Component importance assessment method, device, terminal equipment and storage medium suitable for fault repair

Abstract

The present invention discloses a component importance assessment method, apparatus, terminal device and storage medium suitable for fault repair. When deriving the power supply coefficient of a power supply element, not only its direct power supply capability to the load element is considered, but also the influence of the load element with a high importance value on the power supply reliability of the power supply element is considered. Based on the power supply coefficient, performance parameters and ideal solution set, the importance assessment result of the power supply element is obtained, and then the fault repair priority of the power supply element is obtained. The present invention considers the actual power supply role of the power supply element in the power grid and the power supply reliability to the important load elements, so that the power supply elements with the greatest power supply reliability to the important load elements have a higher corresponding fault repair priority, ensuring that in the event of a power grid fault or load fluctuation, the important load elements related to the power supply elements with high importance can continue to obtain priority power supply, thereby ensuring the overall power supply capacity and reliability of the power grid.

Description

Technical Field

[0001] The present invention relates to the technical field of power grid component processing, and in particular to a component importance assessment method, apparatus, terminal equipment and storage medium suitable for fault repair. Background Art

[0002] During periods of power outages due to faults or other reasons, it is often necessary to inspect, repair, replace or adjust the power generation equipment or power supply devices (such as generators, transformers, circuit breakers, capacitors, renewable energy power generation systems, etc.) in the power grid to restore their normal functions and ensure that the power grid can regain stable power supply.

[0003] Power systems contain numerous power components related to power generation equipment, but maintenance resources (such as manpower, material resources, and time) are limited. Therefore, it is often necessary to assess the importance of power components and prioritize repairs for those most critical to grid stability and power restoration. Furthermore, high-importance components are often key nodes or load centers in the grid, and their failures have the greatest impact on the grid. Therefore, prioritizing repairs on these high-importance components can restore power to critical loads more quickly, reduce the duration and scope of outages, and improve grid reliability and stability.

[0004] However, in the traditional evaluation process of power supply elements, people often only focus on the performance parameters (such as output power, efficiency, etc.) and fault frequency of the power supply elements themselves, while ignoring the actual power supply capacity of the power supply elements to the load elements and the importance of the load elements in the power grid. As a result, the importance evaluation results of the power supply elements cannot fully reflect the actual power supply role of the power supply elements in the power grid and the power supply reliability to important load elements, making it impossible for important load elements to be reflected through the importance evaluation results of the power supply elements. In the event of power grid failure or load fluctuation, those power supply elements that contribute more to the power supply of important load elements cannot be repaired first. That is, the traditional evaluation method cannot sort the fault repair priority of the power supply elements by the power supply reliability of the power supply elements to important load elements, thereby failing to ensure that important load elements related to the highly important power supply elements can continue to obtain priority power supply, and may not be able to restore power supply in a timely and effective manner, resulting in low overall power supply capacity and reliability of the power grid. Summary of the Invention

[0005] The embodiments of the present invention provide a component importance assessment method, apparatus, terminal device and storage medium suitable for fault repair. The importance assessment result of the power supply element can be obtained through the power supply reliability of the power supply element to the important load element, thereby further sorting the fault repair priority of the power supply element, so that the important load elements can continuously obtain priority power supply. It can effectively solve the problem in the prior art that the overall power supply capacity and reliability of the power grid are low due to the inability to ensure that the important load elements can continuously obtain priority power supply.

[0006] An embodiment of the present invention provides a component importance assessment method applicable to fault repair, comprising:

[0007] Obtain the current performance parameters of each power element in the power grid architecture;

[0008] For each power supply element, a power supply coefficient is generated based on the power supply element's current power supply to each load element and the importance of each load element in the power grid architecture; wherein the power supply coefficient is used to characterize the power supply reliability of the power supply element to each load element;

[0009] generating, based on each power supply coefficient and each performance parameter, a first ideal solution set comprising a first positive ideal solution set and a first negative ideal solution set; wherein the first positive ideal solution set comprises: a maximum value among each power supply coefficient and a maximum value among each performance parameter; and the first negative ideal solution set comprises: a minimum value among each power supply coefficient and a minimum value among each performance parameter;

[0010] For each power supply element, a similarity value between the power supply element and the first ideal solution set is generated based on the first ideal solution set, the power supply coefficient corresponding to the power supply element, and the performance parameter corresponding to the power supply element; and the similarity value corresponding to each power supply element is used as the importance value of each power supply element in the power grid architecture;

[0011] Based on the importance values, the current fault repair priorities of the power supply components are ranked from largest to smallest.

[0012] Preferably, generating a first ideal solution set including a first positive ideal solution set and a first negative ideal solution set according to each power supply coefficient and each performance parameter includes:

[0013] Generate a decision matrix based on each power supply coefficient and each performance parameter; wherein column vectors corresponding to different columns in the decision matrix represent different types of indicators; the indicators are power supply coefficients or performance parameters;

[0014] For the column vector corresponding to each column in the decision matrix, fill the column vector corresponding to the maximum value into the first positive ideal solution set;

[0015] For the column vector corresponding to each column in the decision matrix, fill the column vector corresponding to the minimum value into the first negative ideal solution set;

[0016] A first set of ideal solutions is generated according to the first set of positive ideal solutions of the padded data and the first set of negative ideal solutions of the padded data.

[0017] Preferably, generating a similarity value between the power supply element and the first ideal solution set based on the first ideal solution set, the power supply coefficient corresponding to the power supply element, and the performance parameter corresponding to the power supply element includes:

[0018] Generate a parameter matrix corresponding to the power supply element according to the power supply coefficient corresponding to the power supply element and the performance parameters corresponding to the power supply element; wherein the parameter matrix is ​​used to represent the current corresponding operating state of the power supply element;

[0019] generating, according to each element in the first positive ideal solution set and each element in the parameter matrix, a first correlation degree between the power supply element in a current corresponding operating state and the first positive ideal solution set;

[0020] generating, according to each element in the first negative ideal solution set and each element in the parameter matrix, a second correlation degree between the power supply element in the current corresponding operating state and the first negative positive ideal solution set;

[0021] Determining, according to a weight allocation method for characterizing that weight allocation can be performed based on comparison strength between indicators and conflict between indicators, a first weight corresponding to the power supply coefficient among all power supply components and a second weight corresponding to the performance parameter among all power supply components;

[0022] Generate a first information divergence distance value between the power supply element in the current corresponding operating state and the first positive ideal solution set according to the first weight, the second weight, each element in the first positive ideal solution set, and the parameter matrix;

[0023] Generate a second information divergence distance value between the power supply element in the current corresponding operating state and the first negative ideal solution set according to the first weight, the second weight, each element in the first negative ideal solution set, and the parameter matrix;

[0024] A similarity value representing the degree of similarity between the power supply element and the first ideal solution set is generated according to the first correlation degree, the second correlation degree, the first information divergence distance value, and the second information divergence distance value.

