Fault diagnosis method and device for substation grounding grid and computer equipment

Abstract

The application relates to a fault diagnosis method and device for a substation grounding grid, computer equipment and a storage medium. The method comprises the following steps: acquiring a pre-constructed fault diagnosis model for the substation grounding grid, and acquiring a grounding grid topology structure of the substation grounding grid; according to the fault diagnosis model and the grounding grid topology structure, determining relevant attribute information corresponding to each constituting unit in the grounding grid topology structure; according to the relevant attribute information corresponding to each constituting unit, determining a plurality of groups of resistance change parameters for each branch resistance in the substation grounding grid; using a group intelligence optimization algorithm to optimize the plurality of groups of resistance change parameters, and obtaining target resistance change parameters; according to the target resistance change parameters, determining a target resistance increase multiple of each branch resistance, so as to generate a fault diagnosis result for the substation grounding grid. The method can improve the fault diagnosis efficiency of the grounding grid.

Description

Technical Field

[0001] This application relates to the field of power technology, and in particular to a fault diagnosis method, apparatus, computer equipment, and storage medium for substation grounding grids. Background Technology

[0002] In the construction and optimization of power grids in various regions of my country, rapid fault diagnosis, troubleshooting, and timely maintenance are crucial safety assurance measures. Smart substation networks occupy a vital position in power dispatching networks, therefore, their related safety assurance is a research issue that deserves serious attention. Within smart substation networks, the grounding grid is an extremely important component.

[0003] The grounding grid of a smart substation is a crucial component for ensuring the safety of operators and operating electrical equipment, and is an essential part of improving the safety and stability of the power system. Electrical grounding connects a point in the electrical equipment of the power system to the earth, providing an effective discharge path for fault currents and lightning strike currents. The purpose of electrical grounding is to ensure the safety of relevant operators and the safe and normal operation of electrical equipment by stabilizing the potential. Therefore, relevant design, production, and operation departments have always attached great importance to the performance and safety assurance of the grounding grid.

[0004] Fault diagnosis of grounding grids is a key accident prevention measure for the power sector. During the construction of grounding grids, the relevant conductors are buried underground. Improper welding procedures, prolonged soil corrosion, and damage from external forces can all affect the overall structure of the grounding grid, reducing its current-carrying capacity, shortening its lifespan, and ultimately causing safety problems of varying degrees. Therefore, when problems occur in the grounding grid, timely identification of the fault point is extremely important. Traditional troubleshooting methods typically involve manually digging up the grounding grid area, which has significant drawbacks such as wasting manpower and resources, lack of specificity, and low efficiency. Summary of the Invention

[0005] Therefore, it is necessary to provide a method, device, computer equipment, and storage medium for fault diagnosis of substation grounding grids that can improve the fault diagnosis efficiency of grounding grids, in order to address the above-mentioned technical problems.

[0006] A fault diagnosis method for substation grounding grids includes:

[0007] Obtain a pre-constructed fault diagnosis model for the substation grounding grid, and obtain the grounding grid topology of the substation grounding grid;

[0008] Based on the fault diagnosis model and the grounding grid topology, the relevant attribute information corresponding to each constituent unit in the grounding grid topology is determined; the constituent unit includes grounding grid nodes and grounding grid branches;

[0009] Based on the relevant attribute information corresponding to each of the constituent units, multiple sets of resistance variation parameters are determined for the resistance of each branch in the substation grounding grid; the resistance variation parameters are used to characterize the resistance increase factor of each branch resistance.

[0010] The target resistance change parameters are obtained by optimizing the multiple sets of resistance change parameters using a swarm intelligence optimization algorithm.

[0011] Based on the target resistance change parameters, the target resistance increase factor for each branch resistance is determined to generate fault diagnosis results for the substation grounding grid.

[0012] In one embodiment, obtaining the pre-built fault diagnosis model for the substation grounding grid includes:

[0013] Based on the fault diagnosis theoretical information of the substation grounding grid, an initial mathematical model for the substation grounding grid is constructed.

[0014] The initial mathematical model is simplified using a preset adaptive penalty function to obtain an optimized mathematical model, which serves as the fault diagnosis model.

