Power grid target net rack reconstruction optimization method, system and device and medium

Abstract

The invention belongs to the technical field of large power grid recovery, and discloses a power grid target net rack reconstruction optimization method, system and device and a medium, and the method comprises the steps: obtaining power grid operation data; identifying a fault line based on the power grid operation data, and evaluating the probability of a recoverable line fault caused by a disaster in a preset time period; dividing a power failure area into a plurality of recovery partitions, sampling and simulating a recoverable state of a line in the recovery partitions, and calculating a node importance degree expected value based on the recoverable state; and in combination with a discrete particle swarm algorithm, generating optimal particles of each recovery partition to optimize the target net rack, and converting the optimal particles of each recovery partition into a corresponding target net rack scheme. According to the method, optimization adjustment calculation of a large amount of unreasonable net racks is avoided, the algorithm efficiency is improved, the method can be applied to online optimization, the target net racks are dynamically updated online according to disaster information and power grid power failure scenes, and decision support can be provided for dispatching operation personnel for power grids in different recovery stages.

Description

Technical Field

[0001] The present invention relates to the technical field of large power grid restoration, and in particular to a power grid target grid reconstruction optimization method, system, device and medium. Background Art

[0002] Natural disasters are the main cause of blackouts and increase the risk of power grid restoration. How to reasonably consider disaster factors in restoration decisions and avoid their adverse effects is an urgent problem to be solved. Target grid reconstruction is crucial to power grid restoration, especially the optimization decision of the skeleton network, which can dynamically analyze the key nodes and lines to be restored, form a reasonable transitional grid (the power grid system during the restoration process), gradually expand the available restoration power and restoration range, and provide decision support for dispatching and operation personnel.

[0003] Most existing studies analyze the importance of nodes and lines from the topological characteristics of the power network and optimize the target grid. The importance of nodes and lines is a key indicator for restoration decision optimization. Currently, node contraction method, betweenness method, and deletion method are mostly used to evaluate the topological importance of nodes from a global perspective. In terms of target grid reconstruction optimization, most existing studies take the maximum node importance as the goal, and use discrete particle swarm algorithm (DPSO), hierarchical optimization and other methods to solve, and only consider connectivity constraints for the network structure. To this end, the present invention constructs a fitness function based on line and node importance indicators, converts the rationality of the grid into a penalty function, and uses the DPSO algorithm to optimize the target grid, so that the target grid can be dynamically updated according to disaster information and power grid outage scenarios, providing decision support for dispatching and operation personnel.

[0004] Therefore, how to provide a power grid target grid reconstruction optimization method, system, device and medium is a problem that needs to be solved urgently. Summary of the invention

[0005] Embodiments of the present invention provide a power grid target grid reconstruction optimization method, system, device and medium to solve the problem in the prior art that the target grid cannot be dynamically updated online based on disaster information and power grid blackout scenarios.

[0006] In order to have a basic understanding of some aspects of the disclosed embodiments, a brief summary is given below. This summary is not intended to be a general review, nor is it intended to identify key / important components or to delineate the scope of protection of these embodiments. Its only purpose is to present some concepts in a simple form as a preface to the detailed description that follows.

[0007] According to a first aspect of an embodiment of the present invention, a method for optimizing target grid reconstruction of a power grid is provided.

[0008] In one embodiment, the power grid target grid reconstruction optimization method includes:

[0009] Obtain grid operation data; identify faulty lines based on grid operation data, and assess the probability of disasters causing recoverable line failures within a preset period of time;

[0010] The power outage area is divided into several recovery zones, the recoverable states of the simulated lines are sampled in the recovery zones, and the expected values ​​of node importance are calculated based on the recoverable states;

[0011] The nodes are grouped according to the expected value of node importance, and the optimal particles of each recovery partition are generated in combination with the discrete particle swarm algorithm to optimize the target grid. The optimal particles of each recovery partition are converted into the corresponding target grid solution.

[0012] In one embodiment, the power grid operation data includes power grid parameters, real-time line operation data, disaster monitoring data, equipment failure alarm data, equipment maintenance plan data and scheduling plan data; the real-time power grid operation data includes the operation status of power grid equipment and the shutdown status of power grid equipment.

[0013] In one embodiment, the identifying of faulty lines based on power grid operation data and evaluating the probability of disasters causing recoverable line failures within a preset period of time includes:

[0014] Identify faulty lines based on real-time line operation data, equipment fault alarm data, and equipment maintenance plan data;

[0015] Calculate the probability of a disaster causing a recoverable line failure within a preset period of time in combination with the disaster monitoring data, wherein the probability of a recoverable line failure includes a single failure probability and a group failure probability;

[0016] The single failure probability indicates the probability of a single recoverable line failing, and the group failure probability indicates the probability of several recoverable lines failing successively within a preset time period.

[0017] In one embodiment, dividing the power outage area into a plurality of recovery partitions, sampling the recoverable state of the simulated line in the recovery partition, and calculating the expected value of the node importance based on the recoverable state includes:

[0018] Based on the real-time operation data of the line, the nodes and faulty lines at the time of power outage are dynamically divided into several recovery partitions, wherein the recovery partitions include at least one power supply node and several fully stopped nodes, and all nodes are connected through recoverable lines;

[0019] The recovery partition is randomly selected to simulate the recoverable state and recovery operation time of the faulty line, and the expected value of the node importance is calculated based on the recoverable state.

