the optimal fault recovery action strategy.

[0135] In this embodiment, the hybrid integer programming algorithm is used to solve the reconstruction and islanding multi-objective collaborative optimization model, and the optimal fault recovery action strategy is obtained. The optimal fault recovery action strategy includes key information such as the optimal opening and closing actions of tie switches, the optimal power output of power sources, the optimal load on / off instructions, and the optimal controllable load power adjustment instructions. According to the optimal fault recovery action strategy, this embodiment can timely issue corresponding control instructions to quickly realize the reconstruction and islanding division of the distribution network when a fault occurs. During the fault recovery process, the problem of utilizing distributed power resources is often faced. By constructing a unified mathematical model, this embodiment reasonably schedules key resources such as distributed power sources during the fault recovery stage. This strategy effectively avoids the problems of load imbalance and resource idleness that may occur in traditional methods, thus ensuring the efficient and stable operation of the power supply network and greatly improving the overall resource utilization efficiency of the power system. At the same time, with the help of the collaborative optimization strategy, this embodiment can ensure the stable operation of the islanded area and quickly restore the power supply capacity of the main network in complex fault situations, especially for the power supply guarantee of important loads. This dual optimization mechanism significantly improves the power supply reliability of the entire distribution network, effectively shortens the power outage time, and greatly reduces the impact range of faults. Moreover, in the face of complex scenarios, especially when there are multiple fault points, the mathematical model based on the collaborative optimization of reconstruction and islanding shows extremely high flexibility in dealing with multiple fault points and can achieve comprehensive optimization of the global and local areas. Compared with traditional methods, this embodiment can quickly make recovery decisions in the face of a multi-fault environment, significantly shortening the fault recovery time, thus greatly improving the fault handling efficiency of the distribution network in complex scenarios.

[0136] This embodiment is verified by taking the unified recovery strategy of reconstruction and islanding division after a line fault occurs in the IEEE 33-node distribution network system as an example. The structure of the IEEE 33-node distribution network system is as Figure 2 shown, in Figure 2In this embodiment, the nodes in the distribution network are labeled with serial numbers from 1 to 33. Each node can be the location of a substation, distributed power source, or switch. Among them, L1 to L36 represent the lines in the distribution network. Each line connects two nodes and is used to transmit electric power. For example, L1 connects node 1 and node 2, L2 connects node 2 and node 3, and so on. At the same time, DG represents distributed power sources. The distributed power sources are connected to the following nodes respectively: A distributed power source with a capacity of 550 kW is connected to node 5 and has the black start ability; A distributed power source with a capacity of 520 kW is connected to node 14 and also has the black start ability; A distributed power source with a capacity of 800 kW is connected to node 25 and also has the black start ability; A distributed power source with a capacity of 600 kW is connected to node 32 and has the black start ability. Then, this embodiment classifies the loads and clarifies their connected nodes and importance levels. The loads are divided into two major categories: controllable loads and ordinary loads. Among them, ordinary loads are further divided into first-level, second-level, and third-level according to their importance levels. The specific classification and connected nodes are as follows:

[0137] First-level loads: The connected nodes include node 3, node 10, node 11, node 24, and node 31. The capacity of these loads is 100 each, and they are uncontrollable loads;

[0138] Second-level loads: The connected nodes include node 23, node 30, and nodes 4, 6, 12, 15, 18, 19, 21, 22, 33. The capacity of these loads is 10 each, and they are uncontrollable loads;

[0139] Third-level loads: The connected nodes include node 7, node 16, and nodes 2, 5, 8, 9, 13, 14, 17, 20, 25 to 29, 32. The capacity of these loads is 1 each, and they are uncontrollable loads.

[0140] The capacity of other loads and other line parameters follow the standard example of the IEEE 33-node distribution network system. Subsequently, this embodiment established a multi-objective collaborative optimization model for reconstruction and islanding according to the above steps. On this basis, this embodiment set a fault scenario, that is, lines 9 and 22 were disconnected. After calculation and solution, this embodiment obtained the operating state of the distribution network after fault recovery as shown in Figure 3 The results show that the amount of lost load is 0, the total number of switch operations is 4 times, and the lowest voltage among all nodes is 0.932 p.u. This indicates that under the current fault scenario, this algorithm can ensure that the power supply requirements of all loads are met.

