A fault recovery path evaluation method for flexible interconnected distribution networks in low-voltage distribution substations

Abstract

The present application discloses a method for evaluating the fault recovery path of a flexible interconnected distribution network in a low-voltage distribution substation area, comprising: locating the fault node based on captured fault information and grid topology information; taking the fault node as the root node, searching for all distributed energy storage units that have a direct or indirect electrical connection with the root node; taking each distributed energy storage unit as the center, establishing a fault recovery alternative network including the fault node through load switching iteration, and calculating the load and performance evaluation results of each fault recovery alternative network; combining the load and performance evaluation results of each fault recovery alternative network, judging whether each fault recovery alternative network meets the set electrical boundary constraints and calculating its recovery cost, and selecting the fault recovery alternative network with the lowest recovery cost as the fault recovery path from the fault recovery alternative network that meets the set electrical boundary constraints. The present application can improve the efficiency and effectiveness of fault recovery.

Description

Technical Field

[0001] The present invention relates to the technical field of flexible interconnected distribution networks in low-voltage distribution substations, and in particular to a fault recovery path assessment method for flexible interconnected distribution networks in low-voltage distribution substations. Background Art

[0002] In flexible, interconnected low-voltage distribution networks, determining fault recovery paths is crucial for ensuring stable power system operation. Traditional fault recovery methods often rely on manual operations, which are inefficient and prone to errors. Furthermore, traditional fault recovery strategies often overlook the role of distributed energy storage units in the fault recovery process, resulting in a suboptimal recovery process.

[0003] While some fault recovery methods based on automatic control exist in existing technologies, these methods often target specific distribution network structures or fault types, lacking versatility and flexibility. Furthermore, these methods often fail to fully consider parameters such as the grid topology, branch impedances, and node load types when determining fault recovery paths, resulting in unsatisfactory recovery efficiency and effectiveness. Summary of the Invention

[0004] The purpose of the present invention is to provide a method for evaluating the fault recovery path of a flexible interconnected distribution network in a low-voltage distribution station area, which can improve the fault recovery efficiency and effect.

[0005] The technical solution provided by this application is:

[0006] In a first aspect, the present application provides a method for evaluating a fault recovery path of a flexible interconnected distribution network in a low-voltage distribution sub-area, comprising the following steps:

[0007] Obtain the grid topology information of the flexible interconnected distribution network of the low-voltage distribution substation area;

[0008] Locating a faulty node in the flexible interconnected distribution network according to the captured fault information and the grid topology information to obtain the faulty node;

[0009] Taking the fault node as a root node, searching in the flexible interconnected distribution network according to a set traversal direction and backtracking direction for all distributed energy storage units that have a direct or indirect electrical connection relationship with the root node;

[0010] Taking each of the distributed energy storage units as the center, establishing a fault recovery candidate network including the fault node through load switching iteration, and calculating the load and performance evaluation results of each fault recovery candidate network;

[0011] Combined with the load and performance evaluation results of each fault recovery candidate network, it is determined whether each fault recovery candidate network meets the set electrical boundary constraints, and the recovery cost of each fault recovery candidate network is calculated. The fault recovery candidate network with the lowest recovery cost is selected from the fault recovery candidate networks that meet the set electrical boundary constraints to generate a fault recovery path for the faulty node.

[0012] In a possible implementation, locating a faulty node in the flexible interconnected distribution network to obtain the faulty node based on the captured fault information and the grid topology information includes:

[0013] Collect fault information from the monitoring system of the distribution network;

[0014] Clean, standardize and normalize the collected fault information;

[0015] Extracting fault-related features from fault data;

[0016] Encoding the power grid topology information and the fault-related features extracted from the fault data to obtain a fault feature vector, and calculating the similarity between the fault feature vector and known fault feature vectors in a fault feature library, and determining features related to the fault node location based on the similarity;

[0017] After determining the features related to the location of the faulty node, the location of the faulty node is determined based on the features related to the location of the faulty node.

[0018] In one possible implementation, all distributed energy storage units that have a direct or indirect electrical connection with the root node are searched in the flexible interconnected distribution network according to the set traversal direction and backtracking direction, wherein the specified traversal direction includes the electrical flow direction, and the backtracking direction includes: a reverse electrical flow direction, a backtracking direction determined according to the parent-child relationship of the tree, a backtracking direction determined based on a search queue or stack, a backtracking direction determined based on a predefined backtracking rule, and a backtracking direction determined in combination with the grid protection and control logic; and the optimal backtracking direction is selected according to the actual situation of the grid.

[0019] In one possible implementation, if there is an electrically isolated area in the power grid, the area is skipped during the search for all distributed energy storage units that have a direct or indirect electrical connection with the root node, or the electrical isolation boundary is explicitly processed in the search algorithm.