[0020] In one embodiment, the method of using a discrete particle swarm algorithm to generate the optimal particles of each recovery partition to optimize the target grid, and converting the optimal particles of each recovery partition into a corresponding target grid solution includes:

[0021] Incorporate the selected lines in the recovery partition into the value sequence of the target grid as particles, and calculate the initial particle group based on the total number of particles in the recovery partition;

[0022] Execute particle swarm iterative optimization, perform topological analysis on the target grid corresponding to the particles updated in each round of iteration, and calculate the penalty function value and fitness function value based on the analysis results;

[0023] If the iteration condition meets the maximum number of iteration steps or the minimum error of the fitness function value, the particle swarm iterative optimization is stopped within a limited time to obtain the optimal particle, and the output optimal particle is converted into the corresponding target grid scheme. Otherwise, the particle swarm iterative optimization is continuously performed until the optimal particle is found.

[0024] In one embodiment, calculating the initial particle group based on the total number of particles in the recovery partition includes:

[0025] The nodes are grouped according to the expected values ​​of node importance to obtain several groups of grouping results, and the nodes with high average expected values ​​of importance are selected from each group of grouping results as initial nodes;

[0026] Construct a minimum tree connecting the initial nodes, add a preset number of important lines to the minimum tree, perform a topology check, and generate initial particles;

[0027] Repeat the minimum tree construction process for each clustering result to obtain the initial particles of each clustering result, and aggregate several initial particles to generate the first group of initial particle groups;

[0028] The electrical distance from the power node is combined with the particle optimization condition to adjust each particle in the first group of initial particle groups, and the minimum tree is updated based on the adjustment result to obtain the second group of initial particle groups.

[0029] In one embodiment, the calculation formula of the fitness function is:

[0030]

[0031] Where f is the fitness function; E is the line number set selected by the particle; G is the power generation node number set selected by the particle; B is the non-power generation node number set; V i ' L is the normalized expected value of line importance, V j ' G is the normalized expected value of the importance of the power generation node, V k ' Bis the normalized importance of non-generation nodes; ω L is the line weight; ω G is the power generation node weight; ω B is the weight of non-generation node; ω F is the penalty function weight; f p is the penalty function value of unreasonable particles.

[0032] According to a second aspect of an embodiment of the present invention, a power grid target grid reconstruction optimization system is provided.

[0033] In one embodiment, the power grid target grid reconstruction optimization system includes:

[0034] A fault probability assessment module is used to obtain grid operation data; identify faulty lines based on grid operation data, and assess the probability of disasters causing recoverable line failures within a preset period of time;

[0035] The importance evaluation module is used to divide the power outage area into several recovery zones, sample the recoverable state of the simulated line in the recovery zone, and calculate the expected value of the node importance based on the recoverable state;

[0036] The grid optimization module is used to group nodes according to the expected value of node importance, and generate the optimal particles of each recovery partition in combination with the discrete particle swarm algorithm to optimize the target grid, and convert the optimal particles of each recovery partition into the corresponding target grid solution.

[0037] In one embodiment, the power grid operation data includes power grid parameters, real-time line operation data, disaster monitoring data, equipment failure alarm data, equipment maintenance plan data and scheduling plan data; the real-time power grid operation data includes the operation status of power grid equipment and the shutdown status of power grid equipment.

[0038] In one embodiment, the fault probability assessment module includes a fault line identification module and a line fault calculation module, wherein:

[0039] Fault line identification module, used to identify faulty lines based on real-time line operation data, equipment fault alarm data and equipment maintenance plan data;

[0040] A line fault calculation module, used to calculate the probability of a disaster causing a recoverable line fault within a preset period of time in combination with the disaster monitoring data, wherein the probability of the recoverable line fault includes a single fault probability and a group fault probability;

[0041] The single failure probability indicates the probability of a single recoverable line failing, and the group failure probability indicates the probability of several recoverable lines failing successively within a preset time period.

[0042] In one embodiment, the importance evaluation module includes a recovery partition division module and an importance calculation module, wherein:

[0043] A recovery partition division module is used to dynamically divide the nodes and faulty lines at the time of power outage into several recovery partitions based on the real-time operation data of the lines. The recovery partitions include at least one power supply node and several fully stopped nodes, and all nodes are connected through recoverable lines;

[0044] The importance calculation module is used to randomly select a recovery partition to simulate the recoverable state and recovery operation time of the faulty line, and calculate the expected value of the node importance based on the recoverable state.

[0045] In one embodiment, the grid optimization module includes an initial particle swarm optimization module, a grid topology analysis module, and a target grid solution generation module, wherein:

[0046] An initial particle swarm optimization module is used to include the selected lines in the recovery partition into the value sequence of the target grid as particles, and calculate the initial particle swarm based on the total number of particles in the recovery partition;

[0047] The grid topology analysis module is used to perform particle swarm iterative optimization, perform topological analysis on the target grid corresponding to the particles updated in each round of iteration, and calculate the penalty function value and fitness function value based on the analysis results;

[0048] The target grid scheme generation module is used to stop the particle swarm iterative optimization to obtain the optimal particle if the iteration condition meets the maximum number of iteration steps or the minimum error of the fitness function value, and convert the output optimal particle into the corresponding target grid scheme. Otherwise, the particle swarm iterative optimization is continuously performed until the optimal particle is found.

[0049] In one embodiment, the initial particle swarm optimization module includes an initial node selection module, an initial particle generation module, a first initial particle swarm generation module and a second initial particle swarm generation module, wherein:

[0050] The initial node selection module is used to group the nodes according to the expected values ​​of node importance to obtain several groups of grouping results, and select the nodes with high average expected values ​​of importance from each group of grouping results as the initial nodes;

[0051] The initial particle generation module is used to construct a minimum tree connecting the initial nodes, add a preset number of important lines to the minimum tree, perform topology check, and generate initial particles;

[0052] A first initial particle swarm generation module is used to repeat the minimum tree construction process for each clustering result to obtain the initial particles of each clustering result, and to aggregate several initial particles to generate a first group of initial particle swarms;

[0053] The second initial particle swarm generation module is used to adjust each particle in the first initial particle swarm by combining the electrical distance from the power node with the particle optimization condition, and obtain the second initial particle swarm after updating the minimum tree based on the adjustment result.