[0025] Preferably, the power supply element includes: a new energy element; wherein the new energy element is used to characterize the equipment using renewable energy to generate electricity in the power grid architecture; the performance parameters corresponding to the new energy element include: an equivalent utilization coefficient of a wind farm and an equivalent utilization coefficient of a photovoltaic power station;

[0026] The generating of a decision matrix according to each power supply coefficient and each performance parameter includes:

[0027] The power supply coefficient, wind farm equivalent utilization coefficient and photovoltaic power station equivalent utilization coefficient are used as different types of indicators;

[0028] Each power supply coefficient, each wind farm equivalent utilization coefficient, and each photovoltaic power station equivalent utilization coefficient are respectively used as column vectors corresponding to different columns to generate a corresponding decision matrix.

[0029] Preferably, the power supply element includes: a non-new energy element; wherein the non-new energy element is used to represent a device that uses non-renewable energy to generate electricity in the power grid architecture; the performance parameters corresponding to the non-new energy element include: unit spare capacity and unit ramp rate;

[0030] The generating of a decision matrix according to each power supply coefficient and each performance parameter includes:

[0031] The power supply coefficient, unit spare capacity and unit ramp rate are used as different types of indicators;

[0032] The power supply coefficient of each power source, the spare capacity of each unit and the ramp rate of each unit are respectively used as column vectors corresponding to different columns to generate the corresponding decision matrix.

[0033] Preferably, the generation of the importance value of the load element in the power grid architecture includes:

[0034] For each load element, obtain a clustering coefficient and a centrality coefficient of the load element; wherein the clustering coefficient is used to characterize the degree of clustering between the load element and adjacent load elements; and the centrality coefficient is used to characterize the degree of closeness between the load element and other load elements in the power grid architecture except the load element.

[0035] For each load element, generating a topology coefficient for characterizing the topological characteristics of the load element in the power grid architecture according to the clustering coefficient and the centrality coefficient;

[0036] For each load element, the power outage loss value corresponding to the load element is generated based on the unit cost of power outage loss corresponding to different types of users in the power grid architecture and the current power consumption of each user;

[0037] Generate a second ideal solution set including a second positive ideal solution set and a second negative ideal solution set based on the topology coefficients of the load elements and the power outage loss values ​​of the load elements; wherein the second positive ideal solution set includes: the maximum value of the topology coefficients and the maximum value of the power outage loss values; and the second negative ideal solution set includes: the minimum value of the topology coefficients and the minimum value of the power outage loss values;

[0038] For each load element, generating a similarity value between the load element and the second ideal solution set according to the second ideal solution set, the topology coefficient corresponding to the load element, and the power outage loss value corresponding to the load element;

[0039] The similarity value corresponding to each load element is used as the importance value of each load element in the power grid architecture.

[0040] Preferably, the current generation of the power supply power provided by the power supply element to each load element includes:

[0041] Obtaining current wind speed data, light intensity, and grid operating parameters; wherein the grid operating parameters include resistance, reactance, susceptance, transformer ratio, generator parameters, and load parameters;

[0042] Performing power flow calculations on wind speed data, light intensity, and grid operating parameters to generate power flow calculation results that characterize the power distribution of the grid architecture; wherein the power flow calculation results include: the injected power of each node, the power of each load element, and the output power of each power element;

[0043] Generating a downstream distribution matrix based on the injected power of each node and the power of each load element in the power flow calculation result; wherein the downstream distribution matrix is ​​used to represent the flow path of power in the power grid architecture;

[0044] generating a power distribution coefficient matrix between the power supply element and the load element based on the downstream distribution matrix, the injected power of each node, the power of each load element, and the output power of the power supply element;

[0045] The current power supplied by each power supply element to each load element is generated according to the power distribution coefficient matrix and the power of each load element.

[0046] Based on the above method embodiments, the present invention provides corresponding device embodiments.

[0047] An embodiment of the present invention provides a component importance evaluation device suitable for fault repair, comprising: a performance parameter acquisition module, a power supply coefficient generation module, an ideal solution set generation module, an importance value evaluation module, and a fault repair priority generation module;

[0048] The performance parameter acquisition module is used to obtain the current performance parameters corresponding to each power supply element in the power grid architecture;

[0049] The power supply coefficient generation module is configured to generate a power supply coefficient for each power supply element based on the power supply element's current power supply to each load element and the importance of each load element in the power grid architecture; wherein the power supply coefficient is used to characterize the power supply reliability of the power supply element to each load element;

[0050] The ideal solution set generation module is configured to generate a first ideal solution set including a first positive ideal solution set and a first negative ideal solution set based on each power supply coefficient and each performance parameter; wherein the first positive ideal solution set includes: the maximum value of each power supply coefficient and the maximum value of each performance parameter; and the first negative ideal solution set includes: the minimum value of each power supply coefficient and the minimum value of each performance parameter;

[0051] The importance value evaluation module is configured to generate, for each power supply element, a similarity value between the power supply element and the first ideal solution set based on the first ideal solution set, a power supply coefficient corresponding to the power supply element, and a performance parameter corresponding to the power supply element; and use the similarity value corresponding to each power supply element as the importance value of each power supply element in the power grid architecture;

[0052] The fault repair priority generation module is used to sort the current fault repair priority of each power supply component in descending order based on each importance value.

[0053] Based on the above method embodiments, the present invention provides corresponding terminal device embodiments.

[0054] Another embodiment of the present invention provides a terminal device, including a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, it implements a component importance assessment method suitable for fault repair as described in the above-mentioned embodiment of the invention.

[0055] Based on the above method embodiment, the present invention provides a corresponding storage medium embodiment.

[0056] Another embodiment of the present invention provides a storage medium, wherein the computer-readable storage medium includes a stored computer program, wherein when the computer program is running, the device where the computer-readable storage medium is located is controlled to execute a component importance assessment method suitable for fault repair as described in the above-mentioned embodiment of the invention.