[0020] In a possible implementation, the set electrical boundary constraints include: voltage range constraints, equipment capacity constraints, network topology constraints, and safety constraints; the restoration cost comprehensively considers load transfer amount, operation cost, and time cost.

[0021] In a second aspect, the present application provides an electronic device, comprising: a memory and a processor;

[0022] The memory is used to store computer programs;

[0023] The processor is configured to call the computer program to execute the method described above.

[0024] In a third aspect, the present application provides a computer-readable storage medium, in which a computer program is stored. When the computer program is executed on an electronic device, the electronic device implements the method described above.

[0025] In a fourth aspect, the present application provides a computer program product, including a computer program, which, when executed on an electronic device, enables the electronic device to implement the method described above.

[0026] The specific implementation methods of the second to fourth aspects of this application can refer to the implementation method of the first aspect above, and will not be repeated here.

[0027] Beneficial effects:

[0028] Compared with the prior art, this application has the following technical differences:

[0029] Comprehensive consideration of grid parameters: In the process of determining the fault recovery path, this application comprehensively considers the grid topology information, including the initial topology structure parameters of the distribution network, branch impedance parameters, and node load type parameters, making the recovery path more reasonable and efficient.

[0030] Fully utilize distributed energy storage units: This application fully utilizes the distributed energy storage units in the flexible interconnected distribution network. Through load switching iteration, a fault recovery alternative network including fault nodes is established, thereby achieving more optimized fault recovery.

[0031] Restoration path selection based on load calculation: In the process of selecting alternative networks, this application selects the fault recovery alternative network with the lowest recovery cost based on the set electrical boundary constraints and the load size of each alternative network, thereby ensuring the efficiency and effectiveness of fault recovery.

[0032] Versatility and flexibility: This application does not depend on a specific distribution network structure or fault type, has strong versatility and flexibility, and can be widely used in various low-voltage distribution substations and flexible interconnected distribution networks.

[0033] Through the above technical differences, this application effectively solves the problems existing in the background technology:

[0034] Improved efficiency and effectiveness of fault recovery: This application achieves a more reasonable and efficient fault recovery path determination by comprehensively considering the grid topology information and making full use of distributed energy storage units, thereby improving the efficiency and effectiveness of fault recovery.

[0035] Enhanced versatility and flexibility of fault recovery: This application does not rely on a specific distribution network structure or fault type, has strong versatility and flexibility, and can adapt to the fault recovery needs of flexible interconnected distribution networks in different low-voltage distribution substations.

[0036] Reduced error rate of manual operation: This application determines the fault recovery path based on automatic control technology, reduces dependence on manual operation, reduces the error rate, and improves the accuracy and reliability of fault recovery. DETAILED DESCRIPTION

[0037] In order to help those skilled in the art better understand the present invention, the following will clearly and completely describe the technical solution of the present invention in conjunction with the embodiments of the present invention. Obviously, the embodiments described are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative work should fall within the scope of protection of the present invention.

[0038] A method for evaluating a fault recovery path of a flexible interconnected distribution network in a low-voltage distribution area comprises the following steps:

[0039] S1. Obtaining grid topology information of the flexible interconnected distribution network of the low-voltage distribution substation area;

[0040] The power grid topology information includes the initial topology parameters of the distribution network, branch impedance parameters and node load type parameters;

[0041] Specifically, since the data management system (DMS) stores the initial topology parameters of the distribution network, which describe the connection relationships between various devices in the distribution network (such as transformers, lines, switches, etc.), the grid topology information of the flexible interconnected distribution network of low-voltage distribution sub-areas can be obtained from the data management system.

[0042] In addition, the data management system also stores the topology of the distribution network, which is represented by the connections between nodes and edges, using graph theory algorithms to abstract devices in the distribution network. The distribution network search methods used in graph theory implementations can be divided into two categories: breadth-first search (BFS) and depth-first search (DFS). These search methods traverse the distribution network structure to obtain the connection relationships between each node and branch in the distribution network.

[0043] Breadth-first Search (BFS) algorithm

[0044] The BFS algorithm is an algorithm for traversing or searching a tree or graph. Starting from the root (or an arbitrary node), the algorithm explores the nearest neighbor nodes. Then, for each of its neighbors, it explores its neighbors, and so on. The algorithm terminates when all nodes have been visited. This process can also be thought of as traversing the graph layer by layer.

[0045] In this application, the steps to implement the BFS algorithm in the distribution network are as follows:

[0046] Initialization: Select a starting node (for example, a node with the highest failure rate determined based on historical failure data) and mark it as visited. At the same time, create a queue and enqueue the starting node.

[0047] Start loop: While the queue is not empty, perform the following steps:

[0048] Remove a node from the queue.