[0054] In one embodiment, the calculation formula of the fitness function is:

[0055]

[0056] Where f is the fitness function; E is the line number set selected by the particle; G is the power generation node number set selected by the particle; B is the non-power generation node number set; V i ' L is the normalized expected value of line importance, V j ' G is the normalized expected value of the importance of the power generation node, V k ' B is the normalized importance of non-generation nodes; ω L is the line weight; ω G is the power generation node weight; ω B is the weight of non-generation node; ω F is the penalty function weight; f p is the penalty function value of unreasonable particles.

[0057] According to a third aspect of an embodiment of the present invention, a computer device is provided.

[0058] In one embodiment, the computer device includes a memory and a processor, the memory stores a computer program, and the processor implements the steps of the above method when executing the computer program.

[0059] According to a fourth aspect of embodiments of the present invention, a computer-readable storage medium is provided.

[0060] In one embodiment, the computer-readable storage medium stores a computer program, and the computer program implements the steps of the above method when executed by a processor.

[0061] The technical solution provided by the embodiment of the present invention may have the following beneficial effects:

[0062] The present invention constructs a fitness function based on line and node importance indicators, and converts constraints such as grid connectivity and loop-free network into penalty functions and adds them to the fitness function. At the same time, the DPSO algorithm is used to optimize the target grid, thereby avoiding a large number of unreasonable grid optimization adjustment calculations and improving the algorithm efficiency. The present invention can be applied to online optimization, and the target grid can be dynamically updated online according to disaster information and power grid blackout scenarios, and decision support can be provided for dispatching and operating personnel for power grids in different recovery stages.

[0063] It is to be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0064] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the invention and, together with the description, serve to explain the principles of the invention.

[0065] Figure 1 is a flow chart of a power grid target grid reconstruction optimization method according to an exemplary embodiment;

[0066] Figure 2 is a principle block diagram of a power grid target grid reconstruction optimization system according to an exemplary embodiment;

[0067] Figure 3 The figure is a schematic diagram showing the structure of a computer device according to an exemplary embodiment. DETAILED DESCRIPTION

[0068] The following description and accompanying drawings fully illustrate the specific embodiments of this article so that those skilled in the art can practice them. Parts and features of some embodiments may be included in or replace parts and features of other embodiments. The scope of the embodiments of this article includes the entire scope of the claims, as well as all available equivalents of the claims. Herein, the terms "first", "second", etc. are only used to distinguish one element from another, without requiring or implying any actual relationship or order between these elements. In fact, the first element can also be called the second element, and vice versa. Moreover, the terms "include", "comprise" or any other variant thereof are intended to cover non-exclusive inclusion, so that the structure, device or equipment including a series of elements includes not only those elements, but also other elements that are not explicitly listed, or also include elements inherent to such structure, device or equipment. In the absence of more restrictions, the elements defined by the sentence "including one..." do not exclude the existence of other identical elements in the structure, device or equipment including the elements. Each embodiment is described in a progressive manner herein, and each embodiment focuses on the differences from other embodiments, and the same and similar parts between the embodiments can be referred to each other.

[0069] The terms "longitudinal", "lateral", "upper", "lower", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside", etc. in this document indicate the orientation or position relationship based on the orientation or position relationship shown in the drawings, and are only for the convenience of describing this document and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operate in a specific orientation, and therefore cannot be understood as a limitation on the present invention. In the description of this document, unless otherwise specified and limited, the terms "installed", "connected", and "connected" should be understood in a broad sense, for example, it can be a mechanical connection or an electrical connection, it can also be the internal communication of two elements, it can be a direct connection, or it can be an indirect connection through an intermediate medium. For ordinary technicians in this field, the specific meanings of the above terms can be understood according to specific circumstances.

[0070] As used herein, the term "plurality" means two or more than two, unless otherwise specified.

[0071] In this document, the character " / " indicates that the preceding and following objects are in an "or" relationship. For example, A / B means: A or B.

[0072] In this article, the term "and / or" is a description of the association relationship between objects, indicating that three relationships may exist. For example, A and / or B means: A or B, or, A and B.

[0073] It should be understood that, although the various steps in the flow chart are displayed in sequence according to the indication of the arrows, these steps are not necessarily executed in sequence according to the order indicated by the arrows. Unless there is a clear description in this article, the execution of these steps is not strictly limited in order, and these steps can be executed in other orders. Moreover, at least a portion of the steps in the figure may include multiple sub-steps or multiple stages, and these sub-steps or stages are not necessarily executed at the same time, but can be executed at different times, and the execution order of these sub-steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a portion of the sub-steps or stages of other steps.

[0074] Each module in the device or system of the present application can be implemented in whole or in part by software, hardware, or a combination thereof. The above modules can be embedded in or independent of a processor in a computer device in the form of hardware, or can be stored in a memory in a computer device in the form of software, so that the processor can call and execute operations corresponding to the above modules.

[0075] In the absence of conflict, the embodiments of the present invention and the features of the embodiments may be combined with each other.

[0076] Figure 1 An embodiment of the power grid target grid reconstruction optimization method of the present invention is shown.

[0077] In this optional embodiment, the power grid target grid reconstruction optimization method includes:

[0078] Step S101, acquiring power grid operation data; identifying faulty lines based on the power grid operation data, and evaluating the probability of disasters causing recoverable line failures within a preset period of time;

[0079] Step S103, dividing the power outage area into a number of restoration partitions, sampling the restorable states of simulated lines in the restoration partitions, and calculating the expected values ​​of node importance based on the restorable states;

[0080] Step S105, clustering the nodes according to the expected value of node importance, and generating the optimal particles of each recovery partition in combination with the discrete particle swarm algorithm to optimize the target grid, and converting the optimal particles of each recovery partition into the corresponding target grid solution.

