Power distribution network structure adjustment method and device, equipment, storage medium and program product

Abstract

The invention relates to a power distribution network structure adjustment method and device, equipment, a storage medium and a program product. The method comprises the following steps: acquiring a topological structure of a target power distribution network, wherein the topological structure is used for describing a connection mode, a node parameter, a line parameter and a load parameter of each node in the target power distribution network; if the topological structure of the target power distribution network meets a preset reconstruction condition, adjusting the topological structure of the target power distribution network based on a minimum spanning tree algorithm to generate a first target topological structure; evaluating the performance of the target power distribution network based on the first target topological structure; and if the performance of the target power distribution network does not meet the preset performance index, continuously adjusting the first target topological structure based on an optimization algorithm to obtain a second target topological structure, and enabling the performance of the target power distribution network based on the second target topological structure to meet the preset performance index. By adopting the method, the reliability of power distribution network structure adjustment can be improved.

Description

Technical Field

[0001] The present application relates to the technical field of distribution network, and in particular to a distribution network structure adjustment method, device, equipment, storage medium and program product. Background Art

[0002] Distribution network structure adjustment is an important tool for distribution network system planning and online management. It changes the topology of the distribution network by controlling the opening and closing status of branch switches and tie switches to achieve optimal operation of the system, thereby reducing the loss of distribution network lines and improving power supply reliability.

[0003] In the prior art, staff generally analyze the distribution network structure and output an adjustment plan, and then adjust the distribution network structure according to the adjustment plan. However, this method relies on the staff's experience and knowledge and cannot guarantee the accuracy of the distribution network structure adjustment plan, that is, the reliability of adjusting the distribution network structure based on experience and knowledge is low. Summary of the invention

[0004] Based on this, it is necessary to provide a distribution network structure adjustment method, device, equipment, storage medium and program product that can improve reliability in response to the above technical problems.

[0005] In a first aspect, the present application provides a method for adjusting the structure of a distribution network, comprising:

[0006] The topological structure of the target distribution network is obtained, and the topological structure is used to describe the connection mode, node parameters, line parameters and load parameters of each node in the target distribution network; if the topological structure of the target distribution network meets the preset reconstruction conditions, the topological structure of the target distribution network is adjusted based on the minimum spanning tree algorithm to generate a first target topological structure; the performance of the target distribution network is evaluated based on the first target topological structure; if the performance of the target distribution network does not meet the preset performance indicators, the first target topological structure is continuously adjusted based on the optimization algorithm to obtain a second target topological structure, and the performance of the target distribution network based on the second target topological structure meets the preset performance indicators.

[0007] In one of the embodiments, the topological structure of the target distribution network is adjusted based on the minimum spanning tree algorithm, and generating a first target topological structure includes: based on the topological structure of the target distribution network, determining the edge weights of the edges connecting each node in the target distribution network, each node being a device in the target distribution network; generating the first target topological structure based on the minimum spanning tree algorithm and each edge weight, the first target topological structure including all nodes and the sum of the edge weights of each node does not exceed a preset weight threshold.

[0008] In one of the embodiments, the performance of a target distribution network is evaluated based on a first target topology structure, including: performing a simulation test on the first target topology structure to determine a test result in the target distribution network; and evaluating the performance of the target distribution network according to the test result, the test result including the power supply reliability, average node voltage deviation, maximum node voltage deviation and flow distribution pass rate of the target distribution network.

[0009] In one of the embodiments, after obtaining the topological structure of the target distribution network, the method also includes: obtaining real-time operating data of the target distribution network; inputting the real-time operating data of the target distribution network into a preset model, and determining whether the topological structure of the target distribution network meets preset reconstruction conditions based on the output result of the preset model.

[0010] In one of the embodiments, the operating data includes voltage, current, power factor and load level, and whether the topological structure of the target distribution network meets the preset reconstruction conditions is determined according to the output result of the preset model, including: if the output result of the preset model is that the state of the target distribution network is abnormal, then the topological structure of the distribution network meets the preset reconstruction conditions; if the output result of the preset model is that the state of the target distribution network is normal, then the topological structure of the distribution network does not meet the preset reconstruction conditions.

[0011] In one of the embodiments, the distribution network structure adjustment method further includes: obtaining user load demand and power supply conditions; and determining a dispatching strategy to adjust the target distribution network structure according to the user load demand and power supply conditions.

[0012] In a second aspect, the present application also provides a distribution network structure adjustment device, comprising:

[0013] An acquisition module is used to acquire the topological structure of the target distribution network, where the topological structure is used to describe the connection mode, node parameters, line parameters and load parameters of each node in the target distribution network;

[0014] A first adjustment module is used to adjust the topology of the target distribution network based on a minimum spanning tree algorithm to generate a first target topology if the topology of the target distribution network meets a preset reconstruction condition;

[0015] An evaluation module, configured to evaluate the performance of a target distribution network based on a first target topology;

[0016] The second adjustment module is used to continuously adjust the first target topology structure based on the optimization algorithm to obtain a second target topology structure if the performance of the target distribution network does not meet the preset performance indicators. The performance of the target distribution network based on the second target topology structure meets the preset performance indicators.

[0017] In one of the embodiments, the first adjustment module is specifically used to determine the edge weight of the connecting edge of each node in the target distribution network based on the topological structure of the target distribution network, and each node is a device in the target distribution network; based on the minimum spanning tree algorithm and each edge weight, a first target topological structure is generated, and the first target topological structure includes all nodes and the sum of the edge weights of each node does not exceed a preset weight threshold.

[0018] In one of the embodiments, the evaluation module is specifically used to perform simulation testing on the first target topology structure to determine the test results in the target distribution network; the performance of the target distribution network is evaluated based on the test results, and the test results include the power supply reliability, average node voltage deviation, maximum node voltage deviation and power flow distribution pass rate of the target distribution network.

[0019] In one of the embodiments, the first adjustment module is also used to obtain real-time operating data of the target distribution network; input the real-time operating data of the target distribution network into a preset model, and determine whether the topological structure of the target distribution network meets the preset reconstruction conditions according to the output result of the preset model.

[0020] In one embodiment, the operating data includes voltage, current, power factor and load level, and the first adjustment module is also used to: if the output result of the preset model is that the target distribution network state is abnormal, then the topology structure of the distribution network meets the preset reconstruction conditions; if the output result of the preset model is that the target distribution network state is normal, then the topology structure of the distribution network does not meet the preset reconstruction conditions.