[0049] Traverse all unvisited neighbor nodes of the node.

[0050] Mark each unvisited neighbor node as visited and add it to the queue.

[0051] End: When the queue is empty, it means that all reachable nodes have been visited and the search ends.

[0052] The BFS algorithm is characterized by starting from the root node, prioritizing the traversal of all neighboring nodes, then traversing the neighboring nodes in the next layer, and thus traversing all nodes in the graph layer by layer. In distribution networks, this helps quickly locate all nodes and branches directly connected to the faulty node. This method is suitable for rapid response and fault location, especially for quickly identifying the affected area when a fault occurs. Therefore, the BFS algorithm is preferred when rapid fault location and assessment of the directly affected area are required.

[0053] Depth-first Search (DFS) algorithm

[0054] The DFS algorithm is used to traverse or search a tree or graph. It searches the branches of the graph as deeply as possible. When all edges leading to a node v have been explored, the search backtracks to the starting node of the edge that led to the discovery of node v. This process continues until all nodes reachable from the source node have been discovered. If any undiscovered nodes remain, one of them is chosen as the source node and the process repeats until all nodes have been visited.

[0055] In this application, the steps for implementing depth-first search in the distribution network are as follows:

[0056] Initialization: Select a starting node (for example, a node with the highest failure rate determined based on historical failure data) and mark it as visited. At the same time, create a stack (or recursive function) to store the path.

[0057] Start recursion or loop: For the current node, perform the following steps:

[0058] Traverse all unvisited neighbor nodes of the node.

[0059] For each unvisited neighbor node, mark it as visited and make it the new current node, continuing the recursion or loop.

[0060] When all neighbor nodes of the current node have been visited, return to the previous layer (or pop the stack).

[0061] End: The search ends when all reachable nodes have been visited and there are no more nodes to return to.

[0062] The DFS algorithm is characterized by traversing a graph branch as deeply as possible until there are no more accessible nodes in that branch, then backtracking to the previous node to continue the search. In a distribution network, this approach allows the search to find all reachable nodes and branches directly or indirectly connected to a faulty node. This helps to fully understand the network's connectivity, especially in complex structures. Therefore, if a comprehensive understanding of all possible connection paths to a faulty node is required, the DFS algorithm is the preferred choice.

[0063] Branch impedance parameters describe the electrical characteristics of each branch in the distribution network, such as resistance and reactance. These parameters can typically be obtained through offline measurement or online estimation. The DMS also stores the impedance parameters of each branch.

[0064] The node load type parameter describes the load type and size of each node in the distribution network. These parameters can be obtained indirectly by monitoring electrical quantities such as voltage, current, and power at the node, or directly by measuring the electrical parameters of the load device. In this application, the node load type parameter is also stored by the DMS and updated in real time or periodically to reflect changes in the distribution network load.

[0065] S2. Locating a faulty node in the flexible interconnected distribution network based on the captured fault information and the grid topology information to obtain the faulty node;

[0066] In this application, sensors and monitoring equipment installed in the flexible interconnected distribution network monitor the grid's operating status in real time. When a grid fault occurs, these devices capture fault information, such as abnormal current flow, voltage drop, or equipment failure. This fault information is then sent to the aforementioned data management system (DMS).

[0067] When locating a fault node, this grid topology information is used to analyze the possible location and scope of the fault. For example, based on the voltage and current changes at the time of the fault, combined with the grid topology, a preliminary determination can be made as to which branches or nodes the fault may have occurred. Specifically, based on the captured fault information and grid topology, a fault node location algorithm can be applied to accurately determine the location of the fault node.

[0068] The process of accurately determining the location of the faulty node based on the faulty node location algorithm is as follows:

[0069] Fault information is collected from the monitoring system of the distribution network, including voltage, current, power, etc. when the fault occurs.

[0070] The collected fault information is cleaned, standardized and normalized to eliminate noise, outliers and dimensional inconsistency.

[0071] Extract fault-related features from fault data, such as the amplitude of voltage sag, the rate of current change, etc.

[0072] The grid topology information and fault-related features extracted from the fault data are encoded (for example, input into the XGboot model) to obtain a fault feature vector. The similarity between the fault feature vector and known fault feature vectors in the fault feature library is calculated. Based on the similarity, features related to the location of the fault node are determined, such as the impedance of the branch where the fault node is located and the electrical parameters of adjacent nodes.

[0073] After determining the features related to the location of the faulty node, the location of the faulty node is determined based on the features related to the location of the faulty node.

[0074] Based on the above fault node location determination process, the use of a fault node location algorithm to accurately determine the fault node location brings the following technical benefits:

[0075] Fast response and location: By real-time monitoring and collection of fault information, combined with advanced fault algorithms, we can quickly respond to and locate faults. This significantly shortens the time it takes to discover and resolve faults, improving grid stability and power supply reliability.