[0057] The following beneficial effects are achieved by implementing the present invention:

[0058] Embodiments of the present invention provide a component importance assessment method, apparatus, terminal device, and storage medium suitable for fault repair. The present invention assesses the importance of a power supply element in a power grid architecture based not only on the current performance parameters corresponding to each power supply element, but also on a power supply coefficient, which characterizes the power supply reliability of the power supply element to each load element. When deriving the power supply reliability of a power supply element to each load element, the power supply coefficient can be derived based on the power supply element's current power supply to each load element and the importance of each load element in the power grid architecture. Because the importance of a load element in a power grid reflects its impact on the overall operation and power supply stability of the grid, the present invention, by introducing this factor, ensures that when assessing the power supply reliability of a power supply element, not only the power supply capability of the load element directly but also the impact of load elements with high importance values ​​on the power supply reliability of the power supply element are considered. This allows subsequent assessment of the importance of a power supply element based on its power supply reliability, i.e., based on the power supply coefficient, to identify important load elements. Ultimately, based on the power supply coefficient, performance parameters, and an ideal solution set, an importance assessment result for the power supply element can be derived, thereby determining the fault repair priority of the power supply element. Compared with the prior art, the importance evaluation result of the power supply element finally obtained by the present invention can reflect the actual power supply role of the power supply element in the power grid and the power supply reliability to important load elements. Therefore, the importance of those power supply elements with greater power supply reliability to important load elements can be made higher, and their corresponding fault repair priority can be higher, ensuring that when the power grid fails or the load fluctuates, the important load elements related to the power supply elements with high importance can continue to obtain priority power supply, realize timely and effective power supply restoration, and ensure the overall power supply capacity and reliability of the power grid. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] Figure 1 The figure is a flow chart of a component importance evaluation method applicable to fault repair provided by one embodiment of the present invention.

[0060] Figure 2 This is a flow chart of power importance assessment provided by another embodiment of the present invention.

[0061] Figure 3 The figure is a schematic structural diagram of a component importance evaluation device suitable for fault repair provided by one embodiment of the present invention. DETAILED DESCRIPTION

[0062] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0063] like Figure 1 FIG. 1 is a flow chart of a component importance assessment method for fault repair provided by an embodiment of the present invention. The component importance assessment method for fault repair includes:

[0064] Step S1: Obtain the current performance parameters corresponding to each power supply component in the power grid architecture;

[0065] Step S2: For each power supply element, a power supply coefficient is generated based on the power supply element's current power supply to each load element and the importance of each load element in the power grid architecture; wherein the power supply coefficient is used to characterize the power supply reliability of the power supply element to each load element;

[0066] Step S3: Generating a first ideal solution set including a first positive ideal solution set and a first negative ideal solution set based on each power supply coefficient and each performance parameter; wherein the first positive ideal solution set includes: the maximum value of each power supply coefficient and the maximum value of each performance parameter; and the first negative ideal solution set includes: the minimum value of each power supply coefficient and the minimum value of each performance parameter;

[0067] Step S4: For each power supply element, a similarity value between the power supply element and the first ideal solution set is generated based on the first ideal solution set, the power supply coefficient corresponding to the power supply element, and the performance parameter corresponding to the power supply element; the similarity value corresponding to each power supply element is used as the importance value of each power supply element in the power grid architecture;

[0068] Step S5: Based on the importance values, the current fault repair priorities of the power supply components are sorted in descending order.

[0069] In step S1, in a preferred embodiment, the power supply element includes: a new energy element; and the new energy element is used to represent a device that uses renewable energy to generate electricity in the power grid architecture; and the performance parameters corresponding to the new energy element include: an equivalent utilization coefficient of a wind farm and an equivalent utilization coefficient of a photovoltaic power station;

[0070] In a preferred embodiment, the power supply element includes: a non-new energy element; and the non-new energy element is used to represent a device that uses non-renewable energy to generate electricity in the power grid architecture; the performance parameters corresponding to the non-new energy element include: unit spare capacity and unit ramp rate;

[0071] Therefore, the present invention can evaluate the corresponding importance values ​​of the new energy components and non-new energy components in the power grid architecture respectively, and thus sort the new energy components and non-new energy components respectively to determine the fault repair priorities.

[0072] It can be understood that when the power supply element is a new energy element, the performance parameters obtained are: the equivalent utilization coefficient of the wind farm and the equivalent utilization coefficient of the photovoltaic power station.

[0073] When the power supply element is a new energy element, the performance parameters obtained are: unit standby capacity and unit ramp rate.

[0074] For step S2, in a preferred embodiment, output data of power sources in the grid, parameter information of new energy power sources, wind speed, and light intensity information can be obtained, a point estimation method is used to calculate the power flow of the new energy power system, and a power flow tracking algorithm is used to calculate the power distribution relationship between the load elements and the power sources. After obtaining the current power supply of the power source element to each load element based on the power distribution relationship between the load elements and the power sources, the comprehensive power supply coefficient of the power source element is calculated based on the importance of the load element and the power supply of the power source element to the load element.

[0075] The product of the power supply of each power generation equipment to different load elements and the importance of the load elements is defined as the comprehensive power supply coefficient of the power element, which is calculated as follows:

[0076]

[0077] Where G i represents the power supply coefficient of power supply element i; n and n L Respectively represent the number of new energy components and the number of load components in the grid; ω v It represents the weight of the vth group of power flow calculation results in the 2n+1th group of power flow results calculated by the point estimation method; represents the importance of load element j; It represents the power supplied by power supply element i to load element j in the vth group of power flow calculation results.

[0078] Illustratively, in a preferred embodiment, before calculating the power supply coefficient, the present invention can also separately calculate the power supply currently provided by the power supply element to each load element, and the importance value of each load element in the power grid architecture.

[0079] First, the calculation process of the power supply element's current power supply to each load element can be:

[0080] Obtaining current wind speed data, light intensity, and grid operating parameters; wherein the grid operating parameters include resistance, reactance, susceptance, transformer ratio, generator parameters, and load parameters;

[0081] Performing power flow calculations on wind speed data, light intensity, and grid operating parameters to generate power flow calculation results that characterize the power distribution of the grid architecture; wherein the power flow calculation results include: the injected power of each node, the power of each load element, and the output power of each power element;

[0082] Generating a downstream distribution matrix based on the injected power of each node and the power of each load element in the power flow calculation result; wherein the downstream distribution matrix is ​​used to represent the flow path of power in the power grid architecture;

[0083] generating a power distribution coefficient matrix between the power supply element and the load element based on the downstream distribution matrix, the injected power of each node, the power of each load element, and the output power of the power supply element;

[0084] The current power supplied by each power supply element to each load element is generated according to the power distribution coefficient matrix and the power of each load element.

[0085] Specifically, the steps for power flow calculation based on the point estimation method are as follows:

[0086] ① Take wind speed and light intensity as the original data X1, X2, ..., X n .

[0087] ② Obtain the input variable X i The expected value μ i , standard deviation σ i , skewness coefficient λ i,3 and kurtosis coefficient λ i,4 , and obtain the position metric ξ according to the point estimation method i,k , the weight p of each estimated point i,k , estimated point value x i,k .

[0088] ③Establish the evaluation vector matrix P of the point estimation method.