[0021] In one of the embodiments, the distribution network structure adjustment device also includes a third adjustment module, which is used to obtain user load demand and power supply conditions; based on user load demand and power supply conditions, determine the scheduling strategy to adjust the target distribution network structure.

[0022] In a third aspect, the present application further provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, any of the methods described in the first aspect is implemented.

[0023] In a fourth aspect, the present application further provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements any of the methods described in the first aspect above.

[0024] In a fifth aspect, the present application further provides a computer program product, including a computer program, which, when executed by a processor, implements any of the methods described in the first aspect above.

[0025] The distribution network structure adjustment method, device, equipment, storage medium and program product obtain the topological structure of the target distribution network, where the topological structure is used to describe the connection mode, node parameters, line parameters and load parameters of each node in the target distribution network; if the topological structure of the target distribution network meets the preset reconstruction conditions, the topological structure of the target distribution network is adjusted based on the minimum spanning tree algorithm to generate a first target topological structure; then the performance of the target distribution network is evaluated based on the first target topological structure; if the performance of the target distribution network does not meet the preset performance indicators, the first target topological structure is continuously adjusted based on the optimization algorithm to obtain a second target topological structure, and the performance of the target distribution network based on the second target topological structure meets the preset performance indicators. In this way, the topological structure of the target distribution network is first analyzed, and when the preset reconstruction conditions are met, the target distribution network is adjusted for the first time through the minimum spanning tree algorithm, and the performance of the adjusted distribution network is analyzed to determine whether the structure of the adjusted target distribution network meets the preset performance indicators, and when the preset performance indicators are not met after the first adjustment, a new topological structure is determined through an optimization algorithm based on the first adjustment until a second target topological structure that meets the preset performance indicators is obtained. In this way, the target distribution network topological structure that meets the performance requirements can be obtained, that is, the reliability of the distribution network structure adjustment can be improved in the above manner. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the related technologies, the drawings required for use in the embodiments of the present application or the related technical descriptions will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying creative work.

[0027] Figure 1 An application environment diagram of a distribution network structure adjustment method in an embodiment;

[0028] Figure 2 A schematic diagram of a flow chart of a method for adjusting a distribution network structure in one embodiment;

[0029] Figure 3 A schematic diagram of a flow chart of a step of generating a first target topology structure in one embodiment;

[0030] Figure 4 A schematic flow chart of a step of evaluating the performance of a target power distribution network based on a first target topology structure in one embodiment;

[0031] Figure 5 A schematic diagram of a flow chart of a method for adjusting a distribution network structure in another embodiment;

[0032] Figure 6A schematic diagram of a flow chart of a method for adjusting a distribution network structure in another embodiment;

[0033] Figure 7 A schematic diagram of a flow chart of a method for adjusting a distribution network structure in another embodiment;

[0034] Figure 8 is a structural block diagram of a distribution network structure adjustment device in one embodiment;

[0035] Fig. 9 FIG. 4 is a diagram showing the internal structure of a computer device in one embodiment. DETAILED DESCRIPTION

[0036] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0037] The distribution network structure adjustment method provided in the embodiment of the present application can be applied to Figure 1 In the application environment shown. Among them, the terminal 102 communicates with the server 104 through the network. The data storage system can store the data that the server 104 needs to process. The data storage system can be integrated on the server 104, or it can be placed on the cloud or other network servers. Among them, the terminal 102 includes an operation and maintenance terminal, and the terminal 102 can be but not limited to various personal computers, laptops, smart phones, tablets, Internet of Things devices and portable wearable devices. The Internet of Things devices can be smart speakers, smart TVs, smart air conditioners, smart car devices, projection devices, etc. Portable wearable devices can be smart watches, smart bracelets, head-mounted devices, etc. The head-mounted device can be a virtual reality (VR) device, an augmented reality (AR) device, smart glasses, etc. The server 104 can be an independent physical server, a server cluster or distributed system composed of multiple physical servers, or a cloud server that provides cloud computing services.

[0038] In an exemplary embodiment, Figure 2 As shown, a distribution network structure adjustment method is provided, and the method is applied to Figure 1 The server 104 in the example is used as an example to illustrate, including the following steps 201 to 204. Among them:

[0039] S201, the server obtains the topology structure of the target distribution network.

[0040] Among them, the topological structure is used to describe the connection mode, node parameters, line parameters and load parameters of each node in the target distribution network.

[0041] Optionally, the target distribution network may be a distribution network to be analyzed and adjusted.

[0042] Optionally, the connection mode of each node in the target distribution network may include a radial topology, a segmented connection topology, a ring network topology, an N-supply 1-backup topology, a petal topology, etc.

[0043] Optionally, the node parameters of each node in the target distribution network may include voltage level, phase angle, injection power, etc.

[0044] Optionally, the line parameters of each node in the target distribution network may include a set of circuit equivalent parameters of the electromagnetic characteristics of the power line, including resistance, reactance, conductance and susceptance, etc. The line parameters determine the transmission capacity and efficiency of the target distribution network and affect the stability and safety of the target distribution network.

[0045] Optionally, the load parameters of each node in the target distribution network may include basic parameters such as active power, reactive power and apparent power, as well as dynamic characteristics and static characteristics, wherein active power is the power that actually does work, and is used to measure the actual power consumed by electrical equipment; reactive power is the power associated with the interaction between electromagnetic and magnetic fields, which does not actually do work but is necessary for the normal operation of electromagnetic equipment such as transformers and motors; apparent power is a comprehensive indicator including active power and reactive power; dynamic characteristics may be characteristics that describe the rapid changes in load response when the operating parameters (such as voltage and frequency) of the target distribution network change; and static characteristics may be characteristics that describe the changes in load with operating parameters under steady-state conditions.

[0046] Optionally, the topological structure of the target distribution network can be obtained from a distribution network database, which stores the topological structures of multiple distribution networks and information about the distribution networks. The topological structure of the target distribution network can be obtained from the distribution network database through a query statement using the unique identifier of the target distribution network; the topological structure of the target distribution network can also be obtained from the distribution network structure design file based on the unique identifier of the target distribution network; or, the topological structure of the target distribution network uploaded by the staff from the operation and maintenance terminal can also be received. The method for obtaining the topological structure of the target distribution network is not limited in the embodiments of the present application.

[0047] S202: If the topology structure of the target distribution network meets a preset reconstruction condition, the server adjusts the topology structure of the target distribution network based on a minimum spanning tree algorithm to generate a first target topology structure.