[0015] In one embodiment, the step of simplifying the initial mathematical model using a preset adaptive penalty function to obtain an optimized mathematical model, which serves as the fault diagnosis model, includes:

[0016] Obtain the objective function of the initial mathematical model;

[0017] Substituting the adaptive penalty function into the objective function yields the optimized mathematical model.

[0018] In one embodiment, the optimization mathematical model is expressed as:

[0019]

[0020] Where γ is the proportion of feasible solutions in the current population in the swarm intelligence optimization algorithm, ranging from (0,1], f(x) is the objective function, and H i (x) is the adaptive penalty function, and n is the number of branches in the substation grounding network.

[0021] In one embodiment, the objective function is expressed as:

[0022]

[0023]

[0024]

[0025] Where N is the number of nodes, n is the number of branches, M is the number of reachable nodes, and x j Let x be the diagonal element in the branch admittance matrix, and x j =1 / R j k0 is the reference point for reachable nodes, R j It is the branch resistance, where ε is a positive number less than a preset threshold.

[0026] In one embodiment, the substation grounding grid includes m+1 nodes and has n branches, with the voltage V of the i-th branch being... (i) and the voltage U of the i-th node (i) The relationships that exist are represented as follows:

[0027]

[0028] Where A represents the correlation matrix of the substation grounding network, I S I represents the column vector of node injected currents. S (i) X represents the excitation applied by the i-th current source between two reachable nodes. m This represents the node admittance matrix of the equivalent network of nodes.

[0029] A fault diagnosis device for substation grounding grids, the device comprising:

[0030] The acquisition module is used to acquire a pre-constructed fault diagnosis model for the substation grounding grid, and to acquire the grounding grid topology of the substation grounding grid.

[0031] The determination module is used to determine the relevant attribute information corresponding to each constituent unit in the grounding grid topology based on the fault diagnosis model and the grounding grid topology; the constituent unit includes grounding grid nodes and grounding grid branches;

[0032] An initialization module is used to determine multiple sets of resistance variation parameters for each branch resistance in the substation grounding grid based on the relevant attribute information corresponding to each of the constituent units; the resistance variation parameters are used to characterize the resistance increase factor of each branch resistance.

[0033] The optimization module is used to optimize the multiple sets of resistance variation parameters using a swarm intelligence optimization algorithm to obtain the target resistance variation parameters;

[0034] The generation module is used to determine the target resistance increase factor of each branch resistance based on the target resistance change parameter, so as to generate fault diagnosis results for the substation grounding grid.

[0035] A computer device includes a memory and a processor, the memory storing a computer program, the processor executing the computer program to implement the steps of the method described above.

[0036] A computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the method described above.

[0037] The aforementioned fault diagnosis method, device, computer equipment, and storage medium for substation grounding networks utilize a fault diagnosis mathematical model of the substation grounding network, combined with the topology of the various devices within the substation grounding network, and determine the relevant attributes of each node and branch in the topology. Based on this, the initial parameter values ​​of the swarm intelligence optimization algorithm are selected, and the resistance increase factor on each branch is solved through multiple iterations. Finally, the fault diagnosis result of this grounding network is given. This method is feasible and universal, providing a strong reference for the prediction and maintenance of substation grounding network faults, and offering beneficial ideas for ensuring the safe and stable operation of substation networks, effectively improving the fault diagnosis efficiency of substation grounding networks. Attached Figure Description

[0038] Figure 1 This is a flowchart illustrating a fault diagnosis method for a substation grounding grid in one embodiment.

[0039] Figure 2 This is a schematic diagram of a substation grounding grid in one embodiment;

[0040] Figure 3 This is a schematic diagram of a grounding grid topology in one embodiment;

[0041] Figure 4 This is a flowchart illustrating a fault diagnosis method for a substation grounding grid in another embodiment;

[0042] Figure 5 This is a structural block diagram of a fault diagnosis device for a substation grounding grid in one embodiment;

[0043] Figure 6 This is an internal structural diagram of a computer device in one embodiment. Detailed Implementation

[0044] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0045] In one embodiment, such as Figure 1 As shown, a fault diagnosis method for substation grounding grids is provided, including the following steps:

[0046] Step S110: Obtain the pre-built fault diagnosis model for the substation grounding network, and obtain the grounding network topology of the substation grounding network.