[0081] In this optional embodiment, the power grid operation data includes power grid parameters, real-time line operation data, disaster monitoring data, equipment failure alarm data, equipment maintenance plan data and scheduling plan data; the real-time power grid operation data includes the operation status of power grid equipment and the shutdown status of power grid equipment.

[0082] In this optional embodiment, the identifying of the faulty line based on the power grid operation data and evaluating the probability of a disaster causing a recoverable line fault within a preset period of time includes:

[0083] Identify faulty lines based on real-time line operation data, equipment fault alarm data, and equipment maintenance plan data; calculate the probability of a disaster-induced fault in a restorable line within a preset time period in combination with the disaster monitoring data, wherein the probability of a restorable line failure includes a single failure probability and a group failure probability; the single failure probability represents the probability of a single restorable line failing, and the group failure probability represents the probability of several restorable lines failing one after another within a preset time period.

[0084] In this optional embodiment, the power outage area is divided into a plurality of recovery partitions, the recoverable state of the simulated line is sampled in the recovery partition, and the expected value of the node importance is calculated based on the recoverable state, including:

[0085] Based on the real-time operation data of the line, the nodes and faulty lines at the time of power outage are dynamically divided into several recovery partitions. The recovery partitions include at least one power supply node and several fully stopped nodes, and all nodes are connected through recoverable lines. The recovery partitions are randomly selected to simulate the recoverable state and recovery operation time of the faulty line, and the expected value of the node importance is calculated based on the recoverable state.

[0086] In this optional embodiment, the use of discrete particle swarm algorithm to generate the optimal particles of each recovery partition to optimize the target grid, and converting the optimal particles of each recovery partition into the corresponding target grid solution includes:

[0087] The selected lines in the recovery partition are included in the value sequence of the target grid as particles, and the initial particle swarm is calculated based on the total number of particles in the recovery partition; the particle swarm iterative optimization is performed, and the target grid corresponding to the particles updated in each round of iteration is subjected to topological analysis, and the penalty function value and the fitness function value are calculated according to the analysis results; if the iteration condition meets the maximum number of iteration steps or the minimum error of the fitness function value, the particle swarm iterative optimization is stopped within a time limit to obtain the optimal particle, and the output optimal particle is converted into the corresponding target grid solution, otherwise, the particle swarm iterative optimization is continuously performed until the optimal particle is found.

[0088] In this optional embodiment, calculating the initial particle group based on the total number of particles in the recovery partition includes:

[0089] The nodes are clustered according to the expected values ​​of node importance to obtain several groups of clustering results, and the nodes with high average expected values ​​of importance are selected from each group of clustering results as initial nodes; a minimum tree connecting the initial nodes is constructed, and a topological check is performed after adding a preset number of important lines to the minimum tree, and initial particles are generated; the minimum tree construction process is repeated for each clustering result to obtain the initial particles of each clustering result, and several initial particles are collected to generate a first group of initial particle groups; the electrical distance from the power node is combined with the particle optimization condition to adjust each particle in the first group of initial particle groups, and the minimum tree is updated based on the adjustment result to obtain a second group of initial particle groups.

[0090] Figure 2 An embodiment of the power grid target grid reconstruction optimization system of the present invention is shown.

[0091] In this optional embodiment, the power grid target grid reconstruction optimization system includes:

[0092] The fault probability assessment module 201 is used to obtain power grid operation data; identify faulty lines based on the power grid operation data, and assess the probability of disasters causing recoverable line failures within a preset period of time;

[0093] The importance evaluation module 203 is used to divide the power outage area into a number of restoration partitions, sample the restorable state of the simulated line in the restoration partition, and calculate the expected value of the node importance based on the restorable state;

[0094] The grid optimization module 205 is used to group nodes according to expected values ​​of node importance, generate optimal particles of each recovery partition in combination with discrete particle swarm algorithm to optimize the target grid, and convert the optimal particles of each recovery partition into the corresponding target grid solution.

[0095] In this optional embodiment, the power grid operation data includes power grid parameters, real-time line operation data, disaster monitoring data, equipment failure alarm data, equipment maintenance plan data and scheduling plan data; the real-time power grid operation data includes the operation status of power grid equipment and the shutdown status of power grid equipment.

[0096] In this optional embodiment, the fault probability assessment module 201 includes a fault line identification module and a line fault calculation module, wherein:

[0097] Fault line identification module, used to identify faulty lines based on real-time line operation data, equipment fault alarm data and equipment maintenance plan data;

[0098] A line fault calculation module, used to calculate the probability of a disaster causing a recoverable line fault within a preset period of time in combination with the disaster monitoring data, wherein the probability of the recoverable line fault includes a single fault probability and a group fault probability;

[0099] The single failure probability indicates the probability of a single recoverable line failing, and the group failure probability indicates the probability of several recoverable lines failing successively within a preset time period.

[0100] In this optional embodiment, the importance evaluation module 203 includes a recovery partition division module and an importance calculation module, wherein:

[0101] A recovery partition division module is used to dynamically divide the nodes and faulty lines at the time of power outage into several recovery partitions based on the real-time operation data of the lines. The recovery partitions include at least one power supply node and several fully stopped nodes, and all nodes are connected through recoverable lines;

[0102] The importance calculation module is used to randomly select a recovery partition to simulate the recoverable state and recovery operation time of the faulty line, and calculate the expected value of the node importance based on the recoverable state.