[0089] ④ Initialize the data. Input the power system parameters required by the Newton-Raphson method, including resistance R, reactance X, susceptance B, transformer ratio k, generator parameters, load parameters, etc.

[0090] ⑤ Evaluate the matrix P according to the point estimation method, and perform deterministic power flow calculation on each row of the matrix P using the Newton-Raphson method.

[0091] ⑥ Calculate 2n+1 power flow calculation results and corresponding weights to generate power flow calculation results. The power flow calculation results may include: the injected power of each node, the power of each load element, and the output power of each power element;

[0092] Furthermore, for the power flow calculation results, the power distribution relationship between each generator and each load is determined through power flow tracking analysis and calculation, and then:

[0093] The first step is to establish the downstream distribution matrix. Assume that the grid architecture has n nodes and the total node injection power of each node is P Ti , then:

[0094]

[0095] Where, P Li represents the load power of node i; P ij represents the active power transmitted from node i to node j; J(i) represents the set of nodes connected to node i.

[0096] Based on the formula of the total injected power of the node, the downstream distribution matrix A between nodes can be constructed:

[0097]

[0098] A=(a ij ) n×n ;

[0099] Among them, the downstream distribution matrix A is used to characterize the power flow relationship between network nodes, and it can be used to obtain the power distribution relationship between power source and load, power source and line, and line and load in the network.

[0100] According to the properties of the downstream distribution matrix A:

[0101] AP T =P L ;

[0102] P T =P TT E;

[0103] P G =P GG E;

[0104] P TT =diag(P T1 ,P T2 ,...,P Tn );

[0105] P GG =diag(P G1 ,P G2 ,...,P Gn );

[0106] Where, P T =[P T1 ,P T2 ,...,P Tn ] T represents the node injection power column vector; P L =[P L1 ,P L2 ,...,P Ln ] T represents the load power column vector; P G =[P G1 ,P G2 ,...,P Gn ] T represents the generator active output column vector; E=[1,1,...,1] represents the n-dimensional unit column vector, and we can get:

[0107] P G =P GG (P TT ) -1 A -1 P L ;

[0108] K G =(k Gij ) n×n =P GG (P TT ) -1 A -1 ;

[0109] Where K G Represents the power distribution coefficient matrix of the generator node to the load node;

[0110] Then we have:

[0111] Where K Gij P Lj Indicates the power distribution relationship between generator node i and load node j, and P Gi It is the power currently supplied by the power supply element to each load element.

[0112] Furthermore, the present invention can also generate the importance value of the load element in the power grid architecture, specifically including:

[0113] For each load element, obtain a clustering coefficient and a centrality coefficient of the load element; wherein the clustering coefficient is used to characterize the degree of clustering between the load element and adjacent load elements; and the centrality coefficient is used to characterize the degree of closeness between the load element and other load elements in the power grid architecture except the load element.

[0114] For each load element, generating a topology coefficient for characterizing the topological characteristics of the load element in the power grid architecture according to the clustering coefficient and the centrality coefficient;

[0115] For each load element, the power outage loss value corresponding to the load element is generated based on the unit cost of power outage loss corresponding to different types of users in the power grid architecture and the current power consumption of each user;

[0116] Generate a second ideal solution set including a second positive ideal solution set and a second negative ideal solution set based on the topology coefficients of the load elements and the power outage loss values ​​of the load elements; wherein the second positive ideal solution set includes: the maximum value of the topology coefficients and the maximum value of the power outage loss values; and the second negative ideal solution set includes: the minimum value of the topology coefficients and the minimum value of the power outage loss values;

[0117] For each load element, generating a similarity value between the load element and the second ideal solution set according to the second ideal solution set, the topology coefficient corresponding to the load element, and the power outage loss value corresponding to the load element;

[0118] The similarity value corresponding to each load element is used as the importance value of each load element in the power grid architecture.

[0119] Specifically, the topological coefficient of the load element can be calculated according to the following formula:

[0120]

[0121] Among them, R i is the topological coefficient of the load element, C max and J max are the maximum values ​​of load aggregation coefficient and centrality coefficient respectively; C i is the aggregation coefficient of the load element, J i is the centrality coefficient, also known as the load element proximity to centrality.

[0122] The topological characteristics of load elements in the power network need to be considered from two perspectives: their location information in the grid and the degree of their connection with other nodes. The present invention can determine the topological characteristics of load elements through the clustering coefficient and the proximity centrality of the load element (i.e., the centrality coefficient); schematically, the clustering coefficient represents the degree of clustering of neighbor node information and load element nodes, and the centrality coefficient reflects the closeness of the connection between the node and the rest of the nodes in the network.

[0123] Since different industries (i.e., users) have significantly different sensitivities to losses caused by power outages, for example, power outages in the semiconductor and pharmaceutical industries may cause significantly higher losses than other industries, the present invention can generate the current power outage loss value corresponding to the load element based on the unit cost of power outage losses corresponding to different types of users in the power grid architecture and the current power consumption of each user before evaluating the importance value of the load element in the power grid architecture.

[0124] Therefore, by introducing the clustering coefficient and centrality coefficient, the present invention can quantify the local and global importance of load elements in the power grid. This can be further combined with the load element's topological coefficient (reflecting its location in the grid and its degree of connection with other nodes) and the power outage loss value (calculated based on the unit cost of power outage losses and electricity consumption for different types of users), providing a comprehensive perspective for assessing the importance of load elements. This not only considers the physical location and network structure of the load element, but also its impact on the economic efficiency of the power grid, thereby more accurately reflecting the actual role of the load element in the power grid.

[0125] And by introducing the concepts of the second positive ideal solution set and the second negative ideal solution set, the importance of the load element is evaluated by comparing the topological coefficient and power outage loss value of the load element with the degree of proximity to these ideal solution sets, making the comparison between different load elements more fair and accurate, and thus being able to identify key load elements more quickly, achieving an accurate assessment of the importance of the load elements, and providing more accurate load element properties for the subsequent calculation of the power supply coefficient.

[0126] Regarding step S3, in a preferred embodiment, generating a first ideal solution set including a first positive ideal solution set and a first negative ideal solution set according to each power supply coefficient and each performance parameter includes:

[0127] Generate a decision matrix based on each power supply coefficient and each performance parameter; wherein column vectors corresponding to different columns in the decision matrix represent different types of indicators; the indicators are power supply coefficients or performance parameters;

[0128] For the column vector corresponding to each column in the decision matrix, fill the column vector corresponding to the maximum value into the first positive ideal solution set;

[0129] For the column vector corresponding to each column in the decision matrix, fill the column vector corresponding to the minimum value into the first negative ideal solution set;

[0130] A first set of ideal solutions is generated according to the first set of positive ideal solutions of the padded data and the first set of negative ideal solutions of the padded data.