[0047] Among them, the substation grounding grid can rapidly dissipate fault current or lightning current, limit the rise of ground potential, and ensure the safety of personnel and equipment. Therefore, grounding is an important condition for ensuring the safe and stable operation of the power system. For the convenience of those skilled in the art, Figure 2 A schematic diagram of a substation grounding grid is provided.

[0048] In practice, the electronic equipment acquires a pre-built fault diagnosis model for the substation grounding network, and acquires the grounding network topology of the substation grounding network.

[0049] In the process of acquiring a pre-constructed fault diagnosis model for the substation grounding grid, the electronic equipment can construct an initial mathematical model for the substation grounding grid based on the fault diagnosis theoretical information of the substation grounding grid; and use a preset adaptive penalty function to simplify the initial mathematical model to obtain an optimized mathematical model, which serves as the fault diagnosis model.

[0050] In practical applications, the optimization mathematical model can be expressed as:

[0051]

[0052] Where γ is the proportion of feasible solutions in the current population in the swarm intelligence optimization algorithm, ranging from (0,1], f(x) is the objective function, and H i (x) is the adaptive penalty function, and n is the number of branches in the substation grounding network.

[0053] The objective function is expressed as:

[0054]

[0055]

[0056]

[0057] Where N is the number of nodes, n is the number of branches, M is the number of reachable nodes, and x j Let x be the diagonal element in the branch admittance matrix, and x j =1 / R j k0 is the reference point for reachable nodes, R j It is the branch resistance, where ε is a positive number less than a preset threshold.

[0058] The adaptive penalty function can be expressed as:

[0059]

[0060] Where γ is the proportion of feasible solutions in the current population, ranging from (0,1], g i (x) represents two inequality constraints. The value of 100 / γ affects the optimization of the algorithm. In the early stages, its value should be relatively large to increase the number of feasible solutions in the population. When the number of feasible solutions reaches a certain proportion, the value should be reduced, and it should dynamically decrease as the number of feasible solutions increases, thereby ensuring that the algorithm can effectively obtain the optimal solution during the execution process.

[0061] After acquiring the fault diagnosis model, the electronic equipment can obtain the grounding network topology of the substation grounding network. For ease of understanding by those skilled in the art, Figure 3 A schematic diagram of a grounding grid topology is provided; such as Figure 3 As shown, the grounding network topology has 49 nodes, 84 branches (line segments between nodes), and associated nodes on each branch.

[0062] In another embodiment, the substation grounding grid includes m+1 nodes and has n branches. The relationship between the voltage V(i) of the i-th branch and the voltage U(i) of the i-th node is expressed as follows:

[0063]

[0064] Where A represents the correlation matrix of the substation grounding network, I S I represents the column vector of node injected currents. S (i) X represents the excitation applied by the i-th current source between two reachable nodes. m This represents the node admittance matrix of the equivalent network of nodes.

[0065] Step S120: Based on the fault diagnosis model and the grounding grid topology, determine the relevant attribute information corresponding to each constituent unit in the grounding grid topology.

[0066] The constituent units include grounding grid nodes and grounding grid branches.

[0067] In practical implementation, electronic devices can use fault diagnosis models and grounding grid topology to determine the relevant attribute information of each constituent unit in the grounding grid topology, which is then used for the initialization of swarm intelligence optimization algorithms. Specifically, electronic devices can use fault diagnosis models and grounding grid topology to determine the initial value of the resistance of each branch in the grounding grid topology.

[0068] Step S130: Based on the relevant attribute information corresponding to each constituent unit, determine multiple sets of resistance variation parameters for the resistance of each branch in the substation grounding network.

[0069] Among them, the resistance variation parameter is used to characterize the resistance increase factor of each branch.

[0070] Among them, the electronic equipment can determine multiple sets of resistance variation parameters for the resistance of each branch in the substation grounding grid based on the relevant attribute information corresponding to each constituent unit.

[0071] In practice, after the electronic device obtains the initial value of the resistance of each branch in the grounding grid topology, it can initialize the resistance increase factor of each branch resistance based on the initial value of each branch resistance, thereby determining multiple sets of resistance change parameters for each branch resistance in the substation grounding grid.