[0103] In this optional embodiment, the grid optimization module 205 includes an initial particle swarm optimization module, a grid topology analysis module and a target grid solution generation module, wherein:

[0104] An initial particle swarm optimization module is used to include the selected lines in the recovery partition into the value sequence of the target grid as particles, and calculate the initial particle swarm based on the total number of particles in the recovery partition;

[0105] The grid topology analysis module is used to perform particle swarm iterative optimization, perform topological analysis on the target grid corresponding to the particles updated in each round of iteration, and calculate the penalty function value and fitness function value based on the analysis results;

[0106] The target grid scheme generation module is used to stop the particle swarm iterative optimization to obtain the optimal particle if the iteration condition meets the maximum number of iteration steps or the minimum error of the fitness function value, and convert the output optimal particle into the corresponding target grid scheme. Otherwise, the particle swarm iterative optimization is continuously performed until the optimal particle is found.

[0107] In this optional embodiment, the initial particle swarm optimization module includes an initial node selection module, an initial particle generation module, a first initial particle swarm generation module and a second initial particle swarm generation module, wherein:

[0108] The initial node selection module is used to group the nodes according to the expected values ​​of node importance to obtain several groups of grouping results, and select the nodes with high average expected values ​​of importance from each group of grouping results as the initial nodes;

[0109] The initial particle generation module is used to construct a minimum tree connecting the initial nodes, add a preset number of important lines to the minimum tree, perform topology check, and generate initial particles;

[0110] A first initial particle swarm generation module is used to repeat the minimum tree construction process for each clustering result to obtain the initial particles of each clustering result, and to aggregate several initial particles to generate a first group of initial particle swarms;

[0111] The second initial particle swarm generation module is used to adjust each particle in the first initial particle swarm by combining the electrical distance from the power node with the particle optimization condition, and obtain the second initial particle swarm after updating the minimum tree based on the adjustment result.

[0112] In this optional embodiment, the calculation formula of the fitness function is:

[0113]

[0114] Where f is the fitness function; E is the line number set selected by the particle; G is the power generation node number set selected by the particle; B is the non-power generation node number set; V i ' L is the normalized expected value of line importance, V j ' G is the normalized expected value of the importance of the power generation node, V k ' B is the normalized importance of non-generation nodes; ω L is the line weight; ω G is the power generation node weight; ω B is the weight of non-generation node; ω F is the penalty function weight; f p is the penalty function value of unreasonable particles.

[0115] The present invention is further described in detail below in conjunction with specific implementation modes.

[0116] Step 1: Obtain grid parameters, real-time grid operation information, disaster monitoring and prediction information, equipment failure alarm information, equipment maintenance plan information, and dispatch plan information;

[0117] The real-time operation information of the power grid includes the operation status of the power grid equipment and other real-time operation data, wherein the operation status includes the operation status and the shutdown status.

[0118] Step 2: Combine the real-time operation information, fault alarm information and maintenance plan information of the line to identify the line with current fault or maintenance, and then calculate and set the evaluation period T in combination with the disaster information. E The probability of disasters causing recoverable line failures at each time point;

[0119] Evaluating the probability of restorable line failures includes evaluating the probabilities of single line failures and group failures in turn. The single failure probability refers to the probability of a single line failure, and the group failure probability refers to the probability of multiple lines failing simultaneously or successively within a set period of time. The restorable line refers to the line that has no permanent failures and has not been shut down for maintenance and can be put into operation. Otherwise, it is an irreversible line.

[0120] Step 3: Dynamically partition the blackout area according to the real-time operation information of the power grid. The following steps can be executed in parallel for each restoration partition;

[0121] The dynamic partitioning of the power outage area includes: after a power outage occurs, the nodes and lines of the power outage are dynamically divided into several recovery partitions according to the real-time operation information of the power grid, and all recovery partitions can be restored in parallel; a single recovery partition contains at least one power supply node and several fully stopped nodes, and all nodes can be connected through operating lines or recoverable lines. The power supply node can be a grid-connected node of a black start unit, or a live node connected to the power outage node through a recoverable device; the node refers to a power plant or a substation in the power grid, which are respectively called a power generation node and a non-power generation node.

[0122] Step 4: Restore any recovery partition S i , sampling simulation line recoverable state and recovery operation time, based on the recoverable state of each round of sampling simulation, calculate the expected value of importance of each node and line, i = 1, 2, ... N S , where N S is the total number of recovery partitions.

[0123] Among them, according to the probability evaluation result of the recoverable line fault, the initialization of this round of Monte Carlo sampling simulation is carried out, and the next step is entered to start the i-th round of sampling simulation. Monte Carlo sampling simulation, also known as sampling simulation of recoverable state, further referred to as sampling simulation, refers to sampling simulation based on the probability of transmission line fault to simulate whether a permanent fault occurs at the corresponding time point, and the permanent fault of the line is regarded as an unrecoverable state. If there is no permanent fault in the line and it has not been repaired, it is a recoverable state.

[0124] Sampling simulation initialization, including setting the initial value of the time point sequence number in this round j = 1, j = 1, 2, ..., N t Each round of sampling is intended to include N t At a certain time point, the initial state of the line and node in this round is set to a recoverable state;

[0125] The above-mentioned recoverable state includes the recoverable state of the line and the node. The recoverable state of the line means that the line has no permanent fault and has not been shut down for maintenance, and can be put into operation as a power supply path. Otherwise, it is an irrecoverable state. The recoverable state of the node means whether the node can be restored to the powered operation state by receiving external power supply. If so, it is a recoverable state, otherwise it is an irrecoverable state.

[0126] The above nodes refer to power plants or substations in the power grid, which are respectively called power generation nodes and non-power generation nodes.