[0131] Specifically, taking the power supply coefficient and performance parameters as different indicators, the corresponding decision matrix generated according to the different indicators can be:

[0132]

[0133] Where n L represents the number of load elements, and m represents the number of indicators;

[0134] Column vector in a matrix Indicates the dth index value vector of each element. Furthermore, it is possible to perform index normalization and standardization to determine whether each index is an extremely large index or an extremely small index. If an index is an extremely small index, it is converted to an extremely large index. In the present invention, all the indicators are extremely large indicators.

[0135] When it is determined that the power supply element is a new energy element, a decision matrix is ​​generated according to each power supply coefficient and each performance parameter, including:

[0136] The power supply coefficient, wind farm equivalent utilization coefficient and photovoltaic power station equivalent utilization coefficient are used as different types of indicators;

[0137] Each power supply coefficient, each wind farm equivalent utilization coefficient, and each photovoltaic power station equivalent utilization coefficient are respectively used as column vectors corresponding to different columns to generate a corresponding decision matrix.

[0138] When it is determined that the power supply element is not a new energy element, that is, a conventional power supply, a decision matrix is ​​generated according to each power supply coefficient and each performance parameter, including:

[0139] The power supply coefficient, unit spare capacity and unit ramp rate are used as different types of indicators;

[0140] The power supply coefficient of each power source, the spare capacity of each unit and the ramp rate of each unit are respectively used as column vectors corresponding to different columns to generate the corresponding decision matrix.

[0141] Therefore, the embodiment of the present invention can generate different decision matrices according to the type of power supply element (new energy element or conventional power supply) when processing the importance evaluation of power supply elements. Different types of power supply elements have different characteristics and performance parameters. By generating different decision matrices, the evaluation results can be more applicable to different types of power supply elements.

[0142] Furthermore, the positive ideal solution set and the negative ideal solution set are determined:

[0143]

[0144] Where, is the set of positive ideal solutions; is the set of negative ideal solutions; x cd The elements in row c and column d of the decision matrix represent the positive and negative ideal solutions, i.e., the best and worst solution sets. Putting all the largest indicators into a list forms a positive ideal solution set, and putting all the smallest indicators into a list forms a negative ideal solution set.

[0145] Therefore, the positive ideal solution set contains the maximum values ​​of all indicators (power supply coefficient and performance parameters), representing the optimal state that the power supply component can theoretically achieve. The negative ideal solution set contains the minimum values ​​of all indicators, representing the worst state that the power supply component can theoretically achieve.

[0146] By generating these two sets, a clear benchmark can be provided for evaluating the importance of power components, making the evaluation results comparable and objective. For example, embodiments of the present invention can use an ideal solution to obtain positive and negative ideal sets, thereby evaluating the importance of power components in the power grid architecture.

[0147] Regarding step S4, in a preferred embodiment, generating a similarity value between the power supply element and the first ideal solution set based on the first ideal solution set, the power supply coefficient corresponding to the power supply element, and the performance parameter corresponding to the power supply element includes:

[0148] Generate a parameter matrix corresponding to the power supply element according to the power supply coefficient corresponding to the power supply element and the performance parameters corresponding to the power supply element; wherein the parameter matrix is ​​used to represent the current corresponding operating state of the power supply element;

[0149] generating, according to each element in the first positive ideal solution set and each element in the parameter matrix, a first correlation degree between the power supply element in a current corresponding operating state and the first positive ideal solution set;

[0150] generating, according to each element in the first negative ideal solution set and each element in the parameter matrix, a second correlation degree between the power supply element in the current corresponding operating state and the first negative positive ideal solution set;

[0151] Determining, according to a weight allocation method for characterizing that weight allocation can be performed based on comparison strength between indicators and conflict between indicators, a first weight corresponding to the power supply coefficient among all power supply components and a second weight corresponding to the performance parameter among all power supply components;

[0152] Generate a first information divergence distance value between the power supply element in the current corresponding operating state and the first positive ideal solution set according to the first weight, the second weight, each element in the first positive ideal solution set, and the parameter matrix;

[0153] Generate a second information divergence distance value between the power supply element in the current corresponding operating state and the first negative ideal solution set according to the first weight, the second weight, each element in the first negative ideal solution set, and the parameter matrix;

[0154] A similarity value representing the degree of similarity between the power supply element and the first ideal solution set is generated according to the first correlation degree, the second correlation degree, the first information divergence distance value, and the second information divergence distance value.

[0155] Finally, based on the similarity value corresponding to each power supply component, the importance value of each power supply component in the power grid architecture can be obtained.

[0156] It is understood that the present invention comprehensively reflects the current operating status of power supply components by comprehensively considering the power supply coefficient and performance parameters and generating a parameter matrix corresponding to the power supply components. Furthermore, the introduction of a first positive ideal solution set and a first negative ideal solution set, along with their associated correlation and information divergence distance values, makes the evaluation results more accurate and more detailed in characterizing the difference between the power supply components and their ideal states. Schematically, both the first correlation and the second correlation are gray correlation.

[0157] Among them, a weight distribution method is also adopted, that is, the weights of the power supply coefficient and performance parameters can be determined according to the comparison intensity and conflict between the indicators, which can ensure that the importance of different indicators in the evaluation is reasonably reflected and avoid the situation where a single indicator dominates the evaluation results.

[0158] In a preferred embodiment, the present invention may adopt the CRITIC method, an objective weighting method for a multi-criteria evaluation system, whose full name is "Criteria Importance Through Intercriteria Correlation", and the present invention adopts the Gini coefficient and the Kendall correlation coefficient to represent the indicator contrast and the conflict between indicators, respectively.

[0159] Then the Gini coefficient of the dth indicator is as follows:

[0160]

[0161] Where n L Indicates the number of load elements, Represents the average index value of the d-th index.

[0162] The Kendall correlation coefficient between the dth indicator and the remaining indicators is as follows:

[0163]

[0164] Where Tau dt The Kendall coefficient between the dth index and the tth index; Tau d represents the total Kendall coefficient of the dth indicator and other indicators; M is the smaller value of the row and column of the matrix; C represents the number of consistent elements in the matrix composed of the two indicators, that is, (x cd >x kd , x ct >x kt ) or (x cd <x kd , x ct <x kt ); D represents the number of inconsistency elements in the matrix composed of the two indicators, that is, (x cd >x kd , x ct <x kt ) or (x cd <x kd , x ct >x kt ).

[0165] In principle, the Gini coefficient measures the contrast between indicators, that is, the difference in the values ​​of an indicator between different scenarios. The larger the Gini coefficient, the greater the contribution of the indicator in the evaluation.