[0048] Optionally, the topology structure of the target distribution network is analyzed, and if the topology structure of the target distribution network meets the preset reconstruction conditions, the topology structure of the target distribution network is adjusted; if the topology structure of the target distribution network does not meet the preset reconstruction conditions, there is no need to adjust the topology structure of the target distribution network.

[0049] Optionally, the minimum spanning tree algorithm can find a subcircle in a weighted connected graph in which all nodes are connected and the total weight is the smallest. The weighted connected graph can be determined based on the topological structure of the target distribution network. For example, the weight value can be determined based on the node parameters, and the connected graph can be determined based on the connection relationship.

[0050] In a possible implementation manner, the topology structure of the target distribution network may be adjusted according to the Prim algorithm in the minimum spanning tree algorithm to generate a first target topology structure.

[0051] In another possible implementation manner, the topology structure of the target distribution network may be adjusted according to the Kruskal algorithm in the minimum spanning tree algorithm to generate a first target topology structure.

[0052] Optionally, the basic principles of the Prim algorithm and the Kruskal algorithm are the same. The Prim algorithm starts from a certain starting vertex and gradually adds vertices and edges, and each time the edge added is the edge with the smallest weight connecting the selected vertex and the unselected vertex; the Kruskal algorithm starts from the edge with the smallest weight and gradually builds a forest until all vertices belong to the same tree. The embodiment of the present application does not limit which minimum spanning tree algorithm is selected to adjust the topological structure of the target distribution network.

[0053] S203: The server evaluates the performance of the target distribution network based on the first target topology structure.

[0054] Optionally, the generated first target topology structure may be evaluated to determine the performance of the target distribution network.

[0055] Optionally, the first target topology structure can be input into a preset evaluation model to obtain an evaluation result, and the performance of the target distribution network can be determined based on the evaluation result. The first target topology structure can also be simulated using simulation software to obtain a simulation result, and the performance of the target distribution network can be determined based on the simulation result.

[0056] Optionally, the evaluation model may be a pre-trained neural network model or a mathematical model, such as a mathematical model based on grey correlation analysis, which is not limited in the embodiments of the present application.

[0057] S204: If the performance of the target distribution network does not meet the preset performance index, the server continuously adjusts the first target topology structure based on the optimization algorithm to obtain a second target topology structure.

[0058] The performance of the target distribution network based on the second target topology structure meets the preset performance index.

[0059] Optionally, when evaluating the first target topology structure, if the evaluation result indicates that the performance of the target distribution network based on the first target topology structure meets the preset performance indicators, the first target topology structure can be used as the adjusted structure of the target distribution network, and the first target topology structure is output; if the evaluation result indicates that the performance of the target distribution network based on the first target topology structure does not meet the preset performance indicators, it is necessary to adjust again on the basis of the first target topology structure until a second target topology structure that meets the preset performance indicators is obtained, and then the second target topology structure is used as the adjusted structure of the target distribution network, and the second target topology structure is output.

[0060] Optionally, after adjusting the first target topology structure based on the optimization algorithm, the adjusted topology structure may be input into a simulation software or an evaluation model to determine the performance of the target distribution network.

[0061] In one possible implementation, the optimization algorithm may be a particle swarm algorithm, a genetic algorithm, or an improved algorithm thereof, which is not limited in the embodiments of the present application.

[0062] Exemplarily, when the first target topology structure is continuously adjusted based on the optimization algorithm, the genetic algorithm can simulate operations such as selection, crossover and mutation in the biological evolution process. Through steps such as individual coding, fitness evaluation, selection operation, crossover operation and mutation operation, the performance of the target distribution network can be used as the objective function, and the optimal solution can be continuously iterated to search for the optimal solution. The optimal solution can be the target distribution network topology structure whose performance is closest to the preset performance indicators.

[0063] In another possible implementation, the optimization algorithm may be a mathematical model that can determine aspects and targets that require improvement based on the evaluation results of the first target topology. For example, it can determine the need to reduce the load on certain nodes, optimize line connections, and improve voltage stability in order to adjust the first target topology. The adjusted topology can then be input into a simulation software or evaluation model to determine the performance of the target distribution network.

[0064] For example, based on the load conditions of the nodes, the load is redistributed to make the loads of each node more balanced; by adjusting the line connection relationship, the length, loss or overload of the line is reduced to improve the power supply efficiency; according to the load demand, the capacity of the transformer and switchgear is adjusted to ensure that it meets the actual demand; according to the load conditions of the nodes and the line connection conditions, the positions of the nodes are rearranged to optimize the power supply effect.

[0065] In the above method, the topological structure of the target distribution network is obtained, and the topological structure is used to describe the connection mode, node parameters, line parameters and load parameters of each node in the target distribution network; if the topological structure of the target distribution network meets the preset reconstruction condition, the topological structure of the target distribution network is adjusted based on the minimum spanning tree algorithm to generate a first target topological structure; then the performance of the target distribution network is evaluated based on the first target topological structure; if the performance of the target distribution network does not meet the preset performance index, the first target topological structure is continuously adjusted based on the optimization algorithm to obtain a second target topological structure, and the performance of the target distribution network based on the second target topological structure meets the preset performance index. In this way, the topological structure of the target distribution network is first analyzed, and when the preset reconstruction condition is met, the target distribution network is adjusted for the first time by the minimum spanning tree algorithm, and the performance of the adjusted distribution network is analyzed to determine whether the structure of the adjusted target distribution network meets the preset performance index, and when the preset performance index requirement is not met after the first adjustment, a new topological structure is determined by the optimization algorithm based on the first adjustment until the second target topological structure that meets the preset performance index requirement is obtained, so that the topological structure of the target distribution network that meets the performance requirement can be obtained, that is, the reliability of the distribution network structure adjustment can be improved by the above method.

[0066] In an exemplary embodiment, Figure 3 As shown, based on the above embodiment, optionally, adjusting the topology structure of the target distribution network based on the minimum spanning tree algorithm, generating the first target topology structure includes the following steps 301 to 302. Among them:

[0067] S301, the server determines the edge weight of each node connection edge in the target distribution network based on the topological structure of the target distribution network.

[0068] Among them, each node is a device in the target distribution network.

[0069] In a possible implementation, the topological structure of the target distribution network may be obtained, and the edge weights of the edges connecting the nodes may be obtained based on the connection relationship of the nodes in the topological structure and the node parameters.

[0070] In another possible implementation, the topological structure of the target distribution network also includes the edge weights of the edges connecting each node. After obtaining the topological structure of the target distribution network, the edge weights of the edges connecting each node in the target distribution network can be determined without further calculation processing.