[0072] Step S140: Optimize multiple sets of resistance variation parameters using a swarm intelligence optimization algorithm to obtain the target resistance variation parameters.

[0073] Among them, the swarm intelligence optimization algorithm can be the coyote optimization algorithm (coyote algorithm).

[0074] In practice, after determining multiple sets of resistance variation parameters, the electronic device can use the Jungle Wolf optimization algorithm to generate multiple wolf packs in the Jungle Wolf optimization algorithm for iterative optimization to obtain the target resistance variation parameters.

[0075] The aforementioned jungle wolf algorithm mainly includes the following parts:

[0076] The cultural trend constraint for wolves is used to share the social status of individual wolves. Specifically, firstly, the cultural trend of each wolf pack is calculated, which is the median social status of the wolves in each pack. The difference between the social status of an individual wolf and the cultural trend of its pack should be controlled within a pre-set range (<ε1). Secondly, the difference between the social status of an individual wolf and the cultural trend of its pack leader should also be controlled within a pre-set range (<ε2). Through the above-mentioned difference constraints, the stability of the overall wolf pack is maintained, and its healthy development is promoted.

[0077] The social status of wolves, used for updating social status during population survival iterations, is calculated using the following formula:

[0078]

[0079] Where q represents the wolf pack, l represents an individual wolf in the pack, t represents the current moment, and r 1-2 It is a random number between 0 and 1. It refers to one's original social status.

[0080] The birth and death of jungle wolves are used to represent the selection and generation of relevant attributes of newborn wolf individuals in the pack. Specifically, two individuals are randomly selected from the pack as parents, and the death of older wolf individuals is judged.

[0081] Competition for leadership within a wolf pack is used to simulate the selection of a leader in a naturally inoculated population to maintain population stability and determine its development direction. The formula is as follows:

[0082]

[0083] in, F represents the adaptability of this individual wolf to its surrounding environment, where F is the fitness function.

[0084] Wolf pack reorganization is used to determine whether an individual wolf leaves its pack, with a probability of N. 2 / 200, and whether to join other groups, while completing the unified update and adjustment of related attributes.

[0085] Step S150: Based on the target resistance change parameters, determine the target resistance increase factor for each branch resistance to generate fault diagnosis results for the substation grounding grid.

[0086] In practice, after obtaining the target resistance change parameter, the electronic device can determine the target resistance increase factor for each branch resistance based on the target resistance change parameter. Then, based on the target resistance increase factor for each branch resistance, the electronic device generates a fault diagnosis result for the substation grounding network.

[0087] The technical solution of this embodiment utilizes a mathematical model for fault diagnosis of substation grounding networks, combined with the topology of the various devices within the substation grounding network, and determines the relevant attributes of each node and branch in the topology. Based on this, the initial parameter values ​​of the swarm intelligence optimization algorithm are selected. Through multiple iterations, the resistance increase factor on each branch is calculated, ultimately providing the fault diagnosis result for this grounding network. This solution is feasible and universal, providing a strong reference for the prediction and maintenance of substation grounding network faults, and offering beneficial ideas for ensuring the safe and stable operation of substation networks, effectively improving the fault diagnosis efficiency of substation grounding networks.

[0088] In another embodiment, such as Figure 4 As shown, a fault diagnosis method for substation grounding grids is provided, including the following steps:

[0089] Step S410: Based on the fault diagnosis theoretical information of the substation grounding network, construct an initial mathematical model for the substation grounding network.

[0090] Step S420: Obtain the objective function of the initial mathematical model.

[0091] Step S430: Substitute the preset adaptive penalty function into the objective function to obtain an optimized mathematical model, which serves as the fault diagnosis model.

[0092] Step S440: Obtain the grounding grid topology of the substation grounding grid.

[0093] Step S450: Based on the fault diagnosis model and the grounding grid topology, determine the relevant attribute information corresponding to each constituent unit in the grounding grid topology; the constituent unit includes grounding grid nodes and grounding grid branches.

[0094] Step S460: Based on the relevant attribute information corresponding to each of the constituent units, determine multiple sets of resistance variation parameters for each branch resistance in the substation grounding grid; the resistance variation parameters are used to characterize the resistance increase factor of each branch resistance.