[0076] Improved diagnostic accuracy: Cleaning, standardizing, and normalizing fault information, as well as extracting key features from fault data, eliminates the impact of noise and outliers, ensuring accurate fault diagnosis. Furthermore, using advanced machine learning models (such as XGboost) to calculate feature vector similarity further improves the accuracy of fault node location.

[0077] Adaptability to Complex Grid Structures: The fault algorithm can handle complex grid topology information and is not restricted to a specific grid structure or fault type. This makes the algorithm widely applicable and flexible, and can be applied to various low-voltage distribution sub-areas and flexible interconnected distribution networks.

[0078] Reduced maintenance costs: By accurately locating the faulty node, maintenance personnel can be quickly dispatched to the site for repair, reducing unnecessary inspections and troubleshooting. This significantly reduces maintenance costs and improves troubleshooting efficiency.

[0079] Optimizing resource allocation: Accurately locating the fault node can help power system dispatchers better understand the grid's operating status, enabling them to make more reasonable resource allocation decisions. For example, during fault recovery, the operating status of distributed energy storage units can be adjusted based on the location of the fault node to optimize the recovery process.

[0080] S3. Taking the fault node as the root node, search in the flexible interconnected distribution network according to the set traversal direction and backtracking direction for all distributed energy storage units that have a direct or indirect electrical connection with the root node;

[0081] The prescribed traversal direction is, for example, the electrical flow direction, and the backtracking direction is, for example:

[0082] Reverse electrical flow direction:

[0083] If the electrical power flow in the power grid is bidirectional, then the backtracking direction can be opposite to the current traversal direction (i.e., the electrical power flow direction). For example, when the algorithm visits a node and continues searching along the electrical power flow direction, if backtracking is required, it can select an edge opposite to the electrical power flow direction to return to the node at the previous level.

[0084] The backtracking direction is determined by the parent-child relationship of the tree:

[0085] During the search process, the topology of the power grid can be viewed as a tree structure, where the faulty node is the root node and the other nodes are child nodes. In this case, the backtracking direction is to return to the parent node along the parent-child relationship of the tree.

[0086] Backtracking direction determined based on the search queue or stack:

[0087] A queue is used to store the nodes to be visited. When backtracking is needed, the previous state (the last visited node) can be retrieved from the queue. A stack is often used to store the order in which nodes were visited. When backtracking is needed, the current node can be popped from the stack to return to the previous node.

[0088] The backtracking direction is determined based on predefined backtracking rules:

[0089] The backtracking direction can be defined according to the specific requirements or operating rules of the power grid. For example, a rule can be defined so that the search algorithm always chooses the path with the shortest electrical distance or the smallest electrical parameter (such as impedance) when backtracking.

[0090] The backtracking direction determined by combining grid protection and control logic:

[0091] Flexible interconnected distribution networks often employ complex protection and control logic to ensure stable grid operation. Search algorithms can incorporate this logic to define backtracking directions. For example, when backtracking, nodes or paths that have not triggered protection actions or have not been isolated can be prioritized.

[0092] The search starts from the faulty node, first searching all nodes adjacent to the root node, then searching the neighbors of these nodes, and so on. During the search process, when the search algorithm accesses a distributed energy storage unit, it marks it as found and records its connection relationship with the faulty node.

[0093] The search algorithm may adopt the BFS algorithm or the DFS algorithm.

[0094] The various backtracking direction determination strategies mentioned in the above scheme have the following main benefits in determining the fault recovery path of the flexible interconnected distribution network in the low-voltage distribution sub-area:

[0095] Improved search efficiency: By using the electrical flow direction as the traversal direction and combining it with the reverse electrical flow direction as the backtracking direction, the search algorithm is able to follow the actual path of power transmission, avoiding invalid searches and repeated visits, thereby improving search efficiency.

[0096] The parent-child relationship backtracking method of the tree can clearly track the search path, making the backtracking operation more intuitive and efficient.

[0097] Optimize resource utilization: The use of queues and stacks enables the search algorithm to systematically manage the nodes to be visited, ensuring that it can quickly return to the correct location when backtracking is needed, avoiding waste of resources.

[0098] The backtracking direction determined based on the predefined backtracking rules can select the optimal backtracking direction (path) according to the actual situation of the power grid, thereby optimizing the utilization of power grid resources.

[0099] Enhanced system reliability: Defining the backtracking direction in conjunction with the grid’s protection and control logic ensures that the search algorithm avoids nodes and paths that have triggered protection actions or are isolated during the recovery process, thereby improving system reliability and safety.