[0141] An embodiment of the present invention provides a distribution network fault recovery method based on collaborative optimization of reconstruction and islanding. The method classifies distributed power sources according to the fault response characteristics of distributed power sources in the distribution network to obtain distributed power source classification data; classifies and models the controllability of distribution network loads according to the power supply continuity and controllability of distribution network loads to obtain a load characteristic matrix; reconstructs the distribution network according to the real-time topological connection state of the distribution network and the distributed power source classification data, and divides the operating state of the reconstructed distribution network to obtain a classification diagram of the reconstructed operating state of the distribution network; constructs a multi-objective collaborative optimization model of reconstruction and islanding with the minimization of the total load shedding, the minimization of the system voltage deviation, and the minimization of the number of switch operations as the optimization objectives; uses a mixed-integer programming algorithm to solve the multi-objective collaborative optimization model of reconstruction and islanding to obtain an optimal fault recovery action strategy, and performs fault recovery on the distribution network according to the optimal fault recovery action strategy. Compared with the prior art, by comprehensively considering the fault response characteristics of distributed power sources, etc., the method realizes the efficient reconstruction and islanding division of the distribution network in the event of a fault, improves the efficiency of fault recovery and the reliability and stability of power supply, optimizes resource allocation at the same time, and avoids the waste of distributed power source resources.

[0142] It should be noted that the magnitudes of the serial numbers of the above processes do not mean the order of execution. The order of execution of each process should be determined by its function and internal logic, and should not constitute any limitation to the implementation process of the embodiments of the present application.

[0143] In one embodiment, as Figure 4 shown, an embodiment of the present invention provides a distribution network fault recovery system based on collaborative optimization of reconstruction and islanding. The system includes:

[0144] A power source classification module 101, configured to classify distributed power sources according to the fault response characteristics of distributed power sources in the distribution network to obtain distributed power source classification data; the distributed power source classification data includes black start power source data and non-black start power source data;

[0145] A load analysis module 102, configured to classify and model the controllability of distribution network loads according to the power supply continuity and controllability of distribution network loads to obtain a load characteristic matrix;

[0146] A reconstruction analysis module 103, configured to reconstruct the distribution network according to the real-time topological connection state of the distribution network and the distributed power source classification data, and divide the operating state of the reconstructed distribution network to obtain a classification diagram of the reconstructed operating state of the distribution network;

[0147] The model construction module 104 is configured to construct a multi-objective collaborative optimization model for reconstruction and islanding based on the reconstructed operation state classification diagram of the distribution network and the load characteristic matrix, with the objectives of minimizing the total load loss, minimizing the system voltage deviation, and minimizing the number of switch operations.

[0148] The fault recovery module 105 is configured to solve the multi-objective collaborative optimization model for reconstruction and islanding by using a mixed integer programming algorithm according to the real-time fault information of the distribution network, obtain an optimal fault recovery action strategy, and perform fault recovery on the distribution network according to the optimal fault recovery action strategy.

[0149] For the specific limitations of a distribution network fault recovery system based on collaborative optimization of reconstruction and islanding, reference can be made to the above limitations on a distribution network fault recovery method based on collaborative optimization of reconstruction and islanding, which will not be elaborated here. Those of ordinary skill in the art can realize that, in combination with the various modules and steps described in the embodiments disclosed in the present application, they can be implemented by hardware, software, or a combination of both. Whether these functions are executed in a hardware or software manner depends on the specific application and design constraints of the technical solution. Professional technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of the present application.

[0150] The embodiment of the present invention provides a distribution network fault recovery system based on collaborative optimization of reconstruction and islanding. The power source classification module of the system classifies distributed power sources according to the fault response characteristics of distributed power sources in the distribution network to obtain distributed power source classification data; the load analysis module grades and models the controllability of the distribution network load according to the power supply continuity and controllability of the distribution network load to obtain a load characteristic matrix; the reconstruction analysis module reconstructs the distribution network according to the real-time topological connection state of the distribution network and the distributed power source classification data, and divides the operation state of the reconstructed distribution network to obtain a reconstructed operation state classification diagram of the distribution network; the model construction module constructs a multi-objective collaborative optimization model for reconstruction and islanding based on the reconstructed operation state classification diagram of the distribution network and the load characteristic matrix, with the objectives of minimizing the total load loss, minimizing the system voltage deviation, and minimizing the number of switch operations; the fault recovery module solves the multi-objective collaborative optimization model for reconstruction and islanding by using a mixed integer programming algorithm to obtain an optimal fault recovery action strategy, and performs fault recovery on the distribution network according to the optimal fault recovery action strategy. Compared with the prior art, the system realizes the efficient reconstruction and island division of the distribution network in case of faults by comprehensively considering the fault response characteristics of distributed power sources, etc., improves the efficiency of fault recovery and the reliability and stability of power supply, optimizes the resource allocation at the same time, and avoids the waste of distributed power source resources.