[0095] Step S470: Optimize the multiple sets of resistance variation parameters using a swarm intelligence optimization algorithm to obtain the target resistance variation parameters.

[0096] Step S480: Based on the target resistance change parameters, determine the target resistance increase factor for each branch resistance to generate a fault diagnosis result for the substation grounding grid.

[0097] It should be noted that the specific limitations of the above steps can be found in the above description of the specific limitations of a fault diagnosis method for substation grounding grids.

[0098] It should be understood that, although Figure 1 and Figure 4 The steps in the flowchart are shown sequentially as indicated by the arrows, but these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order in which these steps are executed, and they can be performed in other orders. Figure 1 and Figure 4 At least some of the steps in the process may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but may be executed at different times. The execution order of these steps or stages is not necessarily sequential, but may be executed in turn or alternately with other steps or at least some of the steps or stages in other steps.

[0099] In one embodiment, such as Figure 5 As shown, a fault diagnosis device for substation grounding grids is provided, comprising:

[0100] The acquisition module 510 is used to acquire a pre-constructed fault diagnosis model for the substation grounding grid, and to acquire the grounding grid topology of the substation grounding grid.

[0101] The determination module 520 is used to determine the relevant attribute information corresponding to each constituent unit in the grounding grid topology based on the fault diagnosis model and the grounding grid topology; the constituent unit includes grounding grid nodes and grounding grid branches;

[0102] The initialization module 530 is used to determine multiple sets of resistance variation parameters for each branch resistance in the substation grounding grid based on the relevant attribute information corresponding to each of the constituent units; the resistance variation parameters are used to characterize the resistance increase factor of each branch resistance.

[0103] Optimization module 540 is used to optimize the multiple sets of resistance change parameters using a swarm intelligence optimization algorithm to obtain the target resistance change parameters;

[0104] The generation module 550 is used to determine the target resistance increase factor of each branch resistance based on the target resistance change parameter, so as to generate a fault diagnosis result for the substation grounding grid.

[0105] In one embodiment, the acquisition module 510 is specifically used to construct an initial mathematical model for the substation grounding network based on the fault diagnosis theoretical information of the substation grounding network; and to simplify the initial mathematical model by using a preset adaptive penalty function to obtain an optimized mathematical model as the fault diagnosis model.

[0106] In one embodiment, the acquisition module 510 is specifically used to acquire the objective function of the initial mathematical model; and substitute the adaptive penalty function into the objective function to obtain the optimized mathematical model.

[0107] In one embodiment, the optimization mathematical model is expressed as:

[0108]

[0109] Where γ is the proportion of feasible solutions in the current population in the swarm intelligence optimization algorithm, ranging from (0,1], f(x) is the objective function, and H i (x) is the adaptive penalty function, and n is the number of branches in the substation grounding network.

[0110] In one embodiment, the objective function is expressed as:

[0111]

[0112]

[0113]

[0114] Where N is the number of nodes, n is the number of branches, M is the number of reachable nodes, and x j Let x be the diagonal element in the branch admittance matrix, and x j =1 / R j k0 is the reference point for reachable nodes, R j It is the branch resistance, where ε is a positive number less than a preset threshold.

[0115] In one embodiment, the substation grounding grid includes m+1 nodes and has n branches, with the voltage V of the i-th branch being... (i) and the voltage U of the i-th node (i) The relationships that exist are represented as follows:

[0116]

[0117] Where A represents the correlation matrix of the substation grounding network, I S I represents the column vector of node injected currents. S (i) X represents the excitation applied by the i-th current source between two reachable nodes. m This represents the node admittance matrix of the equivalent network of nodes.

[0118] For specific limitations regarding a fault diagnosis device for substation grounding grids, please refer to the limitations of a fault diagnosis method for substation grounding grids mentioned above, which will not be repeated here. Each module in the aforementioned fault diagnosis device for substation grounding grids can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device in hardware form, or stored in the memory of a computer device in software form, so that the processor can call and execute the operations corresponding to each module.