[0100] By selecting the path with the shortest electrical distance or the smallest electrical parameter (such as impedance) for backtracking, the impact of the fault on the power grid can be further reduced and the robustness of the system can be improved.

[0101] Strong adaptability: The above strategy provides multiple ways to determine the backtracking direction, which can be flexibly selected according to the specific conditions and needs of the power grid. Whether it is a simple tree structure or a complex flexible interconnected distribution network, a suitable backtracking strategy can be found.

[0102] Different backtracking strategies can complement each other, allowing the search algorithm to remain efficient and accurate in a complex and changing power grid environment.

[0103] If there are electrically isolated areas in the power grid (such as parts isolated by circuit breakers), these electrically isolated areas are skipped during the search process. That is, after the search algorithm identifies the electrically isolated areas, it chooses not to enter these areas but continues to search in other areas; alternatively, these electrically isolated boundaries are explicitly handled in the search algorithm.

[0104] Explicitly handling electrical isolation boundaries in the search algorithm can be achieved by following these steps:

[0105] Identify electrical isolation boundaries:

[0106] First, determine which parts of the power grid are electrically isolated, for example by identifying devices such as circuit breakers and disconnect switches that are in the open state. These devices divide the power grid into multiple independent electrically isolated areas.

[0107] Construct a topology graph for electrically isolated areas: For each electrically isolated area, you can construct its topology separately to more clearly understand the connection relationship between nodes and edges in the area.

[0108] Marking electrical isolation boundaries: In the topology diagram of the entire power grid, mark the locations of all electrical isolation boundaries. This can be done by adding special edges or nodes in the diagram to represent electrical isolation boundaries.

[0109] Processing in the search algorithm: When the search traverses an electrical isolation boundary, specific actions need to be taken, such as:

[0110] a. Skip the electrically isolated area:

[0111] If a distributed energy storage unit directly connected to the faulty node is found, you can choose to skip the entire electrically isolated area and continue searching other unisolated areas.

[0112] b. Record quarantine information:

[0113] Information about the electrical isolation boundaries encountered, such as the identifier of the electrical isolation area, boundary devices, etc., is recorded to facilitate decision making during the search process.

[0114] c. Processing during backtracking:

[0115] During the retrace process, if the retrace path passes through the electrical isolation boundary, the electrical isolation area is skipped and the search continues for other non-isolated areas.

[0116] The difference between skipping electrically isolated regions during the search process and explicitly handling these boundaries in the search algorithm is this: suppose there is a fault node in the power grid, and the grid is divided into two electrically isolated regions, A and B, by a circuit breaker. If the algorithm chooses to skip the electrically isolated regions, it may completely ignore region B during the search and only search region A. However, if the algorithm explicitly handles the boundaries, it may record this information when it finds the boundary of region B and decide not to enter region B. It may also record this boundary information for subsequent analysis or decision-making. Therefore, explicit handling can be used to more carefully manage the search path, ensuring that isolated regions are not mistakenly entered.

[0117] Explicitly addressing electrical isolation boundaries in the fault recovery path determination algorithm for a power grid has the following technical benefits:

[0118] Improve search efficiency: Skipping electrically isolated areas can greatly reduce the complexity of the search because the algorithm does not need to perform useless searches inside these areas, which can significantly improve search efficiency, especially in large, complex power grids.

[0119] Optimizing resource allocation: By identifying electrically isolated areas, the algorithm can more accurately understand the actual operating status of the power grid and optimize resource allocation. For example, during fault recovery, the algorithm can prioritize restoring non-isolated areas directly connected to the faulty node to ensure power supply to critical loads.

[0120] Enhanced system reliability: Explicitly handling electrical isolation boundaries ensures that the algorithm does not mistakenly enter isolated areas during search and backtracking, thereby avoiding possible erroneous operations, helping to reduce the impact of erroneous operations on grid stability and improving system reliability.

[0121] Improving algorithm accuracy: Constructing a topological map of the electrically isolated area and marking the electrical isolation boundaries can enable the algorithm to more accurately understand the structure and connection relationships of the power grid, helping the algorithm make more accurate decisions during the search process and improving the accuracy and reliability of the fault recovery path.

[0122] Support for distributed control: Distributed control is a key feature of flexible interconnected distribution networks. Explicitly handling electrical isolation boundaries allows different control units to operate independently within their respective electrical isolation zones, reducing interdependencies and interference, and enabling more flexible and efficient distributed control.

[0123] Enhanced system scalability: As the power grid continues to evolve and change, new electrically isolated regions may emerge. By explicitly addressing electrical isolation boundaries, the algorithm can easily adapt to these changes without requiring large-scale modification or reconstruction, enhancing the scalability and flexibility of the system.