[0071] S302: The server generates a first target topology structure based on a minimum spanning tree algorithm and the weights of each edge.

[0072] The first target topological structure includes all nodes and the sum of the edge weights of each node does not exceed a preset weight threshold.

[0073] Exemplarily, the topological structure of the preset target distribution network includes the following node and edge information: nodes: A, B, C, D, E; edges: AB, AC, BC, BD, CD, CE, DE; edge weights: AB=5, AC=3, BC=4, BD=6, CD=7, CE=8, DE=9.

[0074] Optionally, taking the Prim algorithm as an example, the process of generating the first target topological structure based on the minimum spanning tree algorithm and the weights of each edge includes: selecting any node as the starting node (selecting node A); marking the starting node A as visited; traversing the edges adjacent to the starting node, selecting the edge with the smallest weight and the connected node that has not been visited (ie, AC); marking node C as visited, and adding edge AC to the minimum spanning tree; continuing to traverse the edges adjacent to the visited nodes, selecting the edge with the smallest weight and the connected node that has not been visited (ie, BC); marking node B as visited, and adding edge BC to the minimum spanning tree; repeating the above steps until all nodes have been visited; the final minimum spanning tree topological structure includes the following edges AC, BC, BD and CE, with a total weight of 21.

[0075] Optionally, in this minimum spanning tree topology, nodes A, B, C, D, and E are interconnected and cover all nodes, and the edge weights are selected to be the path that minimizes the total weight, ensuring that all nodes are covered while having the minimum total line length.

[0076] The above-mentioned topological structure is based on the target distribution network, the edge weights of the edges connecting each node in the target distribution network are determined, and then the first target topological structure is generated based on the minimum spanning tree algorithm and the edge weights. In this way, the target distribution network structure can be preliminarily optimized and adjusted based on the minimum spanning tree algorithm to obtain the first target topological structure. Compared with the method of adjusting the target distribution network structure based on the exhaustive method or the traditional mathematical optimization algorithm, the method in the above embodiment can improve the reliability of the distribution network structure adjustment.

[0077] In an exemplary embodiment, Figure 4 As shown, based on the above embodiment, optionally, evaluating the performance of the target distribution network based on the first target topology structure includes the following steps 401 to 402. Among them:

[0078] S401: The server performs a simulation test on a first target topology structure to determine a test result in a target distribution network.

[0079] Optionally, power system simulation software may be used for simulation and testing to simulate operation under different load and fault conditions and to evaluate the performance of the target distribution network based on the first target topology.

[0080] Optionally, parameters such as power capacity, load demand, power limit, etc. can be set for each node in the first target topology, and impedance and transmission capacity parameters can be set for each line. In addition, the load demand of each node, that is, the power supply required for each node, can be set according to actual conditions, and then simulation tests can be performed.

[0081] Exemplarily, take the first target topology structure with 5 nodes as an example: A, B, C, D, E; when performing simulation tests, node parameters, line parameters, load parameters, loads and fault conditions can be set for the first target topology structure, wherein node parameters: the rated voltage of node A is preset to be 10kV, the rated voltage of node B is preset to be 10kV, the rated voltage of node C is preset to be 10kV, the rated voltage of node D is preset to be 10kV, and the rated voltage of node E is preset to be 10kV; line parameters: the impedance of edge AB is preset to be 0.2Ω, the impedance of edge AC is preset to be 0.3Ω, the impedance of edge BC is preset to be 0.4Ω, the impedance of edge BD is preset to be 0.5Ω, the impedance of edge CD is preset to be 0.6Ω, the impedance of edge CE is preset to be 0.7Ω, and the impedance of edge DE is preset to be 0.8Ω; load parameters: the load of node A is preset to be 300kW, the load of node B is preset to be 400kW, the load of node C is preset to be 200kW, the load of node D is preset to be 350kW, and the load of node E is preset to be 2 50kW; when setting the load and fault conditions, the following two scenarios can be taken as examples: Scenario 1: Setting the load during the daytime working period: setting the load of node A to 300kW, setting the load of node B to 400kW, setting the load of node C to 200kW, setting the load of node D to 350kW, and setting the load of node E to 250kW; Scenario 2: Setting the load during the low-load period at night: setting the load of node A to 200kW, setting the load of node B to 250kW, setting the load of node C to 150kW, setting the load of node D to 300kW, and setting the load of node E to 200kW; Run the simulation model through the settings of scenarios 1 and 2, and calculate the voltage, current, and power loss indicators of the nodes. In scenario 1, the power supply reliability is 99.8%, and the average voltage stability is ±1%. In scenario 2, the power supply reliability is 99.9%, and the average voltage stability is ±0.5%.

[0082] S402: The server evaluates the performance of the target distribution network according to the test results.

[0083] Among them, the test results include the power supply reliability of the target distribution network, the average node voltage deviation, the maximum node voltage deviation and the flow distribution pass rate.

[0084] Optionally, power supply reliability can be to calculate the reliability of the distribution network under a given load condition, and use Monte Carlo simulation or state sampling technology to simulate the operating state of the distribution network and statistically calculate power supply reliability indicators such as average interruption time and system availability.

[0085] Optionally, the node voltage deviation may be calculated according to the voltage value of each node in the test result, and the node voltage deviation index is calculated using the following formula to calculate the average node voltage deviation and the maximum node voltage deviation:

[0086] Average node voltage deviation = (|V1-Vref|+|V2-Vref|+...+|Vn-Vref|) / n

[0087] Maximum node voltage deviation = max(|V1-Vref|, |V2-Vref|, ..., |Vn-Vref|) where Vi is the voltage value of the i-th node, Vref is the reference voltage value, and n is the number of nodes.

[0088] Optionally, the power flow distribution may be performed by calculating a power flow distribution pass rate based on the power flow value of each line in the test result, and setting a threshold to determine whether the test is qualified.

[0089] For example, take the first target topology structure with 5 nodes as an example: A, B, C, D, E; the Monte Carlo simulation method is preset to calculate the power supply reliability, and the power supply reliability indicators such as average interruption time and system availability are statistically calculated by multiple random simulations of the operating status of the distribution network; node voltage values: V1=220V, V2=219V, V3=221V, V4=218V, V5=222V, reference voltage value: Vref=220V, average node voltage deviation=(|V1-Vref|+|V2-Vref|+|V3-Vref|+|V4-Vref|+ |V5-Vref|) / 5=(|220-220|+|219-220|+|221-220|+|218-220|+|222-220|) / 5=1.2V; Maximum node voltage deviation = max(|V1-Vref|, |V2-Vref|, |V3-Vref|, |V4-Vref|, |V5-Vref|)=max(|220-220|, |219-220| 0|, |221-220|, |218-220|, |222-220|)=2V; there are 3 preset lines, and the power flow value of each line is as follows: Line 1: I1=50A, Line 2: I2=45A, Line 3: I3=55A, and the power flow distribution qualified threshold is set to 50A, that is, the line power does not exceed 50A and is considered qualified. The number of qualified lines = (the number of lines that meet the conditions) / (the total number of lines) = 2 / 3≈0.67.