[0119] In one embodiment, a computer device is provided, which may be a terminal, and its internal structure diagram may be as follows: Figure 6As shown, the computer device includes a processor, memory, communication interface, display screen, and input devices connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, mobile cellular networks, NFC (Near Field Communication), or other technologies. When the computer program is executed by the processor, it implements a fault diagnosis method for substation grounding grids. The display screen can be an LCD screen or an e-ink display screen. The input devices can be a touch layer covering the display screen, buttons, a trackball, or a touchpad on the computer device's casing, or an external keyboard, touchpad, or mouse.

[0120] Those skilled in the art will understand that Figure 6 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0121] In one embodiment, a computer device is provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to perform the following steps:

[0122] Obtain a pre-constructed fault diagnosis model for the substation grounding grid, and obtain the grounding grid topology of the substation grounding grid;

[0123] Based on the fault diagnosis model and the grounding grid topology, the relevant attribute information corresponding to each constituent unit in the grounding grid topology is determined; the constituent unit includes grounding grid nodes and grounding grid branches;

[0124] Based on the relevant attribute information corresponding to each of the constituent units, multiple sets of resistance variation parameters are determined for the resistance of each branch in the substation grounding grid; the resistance variation parameters are used to characterize the resistance increase factor of each branch resistance.

[0125] The target resistance change parameters are obtained by optimizing the multiple sets of resistance change parameters using a swarm intelligence optimization algorithm.

[0126] Based on the target resistance change parameters, the target resistance increase factor for each branch resistance is determined to generate fault diagnosis results for the substation grounding grid.

[0127] In one embodiment, the processor further performs the following steps when executing the computer program:

[0128] Based on the fault diagnosis theoretical information of the substation grounding grid, an initial mathematical model for the substation grounding grid is constructed.

[0129] The initial mathematical model is simplified using a preset adaptive penalty function to obtain an optimized mathematical model, which serves as the fault diagnosis model.

[0130] In one embodiment, the processor further performs the following steps when executing the computer program:

[0131] Obtain the objective function of the initial mathematical model;

[0132] Substituting the adaptive penalty function into the objective function yields the optimized mathematical model.

[0133] In one embodiment, the optimization mathematical model is expressed as:

[0134]

[0135] Where γ is the proportion of feasible solutions in the current population in the swarm intelligence optimization algorithm, ranging from (0,1], f(x) is the objective function, and H i (x) is the adaptive penalty function, and n is the number of branches in the substation grounding network.

[0136] In one embodiment, the objective function is expressed as:

[0137]

[0138]

[0139]

[0140] Where N is the number of nodes, n is the number of branches, M is the number of reachable nodes, and x j Let x be the diagonal element in the branch admittance matrix, and x j =1 / R j k0 is the reference point for reachable nodes, R j It is the branch resistance, where ε is a positive number less than a preset threshold.

[0141] In one embodiment, the substation grounding grid includes m+1 nodes and has n branches, with the voltage V of the i-th branch being... (i) and the voltage U of the i-th node (i) The relationships that exist are represented as follows:

[0142]

[0143] Where A represents the correlation matrix of the substation grounding network, I S I represents the column vector of node injected currents. S (i) X represents the excitation applied by the i-th current source between two reachable nodes. m This represents the node admittance matrix of the equivalent network of nodes.

[0144] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon, the computer program performing the following steps when executed by a processor:

[0145] Obtain a pre-constructed fault diagnosis model for the substation grounding grid, and obtain the grounding grid topology of the substation grounding grid;

[0146] Based on the fault diagnosis model and the grounding grid topology, the relevant attribute information corresponding to each constituent unit in the grounding grid topology is determined; the constituent unit includes grounding grid nodes and grounding grid branches;

[0147] Based on the relevant attribute information corresponding to each of the constituent units, multiple sets of resistance variation parameters are determined for the resistance of each branch in the substation grounding grid; the resistance variation parameters are used to characterize the resistance increase factor of each branch resistance.

[0148] The target resistance change parameters are obtained by optimizing the multiple sets of resistance change parameters using a swarm intelligence optimization algorithm.

[0149] Based on the target resistance change parameters, the target resistance increase factor for each branch resistance is determined to generate fault diagnosis results for the substation grounding grid.

[0150] In one embodiment, the processor further performs the following steps when executing the computer program:

[0151] Based on the fault diagnosis theoretical information of the substation grounding grid, an initial mathematical model for the substation grounding grid is constructed.