[0124] Supporting Fault Prevention and Maintenance: By recording information about electrical isolation boundaries, the algorithm provides valuable insights for subsequent fault prevention and maintenance. For example, historical records can be used to analyze the causes and patterns of faults in electrically isolated areas, enabling appropriate preventive measures or optimized maintenance strategies.

[0125] S4. With each of the distributed energy storage units as the center, establishing a fault recovery candidate network including the fault node through load switching iteration, and calculating the load and performance evaluation results of each fault recovery candidate network;

[0126] Calculating the load of each fault recovery candidate network involves evaluating the loads in the grid to determine whether they can be supplied by existing distributed energy storage units without violating the grid's operational limitations (such as voltage exceeding the limit, equipment capacity exceeding the limit, etc.).

[0127] In this application, the loads affected by the fault are traversed to check whether they can be supplied by the existing distributed energy storage units.

[0128] If a load can be supplied by a distributed energy storage unit, and this power supply path does not cause the power grid to violate operational restrictions (such as voltage exceeding the limit, equipment capacity exceeding the limit, etc.), then this load is added to the alternative network centered on the distributed energy storage unit. Repeat this process, try different load combinations and connection methods, and generate multiple fault recovery alternative networks. For each generated fault recovery alternative network, calculate its total load, involving flow calculation to determine the power distribution and load level in the network, and use existing power system analysis software or write your own code to implement the flow calculation. In addition, evaluate the performance of each alternative network, including indicators such as recovery time, recovery rate, voltage stability, and equipment utilization.

[0129] Recovery time estimation: Heuristic search algorithms (such as genetic algorithms and simulated annealing) can be used in conjunction with grid switching times to estimate the total time from fault occurrence to complete load transfer. Event time simulation can also be used to simulate the entire recovery process and accurately calculate the recovery time.

[0130] Restoration rate assessment: The load restoration rate is usually defined as the ratio of successfully restored load to the total affected load. It can be calculated by comparing the load levels before and after the failure.

[0131] The node restoration rate can also be used as an indicator to measure the ratio of the number of nodes that have successfully restored power to the total number of failed nodes.

[0132] Voltage Stability Assessment: Power flow analysis is the foundation for voltage stability assessment. Algorithms such as the Newton-Raphson method or Fast Decoupled LoadFlow can be used to calculate power flow and verify that voltage is within the permitted range. Static Voltage Stability Index (such as the L-index) or Continuation PowerFlow can also be used to assess voltage stability.

[0133] Equipment utilization evaluation: Equipment utilization can be calculated by calculating the ratio of the actual output of the equipment during the fault recovery period to its rated capacity. For distributed energy storage units (DESS), depth of discharge (DOD) and state of charge (SOC) can also be considered as evaluation indicators.

[0134] In the grid fault recovery strategy, establishing fault recovery alternative networks through load switching iterations, calculating the load of each alternative network, and evaluating its performance have the following technical benefits:

[0135] Improved restoration efficiency: By traversing the loads affected by the fault and checking whether they can be powered by the DESS, multiple alternative networks for fault restoration can be quickly generated. This flexible strategy can provide multiple restoration solutions in a short period of time, thereby improving restoration efficiency.

[0136] Optimizing resource utilization: When generating alternative networks, considering operational constraints, such as voltage limits and equipment capacity limits, ensures that restoration strategies do not exceed the actual capabilities of the grid. This helps optimize resource utilization and avoids additional burden or risk during the restoration process.

[0137] Improved restoration quality: By calculating the load of each candidate network and evaluating its performance, the optimal restoration plan can be selected. These evaluation metrics, such as restoration time, restoration rate, voltage stability, and equipment utilization, comprehensively reflect the quality and effectiveness of the restoration plan, ensuring that the selected plan maximizes the recovery of normal grid operations.

[0138] Enhanced system stability: Paying special attention to voltage stability when evaluating alternative networks ensures that restoration strategies do not negatively impact grid stability. Using methods such as power flow calculations and the static voltage stability index, potential voltage issues can be identified and addressed promptly, enhancing system stability.

[0139] Supporting Decision Making: Providing multiple alternative networks and detailed performance evaluation results provides powerful decision support for grid dispatchers. Dispatchers can select appropriate restoration plans based on actual conditions, quickly respond to faults, and restore normal grid operations.

[0140] Improved system reliability: By considering indicators such as the depth of discharge and state of charge of distributed energy storage units, it is possible to ensure that DES S are fully utilized during fault recovery, improving their reliability and lifespan. This helps to enhance the reliability of the entire power grid and reduce the risk of power outages caused by equipment failure.

[0141] Strong adaptability: This strategy is highly adaptable to different types of faults and grid structures. Whether it's a simple single-point fault or a complex multi-point fault, this strategy can find an effective recovery solution. Furthermore, the strategy can be flexibly adjusted and optimized based on the actual grid conditions.