[0166] The Kendall correlation coefficient is used to measure the conflict between indicators. The greater the positive correlation between two indicators, the smaller the conflict, that is, the more reproducible the information they provide in the evaluation.

[0167] The CRITIC method can combine the Gini coefficient and the Kendall correlation coefficient to calculate the weight of each indicator:

[0168]

[0169] Among them, ω d is the weight of the dth indicator.

[0170] Furthermore, the embodiment of the present invention can calculate the operating scheme of the power supply element in the current corresponding operating state and the distance from the positive and negative ideal solutions through information divergence, and take into account various complex direct or indirect coupling relationships in the grid, introduce gray correlation to express the difference between the operating scheme of the power supply element in the current corresponding operating state and the positive and negative ideal solutions, thereby obtaining the closeness of the operating scheme of the power supply element in the current corresponding operating state to the positive and negative ideal solutions (that is, the above-mentioned similarity value). The higher the closeness, the higher the importance of the power supply element.

[0171] Then we have:

[0172]

[0173] Where, and They respectively represent the information divergence distance values ​​between the operating scheme i of the power supply component in the current corresponding operating state and the positive and negative ideal solutions.

[0174] Introducing grey correlation to express the difference between the load scheme and the positive and negative ideal solutions, we have:

[0175]

[0176] Where h id ± It represents the grey correlation coefficient between the operation scheme i and the dth index of the power supply component under the current corresponding operation state; H i ± represents the grey relational degree of running scheme i (i.e. the first relational degree and the second relational degree); ρ is the resolution coefficient, which can be 0.5.

[0177] Finally, by combining the information divergence value and the grey correlation degree, the degree of fit between the operation scheme of each power supply component under the current corresponding operation state and the positive and negative ideal solutions is calculated, thereby obtaining the comprehensive closeness between each power supply component and the ideal solution. This comprehensive closeness is used as the similarity value between the power supply component and the first ideal solution set, and then:

[0178]

[0179] Where, Indicates the comprehensive closeness, which is used to indicate the importance of each power supply component. The larger the value, the greater the importance of the power supply component.

[0180] By calculating the distance between the power supply element operation plan and the positive and negative ideal solutions through information divergence, the gap between the current state of the power supply element and the ideal state can be quantitatively reflected, thereby obtaining the extent to which the current state of the power supply element has a decisive influence on the operation of the power grid, and distinguishing which power supply elements are more important.

[0181] Grey correlation can consider the complex direct and indirect coupling relationships within the grid, providing a more comprehensive assessment of the differences between power components and the ideal solution. Rather than focusing solely on individual differences, it examines the overall similarity between power components and the ideal solution, helping to identify which power components are more important.

[0182] Indicative, such as Figure 2 As shown in the evaluation flow chart, the present invention can adopt different indicators to calculate the corresponding importance evaluation results according to different power supply types.

[0183] Indicatively, based on the calculated comprehensive power supply coefficient of the power supply element, it and the equivalent utilization time coefficient can be used as indicators for evaluating the importance of new energy elements. Based on the comprehensive power supply coefficient of the power supply element, the equivalent utilization coefficient of the wind farm and the equivalent utilization coefficient of the photovoltaic power station, an ideal solution can be used to obtain the importance evaluation result of the new energy element.

[0184] Based on the calculated comprehensive power supply coefficient of the power supply component, it is used together with the maximum backup capacity and the ramp rate coefficient as indicators for evaluating the importance of conventional power supply components, and the ideal solution is used to calculate the importance evaluation results of conventional power supply components.

[0185] For step S5, by sorting the fault repair priorities, the priority of each power supply component during fault repair is clarified, so that when faced with multiple faulty components, the operation and maintenance personnel can quickly determine which faulty component to deal with first, thereby making more efficient use of time and resources.

[0186] Since the order of priority is determined by the reliability of power supply components to critical load components, the power supply components at the top of the list are more important to powering critical loads. Prioritizing the repair of these components can more effectively ensure the power supply to critical loads and reduce power outage losses.

[0187] By prioritizing the repair of high-importance power components, the overall power supply capacity of the power grid can be restored more quickly, improving the reliability and stability of the power grid.

[0188] Schematically, the sorting process and results in step S5 are based on the importance values ​​of each power supply component accurately evaluated in the previous step, and these importance values ​​are obtained by comprehensively considering multiple factors such as the power supply power of the power supply component, the importance of the load component, and the performance parameters of the power supply component. Therefore, they have high accuracy and reliability.

[0189] Furthermore, this invention comprehensively considers the load structure characteristics and power outage loss characteristics to assess the importance of load components, resulting in a more realistic ranking of important load components. Furthermore, the integrated power supply coefficient of power components is introduced to better reveal the relationship between load components and power components. This serves as a key indicator for evaluating the importance of power components, ensuring that the resulting ranking of important power components better reflects their criticality to the power supply of important load components, thereby more effectively ensuring the power supply of important load components.

[0190] Since the importance evaluation result of the power supply element finally obtained by the present invention can reflect the actual power supply role of the power supply element in the power grid and the power supply reliability to important load elements, the importance of those power supply elements with greater power supply reliability to important load elements can be made higher, and their corresponding fault repair priority can be higher. Therefore, the fault repair priority of the power supply element can be sorted based on the power supply reliability of the power supply element to important load elements, ensuring that when the power grid fails or the load fluctuates, the important load elements related to the power supply element with high importance can continue to obtain priority power supply, realize timely and effective power supply restoration, and ensure the overall power supply capacity and reliability of the power grid.

[0191] like Figure 3 As shown, based on the above-mentioned various embodiments of component importance evaluation methods applicable to fault repair, the present invention provides corresponding device item embodiments;

[0192] An embodiment of the present invention provides a component importance evaluation device suitable for fault repair, comprising: a performance parameter acquisition module, a power supply coefficient generation module, an ideal solution set generation module, an importance value evaluation module, and a fault repair priority generation module;

[0193] The performance parameter acquisition module is used to obtain the current performance parameters corresponding to each power supply element in the power grid architecture;

[0194] The power supply coefficient generation module is configured to generate a power supply coefficient for each power supply element based on the power supply element's current power supply to each load element and the importance of each load element in the power grid architecture; wherein the power supply coefficient is used to characterize the power supply reliability of the power supply element to each load element;

[0195] The ideal solution set generation module is configured to generate a first ideal solution set including a first positive ideal solution set and a first negative ideal solution set based on each power supply coefficient and each performance parameter; wherein the first positive ideal solution set includes: the maximum value of each power supply coefficient and the maximum value of each performance parameter; and the first negative ideal solution set includes: the minimum value of each power supply coefficient and the minimum value of each performance parameter;

[0196] The importance value evaluation module is configured to generate, for each power supply element, a similarity value between the power supply element and the first ideal solution set based on the first ideal solution set, a power supply coefficient corresponding to the power supply element, and a performance parameter corresponding to the power supply element; and use the similarity value corresponding to each power supply element as the importance value of each power supply element in the power grid architecture;

[0197] The fault repair priority generation module is used to sort the current fault repair priority of each power supply component in descending order based on each importance value.