[0152] The initial mathematical model is simplified using a preset adaptive penalty function to obtain an optimized mathematical model, which serves as the fault diagnosis model.

[0153] In one embodiment, the processor further performs the following steps when executing the computer program:

[0154] Obtain the objective function of the initial mathematical model;

[0155] Substituting the adaptive penalty function into the objective function yields the optimized mathematical model.

[0156] In one embodiment, the optimization mathematical model is expressed as:

[0157]

[0158] Where γ is the proportion of feasible solutions in the current population in the swarm intelligence optimization algorithm, ranging from (0,1], f(x) is the objective function, and H i (x) is the adaptive penalty function, and n is the number of branches in the substation grounding network.

[0159] In one embodiment, the objective function is expressed as:

[0160]

[0161]

[0162]

[0163] Where N is the number of nodes, n is the number of branches, M is the number of reachable nodes, and x j Let x be the diagonal element in the branch admittance matrix, and x j =1 / R j k0 is the reference point for reachable nodes, R j It is the branch resistance, where ε is a positive number less than a preset threshold.

[0164] In one embodiment, the substation grounding grid includes m+1 nodes and has n branches, with the voltage V of the i-th branch being... (i) and the voltage U of the i-th node (i) The relationships that exist are represented as follows:

[0165]

[0166] Where A represents the correlation matrix of the substation grounding network, I S I represents the column vector of node injected currents. S (i) X represents the excitation applied by the i-th current source between two reachable nodes. m This represents the node admittance matrix of the equivalent network of nodes.

[0167] In one embodiment, a computer program product is provided, including a computer program that, when executed by a processor, performs the following steps:

[0168] Obtain a pre-constructed fault diagnosis model for the substation grounding grid, and obtain the grounding grid topology of the substation grounding grid;

[0169] Based on the fault diagnosis model and the grounding grid topology, the relevant attribute information corresponding to each constituent unit in the grounding grid topology is determined; the constituent unit includes grounding grid nodes and grounding grid branches;

[0170] Based on the relevant attribute information corresponding to each of the constituent units, multiple sets of resistance variation parameters are determined for the resistance of each branch in the substation grounding grid; the resistance variation parameters are used to characterize the resistance increase factor of each branch resistance.

[0171] The target resistance change parameters are obtained by optimizing the multiple sets of resistance change parameters using a swarm intelligence optimization algorithm.

[0172] Based on the target resistance change parameters, the target resistance increase factor for each branch resistance is determined to generate fault diagnosis results for the substation grounding grid.

[0173] In one embodiment, when the computer program is executed by the processor, it further performs the following steps: constructing an initial mathematical model for the substation grounding network based on the fault diagnosis theoretical information of the substation grounding network; and simplifying the initial mathematical model using a preset adaptive penalty function to obtain an optimized mathematical model, which serves as the fault diagnosis model.

[0174] In one embodiment, when the computer program is executed by the processor, it further performs the following steps: obtaining the objective function of the initial mathematical model; substituting the adaptive penalty function into the objective function to obtain an optimized mathematical model.

[0175] In one embodiment, the optimization mathematical model is expressed as:

[0176]

[0177] Where γ is the proportion of feasible solutions in the current population in the swarm intelligence optimization algorithm, ranging from (0,1], f(x) is the objective function, and H i (x) is the adaptive penalty function, and n is the number of branches in the substation grounding network.

[0178] In one embodiment, the objective function is expressed as:

[0179]

[0180]

[0181]

[0182] Where N is the number of nodes, n is the number of branches, M is the number of reachable nodes, and x j Let x be the diagonal element in the branch admittance matrix, and x j =1 / R j k0 is the reference point for reachable nodes, R j It is the branch resistance, where ε is a positive number less than a preset threshold.

[0183] In one embodiment, the substation grounding grid includes m+1 nodes and has n branches, with the voltage V of the i-th branch being... (i) and the voltage U of the i-th node (i) The relationships that exist are represented as follows:

[0184]

[0185] Where A represents the correlation matrix of the substation grounding network, I S I represents the column vector of node injected currents. S (i) X represents the excitation applied by the i-th current source between two reachable nodes. m This represents the node admittance matrix of the equivalent network of nodes.