[0142] Calculate the load of each failover candidate network,

[0143] S5. Based on the load and performance evaluation results of each fault recovery candidate network, determine whether each fault recovery candidate network meets the set electrical boundary constraints, and calculate the recovery cost of each fault recovery candidate network. Select the fault recovery candidate network with the lowest recovery cost from the fault recovery candidate networks that meet the set electrical boundary constraints to generate a fault recovery path for the fault node.

[0144] In this way, efficient and safe grid fault recovery can be achieved.

[0145] The electrical boundary constraints include:

[0146] Voltage range constraint: ensures that the voltage of all nodes is within the allowed range.

[0147] Equipment capacity constraints: Ensure that the load of all equipment (such as distributed energy storage units, transformers, lines, etc.) does not exceed its capacity limit.

[0148] Network topology constraints: Specific network connectivity or redundancy requirements may need to be met.

[0149] Safety constraints: such as short-circuit current limit, thermal stability, etc.

[0150] Evaluate the recovery cost of alternative networks: For each candidate network for failure recovery, evaluate its recovery cost. The recovery cost can be a comprehensive indicator that combines the following indicators:

[0151] Load transfer: The amount of load transferred, which may be measured by the load recovery rate.

[0152] Operating costs: such as the number of switch operations, equipment loss, etc.

[0153] Time cost: the time required for the recovery process.

[0154] To quantify the recovery cost, the recovery cost is quantified into a single indicator, for example, by the following method:

[0155] Weighted summation: Assign a weight to each factor and add them together. The weight can be adjusted based on the actual situation.

[0156] Cost Function: Define a cost function that takes all relevant factors into account and outputs a single restoration cost value.

[0157] After evaluating the restoration costs of all candidate networks, it is necessary to select the candidate networks that meet the electrical boundary constraints. This can be achieved, for example, by the following steps:

[0158] Verify voltage range constraints: Check whether the voltages of all nodes are within the allowed range.

[0159] Verify device capacity constraints: Ensure that the load on all devices does not exceed their capacity limits.

[0160] Verify network topology constraints: Verify whether the network meets connectivity and redundancy requirements.

[0161] Verify safety constraints: Ensure that safety constraints such as short-circuit current limit and thermal stability are not violated.

[0162] Among them, the alternative network with the lowest recovery cost is selected

[0163] From the alternative networks that meet the electrical boundary constraints, the network with the minimum recovery cost is selected as the final fault recovery path.

[0164] In the power grid fault recovery strategy, the fault recovery alternative network with the lowest recovery cost is selected based on the set electrical boundary constraints to generate the recovery path for the fault node. This has the following technical benefits:

[0165] Ensuring safe and stable grid operation: By considering electrical boundary conditions such as voltage range constraints, equipment capacity, network topology, and safety constraints, the selected restoration path ensures that the grid will not fall into an unsafe operating state. This helps prevent secondary faults or system crashes caused by restoration operations.

[0166] Optimizing resource utilization and costs: When selecting a recovery path, resources can be optimally allocated by evaluating the recovery costs of alternative networks, including factors such as load transfer volume, operating costs, and time costs. Selecting the network with the lowest recovery cost helps reduce recovery costs and improve resource utilization efficiency.

[0167] Improve restoration efficiency and speed: By quantifying the restoration cost and selecting the alternative network with the lowest restoration cost, the optimal restoration path can be quickly determined. This helps shorten fault recovery time and improve grid restoration efficiency and power supply reliability.

[0168] Flexibility and adaptability: This strategy considers multiple electrical boundary constraints and restoration cost factors, allowing for flexible adjustment and optimization based on the actual grid conditions. Whether it's a simple single-point fault or a complex multi-point fault, a suitable restoration path can be found.

[0169] Decision Support: This system provides powerful decision support for grid dispatchers. By evaluating the restoration costs of multiple alternative networks and selecting the ones that meet the constraints, dispatchers can quickly make decisions and select the optimal restoration plan.

[0170] Enhanced system reliability: By selecting the alternative network with the lowest restoration cost as the final fault recovery path, the impact and potential risks on the power grid during the restoration process can be reduced, which helps to enhance the reliability and stability of the power grid and improve user satisfaction with power supply services.

[0171] An embodiment of the present application further provides an electronic device, comprising: a memory and a processor;

[0172] The memory is used to store computer programs;

[0173] The processor is configured to call the computer program to execute the method described above.

[0174] An embodiment of the present application further provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program. When the computer program is executed on an electronic device, the electronic device implements the method described above.

[0175] An embodiment of the present application further provides a computer program product, including a computer program. When the computer program is run on an electronic device, the electronic device implements the method described above.