[0127] For the jth time point of this round of sampling simulation, if j>1, then the grid state after sampling simulation at the j-1th time point of this round is If j = 1, then the actual state of the power grid is (When j>1, the state after sampling simulation at the previous time point is the initial state of sampling simulation at the jth time point. When sampling for the first time, that is, j=1, there is no sampling simulation state at the previous time point, so the current actual state of the power grid should be used as the initial state. Since when j=1 and j>1, the symbol represents the initial state for sampling at the jth time point, the same variable symbol is used for the power grid states at two different time points because their meanings are the same and correspond to different j values). Execute the sampling simulation step of the recoverable state, sample and simulate the recoverable state of the line and node at that time point, and the recovery operation time of the line; then Remove the lines and nodes whose sampling results are irrecoverable from the power grid in the state, and record the power grid state after removal as

[0128] The sampling simulation steps of the recoverable state include:

[0129] a) Sample each line according to its single fault probability at time point j. For any k-th line L k , if the sampling random number is less than L K Failure probability, then L K A permanent failure has occurred and the system is in an unrecoverable state. Otherwise, it is a recoverable state. At this time, based on the probability distribution of switch operation time, the operation time of the line is sampled.

[0130] b) Sample the unrecoverable state caused by the group fault one by one according to the probability of all group faults at time point j, that is, if the product of the random sampling values ​​of each component line of the group in step a) is less than the probability of the group fault, it is considered that the group fault has occurred and the component lines are all in an unrecoverable state. For example, fi is the fault number, if the component line L of the fault in step a) k1 , L k2 If the product of the random number sampling is less than the fault probability, it is considered that the group fault has occurred and both lines are in an unrecoverable state.

[0131] The same goes for other group failures;

[0132] Remove the lines and nodes whose sampling results are irrecoverable, and record the power grid status after removal as Including: According to the sampling results, from Remove the network from the state to be restored Then analyze the isolated nodes or local passive networks and remove them. The removed lines and nodes are irreversible. The current grid state after sampling simulation is recorded as

[0133] based on Calculate the importance of power generation nodes and non-power generation nodes, respectively Calculate the importance of the line as follows: The importance of irrecoverable lines and nodes is 0;

[0134]

[0135] In the formula, for The betweenness of line k in state, yes The number of shortest paths from node s to node t in the state, It is the number of shortest paths between nodes s and t through line k, and the sum of reactance and susceptance is used as the line weight; V is the set of nodes in the network.

[0136] The calculation formula for the importance of the above power generation nodes and non-power generation nodes is as follows:

[0137]

[0138] If j<N t , then j=j+1, return to S3, otherwise go to the next step, N t is the number of sampling points;

[0139] If i<N s , then i=i+1, return to S2, otherwise go to the next step, N s is the preset maximum number of iterations;

[0140] Based on all N s Round results, calculate T according to the following formula EImportance index of lines, power generation nodes, and non-power generation nodes affected by disasters and operation time expectation

[0141]

[0142] In the formula, Indicates the line importance index; Indicates the importance index of power generation node; Represents the importance index of non-generation nodes; Indicates the expected value of operation time; N s Indicates the maximum number of iterations preset for sampling simulation; N t represents the sampling time point; i represents the round of sampling simulation; j represents the time point; Indicates the importance of the line; Indicates the importance of the power generation node; Indicates the importance of non-generation nodes; Represents the operation time of sampling the kth line.

[0143] Step 5: Use discrete particle swarm algorithm to optimize the target grid, define particles, and initialize the particle swarm. i , let the total number of particles be N i , find a reasonable initial particle swarm and Define the fitness function.

[0144] The target grid refers to a power network formed by connecting power supply points in any partition with important nodes to be restored and containing as few ring networks as possible.

[0145] The particle definition includes: taking a value sequence of whether all lines in the partition are selected to be included in the target grid as a particle, if the line is selected, the corresponding position value of the particle is taken as 1, otherwise it is taken as 0; therefore, a 0 / 1 sequence with a length equal to the total number of lines in the partition constitutes a particle, which can uniquely correspond to one target grid solution; if the line is selected, the nodes at both ends of the line are also selected to be included in the target grid.

[0146] The implementation steps of initializing the particle swarm include:

[0147] First, the nodes are grouped according to their importance, and K-means clustering is used to group them. k K value can get the corresponding N k Then, for each group of results, select the N with the highest average importance. AA specified number of nodes are randomly selected from the clusters as the initial target grid nodes, referred to as initial nodes. The prim algorithm is used to find the minimum tree connecting the initial nodes. In addition to the initial nodes and power nodes, the minimum tree also includes other nodes that connect these initial nodes to form the minimum tree. On this basis, no more than An important line, is the preset value, and a topological check is performed to ensure connectivity and a loop-free network, thereby obtaining a set of initial particles; multiple sets of initial particles can be obtained by randomly selecting initial particles in the same clustering result. Repeating the above operation for each clustering result can obtain different initial particles, and their set is recorded as The important line refers to the line whose importance is greater than the specified threshold value σ V,L The line;

[0148] Furthermore, the electrical distance from the power node is Each particle in the is adjusted one by one and randomly removed The electrical distance from the power node is greater than the specified threshold σ D,N,1 Nodes, is the preset value, adding the same number of electrical distances less than the specified threshold value σ D,N,2 And the importance ranking is greater than the specified threshold value σ V,N Nodes of and update the minimum tree, thereby obtaining another set of initial particles

[0149] The calculation formula of the fitness function of particle swarm optimization is:

[0150]

[0151] Where E is the line number set selected by the particle, G and B are the power generation and non-power generation node number sets selected by the particle respectively; V i ' L 、V j ' G 、V k ' B is the normalized importance of the line, power generation node and non-power generation node; ω L is the line weight; ω G is the power generation node weight; ω B is the weight of non-generation node; ω F is the penalty function weight, the node weight is often higher than the line weight; f p is the penalty function for unreasonable particles.