[0090] The test results are: power supply reliability: 99.8%; average node voltage deviation: 1.2V; maximum node voltage deviation: 2V; flow distribution pass rate: 67%. Based on the test results, the performance of the target distribution network was evaluated and it was found that there was an unbalanced load and the load distribution needed to be adjusted to achieve a better balance; node B had a large voltage deviation and the line connection needed to be optimized to reduce voltage loss and fluctuations; the power capacity of node A was insufficient to meet the load demand and the equipment capacity needed to be adjusted.

[0091] Exemplarily, the load conditions can be adjusted, with the load of node A being 110kW, the load of node B being 105kW, the load of node C being 95kW, the load of node D being 100kW, and the load of node E being 90kW; the optimized line parameters are line AB impedance 0.1Ω, line AC impedance 0.2Ω, line BC impedance 0.3Ω, line BD impedance 0.35Ω, line CD impedance 0.4Ω, line CE impedance 0.5Ω, and line DE impedance 0.6Ω; the adjusted equipment capacity is the power capacity of node A being 120kW, the power capacity of node B being 160kW, the power capacity of node C being 140kW, the power capacity of node D being 110kW, and the power capacity of node E being 100kW.

[0092] Optionally, a new round of simulation tests can be performed based on the adjusted first target topology, and optimization and adjustment can be performed according to the test results until the design requirements and expected goals are met. For example, when the test results are power supply reliability of 99.9%, average node voltage deviation of 0.8V, maximum node voltage deviation of 1.5V, and power flow distribution pass rate of 90%, the current topology can be used as the second target topology and output.

[0093] In the above, a simulation test is performed on the first target topology structure to determine the test results in the target distribution network, and the performance of the target distribution network is evaluated according to the test results, wherein the test results include the power supply reliability, average node voltage deviation, maximum node voltage deviation and flow distribution qualification rate of the target distribution network. In this way, multiple indicators are considered when evaluating the performance of the target distribution network, providing a more comprehensive basis for the optimization and adjustment of the target distribution network structure, avoiding the problem of focusing on only some performance indicators and ignoring other important indicators, and further improving the reliability of the distribution network structure adjustment.

[0094] In an exemplary embodiment, Figure 5 As shown, based on the above embodiment, optionally, after obtaining the topological structure of the target distribution network, the distribution network structure adjustment method further includes the following steps 501 to 502. Among them:

[0095] S501, the server obtains real-time operation data of the target distribution network.

[0096] Optionally, real-time operation data may be obtained from an operation database, or by receiving real-time operation data sent by a collection device installed on the target distribution network, which is not limited in the embodiments of the present application.

[0097] S502, the server inputs the real-time operation data of the target distribution network into a preset model, and determines whether the topological structure of the target distribution network meets the preset reconstruction condition according to the output result of the preset model.

[0098] Optionally, after the real-time operation data of the target distribution network is input into the preset model, the real-time operation data is first preprocessed, which may include data cleaning, data normalization and data anomaly processing; and the preprocessed real-time operation data can be subjected to dimensionality reduction and feature extraction through principal component analysis, which can compress important information of the real-time operation data into relatively few features, thereby reducing the amount of calculation of the preset model and improving the calculation efficiency and performance of the preset model.

[0099] Optionally, the preset model can be a classification model trained with historical operating data of the target distribution network. The classification model can be implemented based on algorithms such as logistic regression, naive Bayes, decision tree, support vector machine or random forest. The embodiments of the present application do not limit the specific implementation method of the classification model.

[0100] Optionally, the operating data includes voltage, current, power factor and load level, and determining whether the topology of the target distribution network meets the preset reconstruction condition according to the output result of the preset model includes:

[0101] If the output result of the preset model is that the target distribution network state is abnormal, the topological structure of the distribution network meets the preset reconstruction conditions; if the output result of the preset model is that the target distribution network state is normal, the topological structure of the distribution network does not meet the preset reconstruction conditions.

[0102] Optionally, if the output result of the preset model is that the state of the target distribution network is normal, the topology structure of the distribution network does not meet the preset reconstruction conditions, that is, there is no need to adjust the topology structure of the target distribution network at present; if the output result of the preset model is that the state of the target distribution network is abnormal, the topology structure of the distribution network meets the preset reconstruction conditions, and it is necessary to adjust the topology structure of the target distribution network based on the minimum spanning tree algorithm, generate a first target topology structure, and evaluate the first target topology structure. If the evaluation result indicates that the performance of the target distribution network based on the first target topology structure meets the preset performance indicators, the first target topology structure can be used as the adjusted structure of the target distribution network, and the first target topology structure is output; and if the evaluation result indicates that the performance of the target distribution network based on the first target topology structure does not meet the preset performance indicators, it is necessary to adjust again on the basis of the first target topology structure until a second target topology structure that meets the preset performance indicators is obtained, and then the second target topology structure is used as the adjusted structure of the target distribution network, and the second target topology structure is output.

[0103] The real-time operating data of the target distribution network is obtained, the real-time operating data of the target distribution network is input into a preset model, and whether the topological structure of the target distribution network meets the preset reconstruction conditions is determined according to the output result of the preset model. In this way, the topological structure of the target distribution network that meets the reconstruction conditions is adjusted, which can improve the efficiency and reliability of the distribution network structure adjustment.

[0104] In an exemplary embodiment, Figure 6 As shown, based on the above embodiment, optionally, the method further includes the following steps 601 to 602. Among them:

[0105] S601, the server obtains user load demand and power supply status.

[0106] Optionally, the server can obtain user load demand and power supply conditions by acquiring real-time monitoring data. For example, sensors and detection equipment can be installed to collect real-time operating data of the power system. User load demand can also be obtained through demand response analysis under a time-of-use electricity price environment, identification of air-conditioning flexible load, and research on electricity consumption behavior characteristics based on load characteristics. Among them, demand response analysis can quantify the degree of response of each user to electricity prices and other user demands in different time periods by establishing a user demand response matrix, thereby evaluating the user's electricity consumption habits and behavior models. Air-conditioning flexible load identification can be based on spring and autumn load data to eliminate the impact of extreme data, fit the benchmark load curve within the non-air-conditioning use period, and then obtain the typical load curve based on the characteristics of the summer and winter load curves. The area enclosed by the typical load curve and the benchmark load curve in a specific period of time is solved by definite integrals to calculate the air-conditioning flexible adjustable load range and the corresponding adjustable coefficient.