[0176] The embodiments of the present application also provide a cable positioning system, an electronic device, a computer-readable storage medium, and a computer program product. The specific implementation methods can refer to the specific embodiments of the above methods and will not be repeated here.

[0177] Obviously, those skilled in the art should understand that the above-mentioned units or steps of the present application can be implemented using a general-purpose computing device. They can be concentrated on a single computing device or distributed across a network composed of multiple computing devices. Alternatively, they can be implemented using program codes executable by the computing device, so that they can be stored in a storage device and executed by the computing device, or they can be made into individual integrated circuit modules, or multiple modules or steps can be made into a single integrated circuit module for implementation. Thus, the present application is not limited to any specific combination of hardware and software.

[0178] The above description of the embodiments of the present application is only a partial embodiment of the present application, which is used to enable professionals in this field to implement or use the contents of the present application, and is not intended to limit the present application. For those skilled in the art, the present application may have various changes and variations. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present application should be included in the scope of protection of the present application.

Claims

1. A method for evaluating fault recovery paths in a flexible interconnected distribution network of a low-voltage distribution substation, characterized in that: The steps include: Obtain the grid topology information of the flexible interconnected distribution network of the low-voltage distribution substation area; Locating a faulty node in the flexible interconnected distribution network according to the captured fault information and the grid topology information to obtain the faulty node; Taking the fault node as a root node, searching in the flexible interconnected distribution network according to a set traversal direction and backtracking direction for all distributed energy storage units that have a direct or indirect electrical connection relationship with the root node; Taking each of the distributed energy storage units as the center, establishing a fault recovery candidate network including the fault node through load switching iteration, and calculating the load and performance evaluation results of each fault recovery candidate network; Combined with the load and performance evaluation results of each fault recovery candidate network, it is determined whether each fault recovery candidate network meets the set electrical boundary constraints, and the recovery cost of each fault recovery candidate network is calculated. The fault recovery candidate network with the lowest recovery cost is selected from the fault recovery candidate networks that meet the set electrical boundary constraints to generate a fault recovery path for the faulty node.

2. The method according to claim 1, characterized in that The locating a faulty node in the flexible interconnected distribution network to obtain the faulty node based on the captured fault information and the grid topology information includes: Collect fault information from the monitoring system of the distribution network; Clean, standardize and normalize the collected fault information; Extracting fault-related features from fault data; Encoding the power grid topology information and the fault-related features extracted from the fault data to obtain a fault feature vector, and calculating the similarity between the fault feature vector and known fault feature vectors in a fault feature library, and determining features related to the fault node location based on the similarity; After determining the features related to the location of the faulty node, the location of the faulty node is determined based on the features related to the location of the faulty node.

3. The method according to claim 1, characterized in that The method searches for all distributed energy storage units that have a direct or indirect electrical connection with the root node in the flexible interconnected distribution network according to the set traversal direction and backtracking direction, wherein the set traversal direction includes the electrical flow direction, and the backtracking direction includes: a reverse electrical flow direction, a backtracking direction determined according to the parent-child relationship of the tree, a backtracking direction determined based on a search queue or stack, a backtracking direction determined based on a predefined backtracking rule, and a backtracking direction determined in combination with the grid protection and control logic; and selects the optimal backtracking direction according to the actual situation of the grid.

4. The method according to claim 1, wherein If there is an electrically isolated area in the power grid, the area is skipped during the search for all distributed energy storage units that are directly or indirectly electrically connected to the root node, or the electrically isolated boundary is explicitly handled in the search algorithm; Among them, electrical isolation boundaries are explicitly handled in the search algorithm, including: Identify electrical isolation boundaries and mark the locations of all electrical isolation boundaries in the topology diagram of the entire power grid; When the search traverses to the electrical isolation boundary, the electrical isolation area is skipped and the search continues for other non-isolated areas, and the information of the encountered electrical isolation boundary is recorded; during the backtracking process, if the backtracking path passes through the electrical isolation boundary, the electrical isolation area is skipped and the search continues for other non-isolated areas.

5. The method according to claim 1, wherein The set electrical boundary constraints include: voltage range constraints, equipment capacity constraints, network topology constraints and safety constraints; the restoration cost comprehensively considers load transfer amount, operation cost and time cost.

6. An electronic device, characterized in that: include: memory and processor; The memory is used to store computer programs; The processor is configured to call the computer program to execute the method according to any one of claims 1 to 5.

7. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a computer program, and when the computer program is executed on an electronic device, the electronic device implements the method according to any one of claims 1 to 5.

8. A computer program product comprising a computer program, characterized in that When the computer program is executed on an electronic device, the electronic device is enabled to implement the method according to any one of claims 1 to 5.