[0152] Among them, the penalty function f of unreasonable particles p ,include:

[0153] In the iterative optimization process, the target grid corresponding to the particles updated in each round of iteration is subjected to topological analysis, and the connectivity and ring network degree are analyzed. The penalty function of unreasonable particles is calculated based on the analysis results. The calculation formula of the penalty function of unreasonable particles is:

[0154] f p =(N SD -1)+(λ-1)=N SD +λ-2;

[0155] Where N SD , λ is the number of islands and edge connectivity of the target grid corresponding to the particle, which quantitatively represent the degree of disconnection and existence of loop network respectively; analyzing the number of islands of the target grid, including: performing topological analysis on the target grid corresponding to the current particle, if the nodes in the target grid can be connected through the lines in the target grid, they belong to the same island, otherwise they belong to different islands, and counting the total number of islands contained in the target grid; analyzing the edge connectivity of the target grid, including: analyzing the minimum number of edges that need to be deleted to make the target grid no longer connected after treating the target grid as an undirected graph, the number of edges is the edge connectivity, if the target grid is not connected, the edge connectivity takes a value greater than 1.

[0156] Step 6: Particle swarm iterative optimization, in which the penalty function value and fitness function are calculated after each round of particle update.

[0157] Step 7: If the particle evolution reaches the maximum number of iterations or satisfies the minimum error limit of the fitness function value, the optimal particle is found and the particle swarm evolution process ends.

[0158] Step 8: Output the optimal particles in each partition and convert them into the corresponding target grid solution.

[0159] In one embodiment, a computer device is provided. The computer device may be a server, and its internal structure diagram may be as follows: Figure 3 As shown. The computer device includes a processor, a memory and a network interface connected through a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store static information and dynamic information data. The network interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, the steps in the above method embodiment are implemented.

[0160] Those skilled in the art will understand that Figure 3The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present invention, and does not constitute a limitation on the computer device to which the solution of the present invention is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0161] In addition, the present invention also provides a computer device, including a memory and a processor, wherein a computer program is stored in the memory, and the processor implements the steps in the above method embodiment when executing the computer program.

[0162] In addition, the present invention further provides a computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, the steps in the above method embodiment are implemented.

[0163] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided by the present invention can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory or optical memory, etc. Volatile memory can include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM).

[0164] The present invention is not limited to the structures which have been described above and shown in the drawings, and various modifications and changes may be made without departing from the scope thereof. The scope of the present invention is limited only by the appended claims.

Claims

1. A method for optimizing target grid reconstruction of power grid, It is characterized in that include: Obtain power grid operation data; Identify faulty lines based on grid operation data and assess the probability of disasters causing recoverable line failures within a preset time period; The power outage area is divided into several recovery zones, the recoverable states of the simulated lines are sampled in the recovery zones, and the expected values ​​of node importance are calculated based on the recoverable states; The nodes are grouped according to the expected value of node importance, and the optimal particles of each recovery partition are generated in combination with the discrete particle swarm algorithm to optimize the target grid. The optimal particles of each recovery partition are converted into the corresponding target grid solution.

2. The target grid reconstruction optimization method of the power grid according to claim 1, It is characterized in that The power grid operation data includes power grid parameters, line real-time operation data, disaster monitoring data, equipment failure alarm data, equipment maintenance plan data and dispatch plan data; The real-time operation data of the power grid includes the operation status of power grid equipment and the shutdown status of power grid equipment.

3. The target grid reconstruction optimization method of the power grid according to claim 2, It is characterized in that The method of identifying faulty lines based on power grid operation data and evaluating the probability of disasters causing recoverable line failures within a preset period of time includes: Identify faulty lines based on real-time line operation data, equipment fault alarm data, and equipment maintenance plan data; The probability of a restoreable line failure caused by a disaster within a preset time period is calculated in combination with the disaster monitoring data, wherein the probability of the restoreable line failure includes a single failure probability and a group failure probability.

4. The target grid reconstruction optimization method of the power grid according to claim 3, It is characterized in that The method of dividing the power outage area into a plurality of recovery zones, sampling and simulating the recoverable state of the lines in the recovery zones, and calculating the expected value of the node importance based on the recoverable state includes: Based on the real-time operation data of the line, the nodes and faulty lines at the time of power outage are dynamically divided into several recovery partitions, wherein the recovery partitions include at least one power supply node and several fully stopped nodes, and all nodes are connected through recoverable lines; The recovery partition is randomly selected to simulate the recoverable state and recovery operation time of the faulty line, and the expected value of the node importance is calculated based on the recoverable state.

5. The target grid reconstruction optimization method of the power grid according to claim 4, It is characterized in that The method of using a discrete particle swarm algorithm to generate the optimal particles of each recovery partition to optimize the target grid, and converting the optimal particles of each recovery partition into a corresponding target grid solution includes: Incorporate the selected lines in the recovery partition into the value sequence of the target grid as particles, and calculate the initial particle group based on the total number of particles in the recovery partition; Execute particle swarm iterative optimization, perform topological analysis on the target grid corresponding to the particles updated in each round of iteration, and calculate the penalty function value and fitness function value based on the analysis results; If the iteration condition meets the maximum number of iteration steps or the minimum error of the fitness function value, the particle swarm iterative optimization is stopped within a limited time to obtain the optimal particle, and the output optimal particle is converted into the corresponding target grid scheme. Otherwise, the particle swarm iterative optimization is continuously performed until the optimal particle is found.