[0107] S602, the server determines the dispatching strategy according to the user load demand and power supply situation to adjust the target distribution network structure.

[0108] Optionally, the target distribution network structure can be adjusted by adjusting generator output, switching power supply lines, or optimizing the power factor scheduling strategy.

[0109] Optionally, after adjusting the target distribution network structure, the effect of the target distribution network structure adjustment is monitored in real time. For example, the changes in power supply reliability and voltage stability indicators can be monitored, and the corresponding data can be recorded. The dispatching strategy can be adjusted in time according to the monitoring results. Through multiple iterations and optimizations, the dispatching strategy is continuously adjusted to adjust the target distribution network structure until the design requirements and expected goals are met.

[0110] Optionally, according to user load demand and power supply conditions, determining the dispatching strategy to adjust the target distribution network structure may include: calculating the difference between user load demand and power supply conditions, namely the load difference; judging whether the load difference is positive or negative. If the load difference is a positive number, it means that the user load demand is greater than the total power generation capacity, and additional power purchase or increase in generator capacity is required. If the load difference is a negative number, it means that the user load is less than the total power generation capacity, and consideration may be given to reducing generator processing or adjusting the power factor to improve power supply efficiency.

[0111] Optionally, switching of power supply lines may be based on real-time data of the target distribution network. If a line is overloaded or fails, user load demand may be met by switching to a backup line. Power factor optimization may be based on real-time data of the target distribution network. By adjusting the power factor correction device in the power system to make the power factor close to a preset optimal value, the efficiency and stability of the power system may be improved.

[0112] For example, taking the user load demand as 1200KW and the power supply as 1500KW of total generating capacity, the generator output can be adjusted to the user load demand of 1200KW, and the power supply line can be switched and the power factor can be optimized according to the real-time data of the target distribution network.

[0113] The above-mentioned method obtains user load demand and power supply conditions, determines the dispatching strategy according to user load demand and power supply conditions to adjust the target distribution network structure, can timely adjust the structure of the target distribution network according to the real-time data of the target distribution network, can timely respond to real-time changes in load demand and power supply conditions, optimize the power supply quality and efficiency of the distribution network, make power dispatching more flexible, and can make instant adjustments according to actual conditions to improve the reliability of the target distribution network structure adjustment.

[0114] As an optional implementation, Figure 7As shown, the distribution network structure adjustment method provided in the embodiment of the present application may include the following specific steps:

[0115] S701: The server obtains the topology of the target distribution network.

[0116] Among them, the topological structure is used to describe the connection mode, node parameters, line parameters and load parameters of each node in the target distribution network.

[0117] S702, the server obtains real-time operation data of the target distribution network.

[0118] S703, the server inputs the real-time operation data of the target distribution network into a preset model, and determines whether the topological structure of the target distribution network meets the preset reconstruction condition according to the output result of the preset model.

[0119] The operating data includes voltage, current, power factor and load level. If the output result of the preset model is that the target distribution network state is abnormal, the topological structure of the distribution network meets the preset reconstruction conditions; if the output result of the preset model is that the target distribution network state is normal, the topological structure of the distribution network does not meet the preset reconstruction conditions.

[0120] S704: If the topological structure of the target distribution network meets the preset reconstruction condition, the server determines the edge weight of each node connection edge in the target distribution network based on the topological structure of the target distribution network.

[0121] Among them, each node is a device in the target distribution network.

[0122] S705: The server generates a first target topology structure based on a minimum spanning tree algorithm and the weights of each edge.

[0123] The first target topological structure includes all nodes and the sum of the edge weights of each node does not exceed a preset weight threshold.

[0124] S706: The server performs a simulation test on the first target topology structure to determine a test result in the target distribution network.

[0125] S707, the server evaluates the performance of the target distribution network according to the test results.

[0126] Among them, the test results include the power supply reliability of the target distribution network, the average node voltage deviation, the maximum node voltage deviation and the flow distribution pass rate.

[0127] S708: If the performance of the target distribution network does not meet the preset performance index, the server continuously adjusts the first target topology structure based on the optimization algorithm to obtain a second target topology structure.

[0128] The performance of the target distribution network based on the second target topology structure meets the preset performance index.

[0129] S709, the server obtains the user's load demand and power supply status.

[0130] S710, the server determines a dispatching strategy based on user load demand and power supply conditions to adjust the target distribution network structure.

[0131] It should be understood that, although the various steps in the flowcharts involved in the above-mentioned embodiments 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 explanation in this article, the execution of these steps does not have a strict order restriction, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above-mentioned embodiments can include multiple steps or multiple stages, and these steps or stages are not necessarily executed at the same time, but can be executed at different times, and the execution order of these 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 part of the steps or stages in other steps.

[0132] Based on the same inventive concept, the embodiment of the present application also provides a distribution network structure adjustment device for implementing the distribution network structure adjustment method involved above. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme recorded in the above method, so the specific limitations in one or more distribution network structure adjustment device embodiments provided below can refer to the limitations of the distribution network structure adjustment method above, and will not be repeated here.

[0133] In an exemplary embodiment, Figure 8 As shown, a distribution network structure adjustment device 800 is provided, comprising: an acquisition module 801, a first adjustment module 802, an evaluation module 803 and a second adjustment module 804, wherein:

[0134] An acquisition module 801 is used to acquire a topological structure of a target distribution network, where the topological structure is used to describe the connection mode, node parameters, line parameters and load parameters of each node in the target distribution network;

[0135] A first adjustment module 802 is configured to adjust the topology of the target distribution network based on a minimum spanning tree algorithm to generate a first target topology if the topology of the target distribution network meets a preset reconstruction condition;

[0136] An evaluation module 803, configured to evaluate the performance of a target distribution network based on a first target topology structure;

[0137] The second adjustment module 804 is used to continuously adjust the first target topology structure based on the optimization algorithm to obtain a second target topology structure if the performance of the target distribution network does not meet the preset performance indicators. The performance of the target distribution network based on the second target topology structure meets the preset performance indicators.