[0151] The above-described embodiments merely represent several preferred embodiments of the present application. The description thereof is relatively specific and detailed, but it should not be construed as a limitation on the scope of the invention patent. It should be noted that for those of ordinary skill in the art, without departing from the technical principles of the present invention, several improvements and substitutions can be made, and these improvements and substitutions should also be regarded as the protection scope of the present application. Therefore, the protection scope of the patent of the present application shall be subject to the protection scope of the claims described above.

Claims

1. A distribution network fault recovery method based on reconstruction and island collaborative optimization, characterized in that: The following steps are involved: Classifying the distributed power sources according to the fault response characteristics of the distributed power sources in the distribution network to obtain distributed power source classification data; the distributed power source classification data includes black start power source data and non-black start power source data; According to the power supply continuity and controllability of the distribution network load, the distribution network load is classified and controllable modeled to obtain the load characteristic matrix; Reconstructing the distribution network according to the real-time topological connection state of the distribution network and the distributed power source classification data, and dividing the operation state of the reconstructed distribution network to obtain a classification diagram of the reconstructed operation state of the distribution network; According to the distribution network reconstruction operation state classification diagram and the load characteristic matrix, a reconstruction and island multi-objective collaborative optimization model is constructed with minimization of the total amount of lost load, minimization of system voltage deviation and minimization of the number of switch operations as optimization objectives; According to the real-time fault information of the distribution network, the reconstruction and island multi-objective collaborative optimization model is solved by using a mixed integer programming algorithm to obtain an optimal fault recovery action strategy, and the distribution network is restored according to the optimal fault recovery action strategy; The step of grading and controllability modeling the distribution network load according to the power supply continuity and controllability of the distribution network load to obtain the load characteristic matrix includes: According to the real-time load data of the distribution network, the entropy weight method is used to evaluate the load power supply continuity level, and the load of the distribution network is graded based on the load power supply continuity level to obtain the power supply load at each level; The load controllability data of each level of power supply load is modeled using a continuous variable model to obtain a controllable load model of each level of power supply load; According to the load power demand changes of each level of power supply load and the controllable load model, the corresponding maximum load adjustment ratio and the minimum load power demand are obtained; Based on the maximum load regulation ratio and the minimum load power requirement, identifying the load controllability flags of each level of power supply load; The load controllability flags of each level of power supply load are integrated, a load characteristic vector is constructed for each load, and the load characteristic vectors of all loads are arranged in sequence to generate a load characteristic matrix.

2. A distribution network fault recovery method based on reconstruction and island collaborative optimization as claimed in claim 1, characterized in that: The step of classifying the distributed power sources according to the fault response characteristics of the distributed power sources in the distribution network to obtain the distributed power source classification data comprises: Conduct fault simulation on the distributed power source in the distribution network to obtain the fault response data of the distributed power source during the fault period; According to the fault response data, the fault response characteristics of each distributed power source are extracted using a principal component analysis method; the fault response characteristics include a black start flag and an island voltage and frequency support capability parameter; According to the fault response characteristics, the distributed power sources are divided into black start power sources and non-black start power sources.

3. A distribution network fault recovery method based on reconstruction and island collaborative optimization as claimed in claim 1, characterized in that: The load characteristic vector includes a load number, a load name, a load type, a load power supply continuity level, and a load controllability flag.

4. A distribution network fault recovery method based on reconstruction and island collaborative optimization as claimed in claim 1, characterized in that: The steps of reconstructing the distribution network according to the real-time topological connection state of the distribution network and the distributed power source classification data, and dividing the operation state of the reconstructed distribution network to obtain a classification diagram of the reconstructed operation state of the distribution network include: Constructing an adjacency matrix of the distribution network according to the real-time topological connection status of the distribution network, and traversing the adjacency matrix using a breadth-first search algorithm to analyze the connectivity status between each distributed power source and the main power grid to obtain the connectivity of the distributed power sources; Generate a topology reconstruction candidate solution according to the connectivity of the distributed power sources, the switch state quantity of the distribution network and the fault isolation section information; Analyze the change of the distribution network topology structure according to the reconstruction tie switch operation sequence in the topology reconstruction candidate scheme, and determine the island division range after the distribution network reconstruction; Determine the black start power distribution location according to the distributed power classification data; Taking the black start power supply distribution position as the clustering center and combining it with the island division range, a clustering algorithm is used to dynamically divide the distribution network into different island operation areas to generate a distribution network reconstruction operation status classification diagram; wherein, the distribution network reconstruction operation status classification diagram at least includes a single distributed power supply island operation status, a multiple distributed power supply island operation status and a main grid joint power supply operation status.