6. The target grid reconstruction optimization method of the power grid according to claim 5, It is characterized in that The calculating of the initial particle group based on the total number of particles in the recovery partition comprises: The nodes are grouped according to the expected values ​​of node importance to obtain several groups of grouping results, and the nodes with high average expected values ​​of importance are selected from each group of grouping results as initial nodes; Construct a minimum tree connecting the initial nodes, add a preset number of important lines to the minimum tree, perform a topology check, and generate initial particles; Repeat the minimum tree construction process for each clustering result to obtain the initial particles of each clustering result, and aggregate several initial particles to generate the first group of initial particle groups; The electrical distance from the power node is combined with the particle optimization condition to adjust each particle in the first group of initial particle groups, and the minimum tree is updated based on the adjustment result to obtain the second group of initial particle groups.

7. The target grid reconstruction optimization method of the power grid according to claim 6, It is characterized in that The calculation formula of the fitness function is: Where f is the fitness function; E is the line number set selected by the particle; G is the power generation node number set selected by the particle; B is the non-power generation node number set; V i ' L is the normalized expected value of line importance, V j ' G is the normalized expected value of the importance of the power generation node, V k ' B is the normalized importance of non-generation nodes; ω L is the line weight; ω G is the power generation node weight; ω B is the weight of non-generation node; ω F is the penalty function weight; f p is the penalty function value of unreasonable particles.

8. A power grid target grid reconstruction optimization system, It is characterized in that include: Fault probability assessment module, used to obtain power grid operation data; Identify faulty lines based on grid operation data and assess the probability of disasters causing recoverable line failures within a preset time period; The importance evaluation module is used to divide the power outage area into several recovery zones, sample the recoverable state of the simulated line in the recovery zone, and calculate the expected value of the node importance based on the recoverable state; The grid optimization module is used to group nodes according to the expected value of node importance, and generate the optimal particles of each recovery partition in combination with the discrete particle swarm algorithm to optimize the target grid, and convert the optimal particles of each recovery partition into the corresponding target grid solution.

9. The power grid target grid reconstruction optimization system according to claim 8, It is characterized in that The power grid operation data includes power grid parameters, line real-time operation data, disaster monitoring data, equipment failure alarm data, equipment maintenance plan data and dispatch plan data; The real-time operation data of the power grid includes the operation status of power grid equipment and the shutdown status of power grid equipment.

10. The power grid target grid reconstruction optimization system according to claim 9, It is characterized in that The fault probability assessment module includes a fault line identification module and a line fault calculation module, wherein: Fault line identification module, used to identify faulty lines based on real-time line operation data, equipment fault alarm data and equipment maintenance plan data; The line fault calculation module is used to calculate the probability of a disaster causing a recoverable line fault within a preset time period in combination with the disaster monitoring data, wherein the probability of the recoverable line fault includes a single fault probability and a group fault probability.

11. The power grid target grid reconstruction optimization system according to claim 10, It is characterized in that The importance assessment module includes a recovery partition division module and an importance calculation module, wherein: A recovery partition division module is used to dynamically divide the nodes and faulty lines at the time of power outage into several recovery partitions based on the real-time operation data of the lines. The recovery partitions include at least one power supply node and several fully stopped nodes, and all nodes are connected through recoverable lines; The importance calculation module is used to randomly select a recovery partition to simulate the recoverable state and recovery operation time of the faulty line, and calculate the expected value of the node importance based on the recoverable state.

12. The power grid target grid reconstruction optimization system according to claim 11, It is characterized in that The grid optimization module includes an initial particle swarm optimization module, a grid topology analysis module and a target grid solution generation module, wherein: An initial particle swarm optimization module is used to include the selected lines in the recovery partition into the value sequence of the target grid as particles, and calculate the initial particle swarm based on the total number of particles in the recovery partition; The grid topology analysis module is used to perform particle swarm iterative optimization, perform topological analysis on the target grid corresponding to the particles updated in each round of iteration, and calculate the penalty function value and fitness function value based on the analysis results; The target grid scheme generation module is used to stop the particle swarm iterative optimization to obtain the optimal particle if the iteration condition meets the maximum number of iteration steps or the minimum error of the fitness function value, and convert the output optimal particle into the corresponding target grid scheme. Otherwise, the particle swarm iterative optimization is continuously performed until the optimal particle is found.

13. The power grid target grid reconstruction optimization system according to claim 12, It is characterized in that The initial particle swarm optimization module includes an initial node selection module, an initial particle generation module, a first initial particle swarm generation module and a second initial particle swarm generation module, wherein: The initial node selection module is used to group the nodes according to the expected values ​​of node importance to obtain several groups of grouping results, and select the nodes with high average expected values ​​of importance from each group of grouping results as the initial nodes; The initial particle generation module is used to construct a minimum tree connecting the initial nodes, add a preset number of important lines to the minimum tree, perform topology check, and generate initial particles; A first initial particle swarm generation module is used to repeat the minimum tree construction process for each clustering result to obtain the initial particles of each clustering result, and to aggregate several initial particles to generate a first group of initial particle swarms; The second initial particle swarm generation module is used to adjust each particle in the first initial particle swarm by combining the electrical distance from the power node with the particle optimization condition, and obtain the second initial particle swarm after updating the minimum tree based on the adjustment result.

14. The power grid target grid reconstruction optimization system according to claim 13, It is characterized in that The calculation formula of the fitness function is: Where f is the fitness function; E is the line number set selected by the particle; G is the power generation node number set selected by the particle; B is the non-power generation node number set; V i ' L is the normalized expected value of line importance, V j ' G is the normalized expected value of the importance of the power generation node, V k ' B is the normalized importance of non-generation nodes; ω L is the line weight; ω G is the power generation node weight; ω B is the weight of non-generation node; ω F is the penalty function weight; f p is the penalty function value of unreasonable particles.

15. A computer device comprising a memory and a processor, wherein the memory stores a computer program. It is characterized in that When the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.

16. A computer-readable storage medium having a computer program stored thereon, It is characterized in that When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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