[0138] In an exemplary embodiment, the first adjustment module 802 is specifically used to determine the edge weight of the connecting edge of each node in the target distribution network based on the topological structure of the target distribution network, where each node is a device in the target distribution network; generate a first target topological structure based on the minimum spanning tree algorithm and each edge weight, and the first target topological structure includes all nodes and the sum of the edge weights of each node does not exceed a preset weight threshold.

[0139] In an exemplary embodiment, the evaluation module 803 is specifically used to perform a simulation test on the first target topology structure to determine the test results in the target distribution network; the performance of the target distribution network is evaluated based on the test results, and the test results include the power supply reliability, average node voltage deviation, maximum node voltage deviation and flow distribution pass rate of the target distribution network.

[0140] In an exemplary embodiment, the first adjustment module 802 is also used to obtain real-time operating data of the target distribution network; input the real-time operating data of the target distribution network into a preset model, and determine whether the topological structure of the target distribution network meets the preset reconstruction conditions according to the output result of the preset model.

[0141] In an exemplary embodiment, the operating data includes voltage, current, power factor and load level. The first adjustment module 802 is also used to: if the output result of the preset model is that the target distribution network state is abnormal, then the topology structure of the distribution network meets the preset reconstruction conditions; if the output result of the preset model is that the target distribution network state is normal, then the topology structure of the distribution network does not meet the preset reconstruction conditions.

[0142] In an exemplary embodiment, the distribution network structure adjustment device 800 also includes a third adjustment module, which is used to obtain user load demand and power supply conditions; based on user load demand and power supply conditions, determine a scheduling strategy to adjust the target distribution network structure.

[0143] Each module in the above-mentioned distribution network structure adjustment device can be implemented in whole or in part by software, hardware and a combination thereof. Each of the above-mentioned 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 the operations corresponding to each of the above modules.

[0144] In an exemplary embodiment, a computer device is provided. The computer device may be a server, and its internal structure diagram may be as shown in FIG. Fig. 9 As shown. The computer device includes a processor, a memory, an input / output interface (Input / Output, referred to as I / O) and a communication interface. The processor, the memory and the input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. 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 data. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication 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, a distribution network structure adjustment method is implemented.

[0145] Those skilled in the art will understand that Fig. 9 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application 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.

[0146] In an exemplary embodiment, a computer device is provided, including a memory and a processor, wherein a computer program is stored in the memory, and the processor implements the following steps when executing the computer program: obtaining a topological structure of a target distribution network, wherein the topological structure is used to describe the connection mode, node parameters, line parameters, and load parameters of each node in the target distribution network; if the topological structure of the target distribution network meets a preset reconstruction condition, adjusting the topological structure of the target distribution network based on a minimum spanning tree algorithm to generate a first target topological structure; evaluating the performance of the target distribution network based on the first target topological structure; if the performance of the target distribution network does not meet a preset performance indicator, continuously adjusting the first target topological structure based on an optimization algorithm to obtain a second target topological structure, wherein the performance of the target distribution network based on the second target topological structure meets the preset performance indicator.

[0147] In one embodiment, when the processor executes the computer program, the following steps are also implemented: based on the topological structure of the target distribution network, the edge weights of the edges connecting each node in the target distribution network are determined, and each node is a device in the target distribution network; based on the minimum spanning tree algorithm and each edge weight, a first target topological structure is generated, and the first target topological structure includes all nodes and the sum of the edge weights of each node does not exceed a preset weight threshold.

[0148] In one embodiment, when the processor executes the computer program, the following steps are also implemented: a simulation test is performed on the first target topology structure to determine the test results in the target distribution network; and the performance of the target distribution network is evaluated based on the test results, and the test results include the power supply reliability, average node voltage deviation, maximum node voltage deviation and power flow distribution qualification rate of the target distribution network.

[0149] In one embodiment, when the processor executes the computer program, the following steps are also implemented: obtaining real-time operating data of the target distribution network; inputting the real-time operating data of the target distribution network into a preset model, and determining whether the topological structure of the target distribution network meets the preset reconstruction conditions based on the output results of the preset model.

[0150] In one embodiment, the operating data includes voltage, current, power factor and load level, and the processor further implements the following steps when executing the computer program: if the output result of the preset model is that the state of the target distribution network is abnormal, the topological structure of the distribution network meets the preset reconstruction conditions; if the output result of the preset model is that the state of the target distribution network is normal, the topological structure of the distribution network does not meet the preset reconstruction conditions.

[0151] In one embodiment, when the processor executes the computer program, the following steps are also implemented: obtaining user load demand and power supply conditions; determining a dispatching strategy based on user load demand and power supply conditions to adjust the target distribution network structure.

[0152] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored, and when the computer program is executed by a processor, the following steps are implemented: obtaining a topological structure of a target distribution network, the topological structure being used to describe the connection mode, node parameters, line parameters and load parameters of each node in the target distribution network; if the topological structure of the target distribution network meets a preset reconstruction condition, adjusting the topological structure of the target distribution network based on a minimum spanning tree algorithm to generate a first target topological structure; evaluating the performance of the target distribution network based on the first target topological structure; if the performance of the target distribution network does not meet a preset performance indicator, continuously adjusting the first target topological structure based on an optimization algorithm to obtain a second target topological structure, and the performance of the target distribution network based on the second target topological structure meets the preset performance indicator.

[0153] In one embodiment, when the computer program is executed by a processor, the following steps are also implemented: based on the topological structure of the target distribution network, the edge weights of the edges connecting each node in the target distribution network are determined, and each node is a device in the target distribution network; based on the minimum spanning tree algorithm and each edge weight, a first target topological structure is generated, and the first target topological structure includes all nodes and the sum of the edge weights of each node does not exceed a preset weight threshold.

[0154] In one embodiment, when the computer program is executed by the processor, the following steps are also implemented: a simulation test is performed on the first target topology structure to determine the test results in the target distribution network; the performance of the target distribution network is evaluated based on the test results, and the test results include the power supply reliability, average node voltage deviation, maximum node voltage deviation and power flow distribution qualification rate of the target distribution network.

[0155] In one embodiment, when the computer program is executed by the processor, the following steps are also implemented: obtaining real-time operating data of the target distribution network; inputting the real-time operating data of the target distribution network into a preset model, and determining whether the topological structure of the target distribution network meets the preset reconstruction conditions based on the output results of the preset model.

[0156] In one embodiment, the operating data includes voltage, current, power factor and load level, and the computer program, when executed by the processor, also implements the following steps: if the output result of the preset model is that the state of the target distribution network is abnormal, the topological structure of the distribution network meets the preset reconstruction conditions; if the output result of the preset model is that the state of the target distribution network is normal, the topological structure of the distribution network does not meet the preset reconstruction conditions.