5. A distribution network fault recovery method based on reconstruction and island collaborative optimization as claimed in claim 4, characterized in that: The step of generating a topology reconstruction candidate solution according to the distributed power supply connectivity, the distribution network switch state quantity and the fault isolation section information comprises: Filter out a distributed power source node set disconnected from the main grid according to the distributed power source connectivity to form a first distributed power source node set; Extracting distributed power supply nodes directly connected to the fault section according to the fault isolation section information to form a second distributed power supply node set; Merging the first distributed power source node set and the second distributed power source node set to obtain a candidate reconstruction area; Traversing all the tie switches in the candidate reconstruction area according to the distribution network switch state quantity to generate a switch operation state combination set; updating the adjacency matrix according to the switch operation state combination set to obtain an updated adjacency matrix corresponding to each switch operation state combination; Based on the updated adjacency matrix, a switch operation state combination that satisfies effective reconstruction of the distribution network is selected from the switch operation state combination set to form a reconstruction tie switch operation sequence; Based on the reconfiguration tie switch operation sequence, a topology reconfiguration candidate solution is formed.

6. A distribution network fault recovery method based on reconstruction and island collaborative optimization as claimed in claim 1, characterized in that: The construction process of the reconstruction and island multi-objective collaborative optimization model includes: According to the load characteristic matrix, the load active power demand, the controllable load power supply ratio and the uncontrollable load power supply status are obtained; Determine a load power supply weight according to the load power supply continuity level in the load characteristic matrix, wherein the load power supply weight includes a controllable load power supply weight and an uncontrollable load power supply weight; Calculate the total amount of load loss using the load power supply weight according to the load active power demand, the controllable load power supply ratio and the uncontrollable load power supply status; The distribution network reconstruction operation state classification diagram is analyzed to obtain load node voltage data and tie switch state data under different operation states; The system voltage offset is calculated based on the difference between the load node voltage data and the reference voltage, and the number of switch operations is calculated based on the tie switch state data; By minimizing the total amount of load loss, the system voltage offset and the number of switch operations, a reconstruction and islanding multi-objective collaborative optimization model is constructed.

7. A distribution network fault recovery method based on reconstruction and island collaborative optimization as claimed in claim 6, characterized in that: The constraints of the reconstruction and island multi-objective collaborative optimization model include network topology constraints, power flow constraints, distributed power output constraints, load node voltage constraints and line current constraints.

8. A distribution network fault recovery method based on reconstruction and island collaborative optimization as claimed in claim 1, characterized in that: The optimal fault recovery action strategy includes optimal tie switch opening and closing action, optimal power supply output power, optimal load on-off instruction and optimal controllable load power adjustment instruction.

9. A distribution network fault recovery system based on reconstruction and island collaborative optimization, characterized in that: The system comprises: A power supply classification module, used to classify the distributed power supplies according to the fault response characteristics of the distributed power supplies in the distribution network to obtain distributed power supply classification data; the distributed power supply classification data includes black start power supply data and non-black start power supply data; The load analysis module is used to classify and model the controllability of the distribution network load according to the power supply continuity and controllability of the distribution network load, and obtain the load characteristic matrix; A reconstruction analysis module is used to reconstruct the distribution network according to the real-time topological connection state of the distribution network and the distributed power classification data, and to classify the operation state of the distribution network after reconstruction to obtain a classification diagram of the distribution network reconstruction operation state; A model building module is used to build a reconstruction and island multi-objective collaborative optimization model based on the distribution network reconstruction operation state classification diagram and the load characteristic matrix, with minimization of the total amount of lost load, minimization of system voltage deviation and minimization of the number of switch operations as optimization objectives; A fault recovery module is used to solve the reconstruction and island multi-objective collaborative optimization model according to the real-time fault information of the distribution network by using a mixed integer programming algorithm to obtain an optimal fault recovery action strategy, and perform fault recovery on the distribution network according to the optimal fault recovery action strategy; The load analysis module is specifically used for: According to the real-time load data of the distribution network, the entropy weight method is used to evaluate the load power supply continuity level, and the load of the distribution network is graded based on the load power supply continuity level to obtain the power supply load at each level; The load controllability data of each level of power supply load is modeled using a continuous variable model to obtain a controllable load model of each level of power supply load; According to the load power demand changes of each level of power supply load and the controllable load model, the corresponding maximum load adjustment ratio and the minimum load power demand are obtained; Based on the maximum load regulation ratio and the minimum load power requirement, identifying the load controllability flags of each level of power supply load; The load controllability flags of each level of power supply load are integrated, a load characteristic vector is constructed for each load, and the load characteristic vectors of all loads are arranged in sequence to generate a load characteristic matrix.

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