[0198] It should be noted that the device embodiments described above are merely illustrative, wherein the modules described as separate components may or may not be physically separated, and the components displayed as modules may or may not be physical modules, and may be located in one place or distributed across multiple network modules. Some or all of the modules may be selected according to actual needs to achieve the purpose of the present embodiment. In addition, in the drawings of the device embodiments provided by the present invention, the connection relationship between the modules indicates that there is a communication connection between them, which may be specifically implemented as one or more communication buses or signal lines. A person of ordinary skill in the art can understand and implement the present invention without paying any creative effort.

[0199] Those skilled in the art can clearly understand that, for the sake of convenience and brevity, the specific working process of the device described above can refer to the corresponding process in the aforementioned method embodiment, and will not be repeated here.

[0200] Based on the above-mentioned various embodiments of component importance evaluation methods applicable to fault repair, the present invention provides corresponding embodiments of terminal equipment items.

[0201] An embodiment of the present invention provides a terminal device, comprising a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, it implements a component importance assessment method suitable for fault repair as described in any method embodiment of the present invention.

[0202] The terminal device may be a computing terminal device such as a desktop computer, a notebook computer, a palmtop computer, a cloud server, etc. The terminal device may include, but is not limited to, a processor and a memory.

[0203] The processor may be a central processing unit (CPU), other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or any conventional processor, etc. The processor is the control center of the terminal device, connecting various parts of the entire terminal device using various interfaces and lines.

[0204] The memory can be used to store the computer program, and the processor implements various functions of the terminal device by running or executing the computer program stored in the memory and calling the data stored in the memory. The memory can mainly include a program storage area and a data storage area, wherein the program storage area can store an operating system, at least one application required for a function, etc.; the data storage area can store data created based on the use of the mobile phone, etc. In addition, the memory can include a high-speed random access memory and can also include a non-volatile memory, such as a hard disk, internal memory, a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), at least one disk storage device, a flash memory device or other volatile solid-state storage device.

[0205] Based on the above-mentioned various embodiments of component importance evaluation methods applicable to fault repair, the present invention provides corresponding storage medium embodiments.

[0206] An embodiment of the present invention provides a storage medium, which includes a stored computer program, wherein when the computer program is running, the device where the computer-readable storage medium is located is controlled to execute a component importance assessment method suitable for fault repair as described in any method embodiment of the present invention.

[0207] The storage medium is a computer-readable storage medium, and the computer program is stored in the computer-readable storage medium. When the computer program is executed by the processor, it can implement the steps of the above-mentioned various method embodiments. The computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may include: any entity or device that can carry the computer program code, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), electric carrier signal, telecommunication signal and software distribution medium. It should be noted that the content contained in the computer-readable medium can be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable media do not include electric carrier signals and telecommunication signals.

[0208] The above is a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present invention. These improvements and modifications are also considered to be within the scope of protection of the present invention.

Claims

1. A component importance assessment method suitable for fault repair, characterized in that: include: Obtain the current performance parameters of each power element in the power grid architecture; For each power supply element, a power supply coefficient is generated based on the power supply element's current power supply to each load element and the importance of each load element in the power grid architecture; wherein the power supply coefficient is used to characterize the power supply reliability of the power supply element to each load element; generating, based on each power supply coefficient and each performance parameter, a first ideal solution set comprising a first positive ideal solution set and a first negative ideal solution set; wherein the first positive ideal solution set comprises: a maximum value among each power supply coefficient and a maximum value among each performance parameter; and the first negative ideal solution set comprises: a minimum value among each power supply coefficient and a minimum value among each performance parameter; For each power supply element, a similarity value between the power supply element and the first ideal solution set is generated based on the first ideal solution set, the power supply coefficient corresponding to the power supply element, and the performance parameter corresponding to the power supply element; and the similarity value corresponding to each power supply element is used as the importance value of each power supply element in the power grid architecture; Based on the importance values, the current fault repair priority of each power supply component is sorted from largest to smallest; The generation of the importance value of the load element in the power grid architecture includes: For each load element, obtain a clustering coefficient and a centrality coefficient of the load element; wherein the clustering coefficient is used to characterize the degree of clustering between the load element and adjacent load elements; and the centrality coefficient is used to characterize the degree of closeness between the load element and other load elements in the power grid architecture except the load element. For each load element, generating a topology coefficient for characterizing the topological characteristics of the load element in the power grid architecture according to the clustering coefficient and the centrality coefficient; For each load element, the power outage loss value corresponding to the load element is generated based on the unit cost of power outage loss corresponding to different types of users in the power grid architecture and the current power consumption of each user; Generate a second ideal solution set including a second positive ideal solution set and a second negative ideal solution set based on the topology coefficients of the load elements and the power outage loss values ​​of the load elements; wherein the second positive ideal solution set includes: the maximum value of the topology coefficients and the maximum value of the power outage loss values; and the second negative ideal solution set includes: the minimum value of the topology coefficients and the minimum value of the power outage loss values; For each load element, generating a similarity value between the load element and the second ideal solution set according to the second ideal solution set, the topology coefficient corresponding to the load element, and the power outage loss value corresponding to the load element; The similarity value corresponding to each load element is used as the importance value of each load element in the power grid architecture.

2. A component importance evaluation method suitable for fault repair according to claim 1, characterized in that: Generating a first ideal solution set including a first positive ideal solution set and a first negative ideal solution set according to each power supply coefficient and each performance parameter includes: Generate a decision matrix based on each power supply coefficient and each performance parameter; wherein column vectors corresponding to different columns in the decision matrix represent different types of indicators; the indicators are power supply coefficients or performance parameters; For the column vector corresponding to each column in the decision matrix, fill the column vector corresponding to the maximum value into the first positive ideal solution set; For the column vector corresponding to each column in the decision matrix, fill the column vector corresponding to the minimum value into the first negative ideal solution set; A first set of ideal solutions is generated according to the first set of positive ideal solutions of the padded data and the first set of negative ideal solutions of the padded data.