[0157] In one embodiment, when the computer program is executed by the processor, the following steps are also implemented: obtaining user load demand and power supply conditions; determining a dispatching strategy based on user load demand and power supply conditions to adjust the target distribution network structure.

[0158] In one embodiment, a computer program product is provided, including a computer program, which implements the following steps when executed by a processor: obtaining a topological structure of a target distribution network, the topological structure being used to describe the connection mode, node parameters, line parameters and load parameters of each node in the target distribution network; if the topological structure of the target distribution network meets a preset reconstruction condition, adjusting the topological structure of the target distribution network based on a minimum spanning tree algorithm to generate a first target topological structure; evaluating the performance of the target distribution network based on the first target topological structure; if the performance of the target distribution network does not meet a preset performance indicator, continuously adjusting the first target topological structure based on an optimization algorithm to obtain a second target topological structure, and the performance of the target distribution network based on the second target topological structure meets the preset performance indicator.

[0159] In one embodiment, when the computer program is executed by a processor, the following steps are also implemented: based on the topological structure of the target distribution network, the edge weights of the edges connecting each node in the target distribution network are determined, and each node is a device in the target distribution network; based on the minimum spanning tree algorithm and each edge weight, a first target topological structure is generated, and the first target topological structure includes all nodes and the sum of the edge weights of each node does not exceed a preset weight threshold.

[0160] In one embodiment, when the computer program is executed by the processor, the following steps are also implemented: a simulation test is performed on the first target topology structure to determine the test results in the target distribution network; the performance of the target distribution network is evaluated based on the test results, and the test results include the power supply reliability, average node voltage deviation, maximum node voltage deviation and power flow distribution qualification rate of the target distribution network.

[0161] In one embodiment, when the computer program is executed by the processor, the following steps are also implemented: obtaining real-time operating data of the target distribution network; inputting the real-time operating data of the target distribution network into a preset model, and determining whether the topological structure of the target distribution network meets the preset reconstruction conditions based on the output results of the preset model.

[0162] In one embodiment, the operating data includes voltage, current, power factor and load level, and the computer program, when executed by the processor, also implements the following steps: if the output result of the preset model is that the state of the target distribution network is abnormal, the topological structure of the distribution network meets the preset reconstruction conditions; if the output result of the preset model is that the state of the target distribution network is normal, the topological structure of the distribution network does not meet the preset reconstruction conditions.

[0163] In one embodiment, when the computer program is executed by the processor, the following steps are also implemented: obtaining user load demand and power supply conditions; determining a dispatching strategy based on user load demand and power supply conditions to adjust the target distribution network structure.

[0164] A person of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment method 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 the memory, database or other medium used in the embodiments provided in the present application can include at least one of non-volatile memory and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. 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). The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. Non-relational databases may include distributed databases based on blockchains, etc., but are not limited to this. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, an artificial intelligence (AI) processor, etc., but are not limited to this.

[0165] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this application.

[0166] The above-described embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the present application. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the attached claims.

Claims

1. A method for adjusting the structure of a distribution network, characterized in that: The method comprises: Acquire a topological structure of a target distribution network, wherein the topological structure is used to describe a connection mode, node parameters, line parameters, and load parameters of each node in the target distribution network; If the topological structure of the target distribution network meets the preset reconstruction condition, the topological structure of the target distribution network is adjusted based on a minimum spanning tree algorithm to generate a first target topological structure; evaluating the performance of the target distribution network based on the first target topology; If the performance of the target distribution network does not meet the preset performance indicators, the first target topology structure is continuously adjusted based on the optimization algorithm to obtain a second target topology structure, and the performance of the target distribution network based on the second target topology structure meets the preset performance indicators.

2. The method according to claim 1, characterized in that The step of adjusting the topology structure of the target distribution network based on a minimum spanning tree algorithm to generate a first target topology structure includes: Based on the topological structure of the target distribution network, determining the edge weight of each node connecting the edge in the target distribution network, each of the nodes being a device in the target distribution network; The first target topological structure is generated based on a minimum spanning tree algorithm and each of the edge weights. The first target topological structure includes all nodes and the sum of the edge weights of each node does not exceed a preset weight threshold.

3. The method according to claim 1, characterized in that The evaluating the performance of the target distribution network based on the first target topology structure includes: Performing a simulation test on the first target topology structure to determine a test result on the target distribution network; The performance of the target distribution network is evaluated according to the test results, wherein the test results include power supply reliability, average node voltage deviation, maximum node voltage deviation and power flow distribution qualification rate of the target distribution network.

4. The method according to claim 1, characterized in that: After obtaining the topological structure of the target distribution network, the method further includes: Acquiring real-time operation data of the target distribution network; The real-time operation data of the target distribution network is input into a preset model, and it is determined whether the topological structure of the target distribution network meets a preset reconstruction condition according to an output result of the preset model.

5. The method according to claim 4, characterized in that The operation data includes voltage, current, power factor and load level, and determining whether the topology structure of the target distribution network meets the preset reconstruction condition according to the output result of the preset model includes: If the output result of the preset model is that the state of the target distribution network is abnormal, then the topological structure of the distribution network meets the preset reconstruction condition; If the output result of the preset model is that the target distribution network is in normal state, the topological structure of the distribution network does not meet the preset reconstruction condition.

6. The method according to claim 1, characterized in that The method further comprises: Obtain user load demand and power supply status; According to the user load demand and the power supply situation, a dispatching strategy is determined to adjust the target distribution network structure.

7. A distribution network structure adjustment device, characterized in that: The device comprises: An acquisition module is used to acquire a topological structure of a target distribution network, wherein the topological structure is used to describe a connection mode, node parameters, line parameters and load parameters of each node in the target distribution network; A first adjustment module, configured to adjust the topology of the target distribution network based on a minimum spanning tree algorithm to generate a first target topology if the topology of the target distribution network meets a preset reconstruction condition; An evaluation module, configured to evaluate the performance of the target distribution network based on the first target topology; The second adjustment module is used to continuously adjust the first target topology structure based on an optimization algorithm to obtain a second target topology structure if the performance of the target distribution network does not meet the preset performance indicators, and the performance of the target distribution network based on the second target topology structure meets the preset performance indicators.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 6 are implemented.

9. A computer-readable storage medium having a computer program stored thereon, 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 6 are implemented.

10. A computer program product, comprising a computer program, 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 6 are implemented.

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