3. A component importance evaluation method suitable for fault repair according to claim 2, characterized in that: Generating a similarity value between the power supply element and the first ideal solution set according to the first ideal solution set, a power supply coefficient corresponding to the power supply element, and a performance parameter corresponding to the power supply element includes: Generate a parameter matrix corresponding to the power supply element according to the power supply coefficient corresponding to the power supply element and the performance parameters corresponding to the power supply element; wherein the parameter matrix is ​​used to represent the current corresponding operating state of the power supply element; generating, according to each element in the first positive ideal solution set and each element in the parameter matrix, a first correlation degree between the power supply element in a current corresponding operating state and the first positive ideal solution set; generating, according to each element in the first negative ideal solution set and each element in the parameter matrix, a second correlation degree between the power supply element in the current corresponding operating state and the first negative positive ideal solution set; Determining, according to a weight allocation method for characterizing weight allocation based on comparison strength between indicators and conflict between indicators, a first weight corresponding to the power supply coefficient among all power supply elements and a second weight corresponding to the performance parameter among all power supply elements; Generate a first information divergence distance value between the power supply element in the current corresponding operating state and the first positive ideal solution set according to the first weight, the second weight, each element in the first positive ideal solution set, and the parameter matrix; Generate a second information divergence distance value between the power supply element in the current corresponding operating state and the first negative ideal solution set according to the first weight, the second weight, each element in the first negative ideal solution set, and the parameter matrix; A similarity value representing the degree of similarity between the power supply element and the first ideal solution set is generated according to the first correlation degree, the second correlation degree, the first information divergence distance value, and the second information divergence distance value.

4. A component importance evaluation method suitable for fault repair according to claim 3, characterized in that: The power supply element includes: a new energy element; wherein the new energy element is used to represent a device that uses renewable energy to generate electricity in a power grid architecture; the performance parameters corresponding to the new energy element include: an equivalent utilization coefficient of a wind farm and an equivalent utilization coefficient of a photovoltaic power station; The generating of a decision matrix according to each power supply coefficient and each performance parameter includes: The power supply coefficient, wind farm equivalent utilization coefficient and photovoltaic power station equivalent utilization coefficient are used as different types of indicators; Each power supply coefficient, each wind farm equivalent utilization coefficient, and each photovoltaic power station equivalent utilization coefficient are respectively used as column vectors corresponding to different columns to generate a corresponding decision matrix.

5. A component importance evaluation method suitable for fault repair according to claim 3, characterized in that: The power supply element includes: a non-new energy element; wherein the non-new energy element is used to represent a device that uses non-renewable energy to generate electricity in the power grid architecture; the performance parameters corresponding to the non-new energy element include: unit spare capacity and unit ramp rate; The generating of a decision matrix according to each power supply coefficient and each performance parameter includes: The power supply coefficient, unit spare capacity and unit ramp rate are used as different types of indicators; The power supply coefficient of each power source, the spare capacity of each unit and the ramp rate of each unit are respectively used as column vectors corresponding to different columns to generate the corresponding decision matrix.

6. A component importance evaluation method suitable for fault repair according to claim 4, characterized in that: The power supply element currently generates the power supplied to each load element, including: Obtaining current wind speed data, light intensity, and grid operating parameters; wherein the grid operating parameters include resistance, reactance, susceptance, transformer ratio, generator parameters, and load parameters; Performing power flow calculations on wind speed data, light intensity, and grid operating parameters to generate power flow calculation results that characterize the power distribution of the grid architecture; wherein the power flow calculation results include: the injected power of each node, the power of each load element, and the output power of each power element; Generating a downstream distribution matrix based on the injected power of each node and the power of each load element in the power flow calculation result; wherein the downstream distribution matrix is ​​used to represent the flow path of power in the power grid architecture; generating a power distribution coefficient matrix between the power supply element and the load element based on the downstream distribution matrix, the injected power of each node, the power of each load element, and the output power of the power supply element; The current power supplied by each power supply element to each load element is generated according to the power distribution coefficient matrix and the power of each load element.

7. A component importance evaluation device suitable for fault repair, characterized in that: include: Performance parameter acquisition module, power supply coefficient generation module, ideal solution set generation module, importance value evaluation module and fault repair priority generation module; The performance parameter acquisition module is used to obtain the current performance parameters corresponding to each power supply element in the power grid architecture; The power supply coefficient generation module is used to generate a power supply coefficient for each power supply element according to the current power supply power of the power supply element to each load element and the importance value of each load element in the power grid architecture; wherein, the power supply coefficient is used to characterize the power supply reliability of the power supply element to each load element; the generation of the importance value of the load element in the power grid architecture includes: for each load element, obtaining the clustering coefficient of the load element and the centrality coefficient of the load element; wherein, the clustering coefficient is used to characterize the degree of clustering between the load element and the adjacent load elements; the centrality coefficient is used to characterize the closeness between the load element and other load elements in the power grid architecture except the load element; for each load element, based on the clustering coefficient and the centrality coefficient, a topology coefficient is generated for characterizing the topological characteristics of the load element in the power grid architecture; for each load element Element, according to the unit cost of power outage loss corresponding to different types of users in the power grid architecture and the current power consumption corresponding to each user, generates the power outage loss value corresponding to the load element; according to the topological coefficient of each load element and the power outage loss value of each load element, generates a second ideal solution set including a second positive ideal solution set and a second negative ideal solution set; wherein, the second positive ideal solution set includes: the maximum value among each topological coefficient and the maximum value among each power outage loss value; the second negative ideal solution set includes: the minimum value among each topological coefficient and the minimum value among each power outage loss value; for each load element, according to the second ideal solution set, the topological coefficient corresponding to the load element and the power outage loss value corresponding to the load element, generates a similarity value between the load element and the second ideal solution set; the similarity value corresponding to each load element is used as the importance value of each load element in the power grid architecture The ideal solution set generation module is configured to generate a first ideal solution set including a first positive ideal solution set and a first negative ideal solution set based on each power supply coefficient and each performance parameter; wherein the first positive ideal solution set includes: the maximum value of each power supply coefficient and the maximum value of each performance parameter; and the first negative ideal solution set includes: the minimum value of each power supply coefficient and the minimum value of each performance parameter; The importance value evaluation module is configured to generate, for each power supply element, a similarity value between the power supply element and the first ideal solution set based on the first ideal solution set, a power supply coefficient corresponding to the power supply element, and a performance parameter corresponding to the power supply element; and use the similarity value corresponding to each power supply element as the importance value of each power supply element in the power grid architecture; The fault repair priority generation module is used to sort the current fault repair priority of each power supply component in descending order based on each importance value.

8. A terminal device, characterized in that: The method comprises a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein when the processor executes the computer program, the method for evaluating component importance applicable to fault repair as claimed in any one of claims 1 to 6 is implemented.

9. A storage medium, characterized in that: The storage medium includes a stored computer program, wherein when the computer program is running, the device where the storage medium is located is controlled to execute the component importance evaluation method applicable to fault repair according to any one of claims 1 to 6.

Images