[0186] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties.

[0187] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, 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. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM). The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, etc., and are not limited to these.

[0188] The technical features of the above embodiments can be combined in any way. For the sake of brevity, 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 specification.

[0189] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of this patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this application should be determined by the appended claims.

Claims

1. A fault diagnosis method for substation grounding grids, characterized in that, include: Obtain a pre-constructed fault diagnosis model for the substation grounding grid, and obtain the grounding grid topology of the substation grounding grid; Based on the fault diagnosis model and the grounding grid topology, the relevant attribute information corresponding to each constituent unit in the grounding grid topology is determined; the constituent unit includes grounding grid nodes and grounding grid branches; Based on the relevant attribute information corresponding to each of the constituent units, multiple sets of resistance variation parameters are determined for the resistance of each branch in the substation grounding grid; the resistance variation parameters are used to characterize the resistance increase factor of each branch resistance. The target resistance change parameters are obtained by optimizing the multiple sets of resistance change parameters using a swarm intelligence optimization algorithm. Based on the target resistance change parameters, the target resistance increase factor for each branch resistance is determined to generate fault diagnosis results for the substation grounding grid.

2. The fault diagnosis method according to claim 1, characterized in that, The process of obtaining a pre-built fault diagnosis model for the substation grounding grid includes: Based on the fault diagnosis theoretical information of the substation grounding grid, an initial mathematical model for the substation grounding grid is constructed. The initial mathematical model is simplified using a preset adaptive penalty function to obtain an optimized mathematical model, which serves as the fault diagnosis model.

3. The fault diagnosis method according to claim 2, characterized in that, The step of simplifying the initial mathematical model using a preset adaptive penalty function to obtain an optimized mathematical model, which serves as the fault diagnosis model, includes: Obtain the objective function of the initial mathematical model; Substituting the adaptive penalty function into the objective function yields the optimized mathematical model.

4. The fault diagnosis method according to claim 3, characterized in that, The optimized mathematical model is expressed as follows: Where γ is the proportion of feasible solutions in the current population in the swarm intelligence optimization algorithm, ranging from (0,1], f(x) is the objective function, and H i (x) is the adaptive penalty function, and n is the number of branches in the substation grounding network.

5. The fault diagnosis method according to claim 4, characterized in that, The objective function is expressed as: Where N is the number of nodes, n is the number of branches, M is the number of reachable nodes, and x j Let x be the diagonal element in the branch admittance matrix, and x j =1 / R j k0 is the reference point for reachable nodes, R j It is the branch resistance, where ε is a positive number less than a preset threshold.

6. The fault diagnosis method according to any one of claims 1 to 5, characterized in that, The substation grounding grid includes m+1 nodes and has n branches. The voltage of the i-th branch is V. (i) and the voltage U of the i-th node (i) The relationships that exist are represented as follows: Where A represents the correlation matrix of the substation grounding network, I S I represents the column vector of node injected currents. S (i) X represents the excitation applied by the i-th current source between two reachable nodes. m This represents the node admittance matrix of the equivalent network of nodes.

7. A fault diagnosis device for substation grounding grids, characterized in that, The device includes: The acquisition module is used to acquire a pre-constructed fault diagnosis model for the substation grounding grid, and to acquire the grounding grid topology of the substation grounding grid. The determination module is used to determine the relevant attribute information corresponding to each constituent unit in the grounding grid topology based on the fault diagnosis model and the grounding grid topology; the constituent unit includes grounding grid nodes and grounding grid branches; An initialization module is used to determine multiple sets of resistance variation parameters for each branch resistance in the substation grounding grid based on the relevant attribute information corresponding to each of the constituent units; the resistance variation parameters are used to characterize the resistance increase factor of each branch resistance. The optimization module is used to optimize the multiple sets of resistance variation parameters using a swarm intelligence optimization algorithm to obtain the target resistance variation parameters; The generation module is used to determine the target resistance increase factor of each branch resistance based on the target resistance change parameter, so as to generate fault diagnosis results for the substation grounding grid.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the fault diagnosis method according to any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the fault diagnosis method according to any one of claims 1 to 6.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by the processor, it implements the steps of the fault diagnosis method according to any one of claims 1 to 6.

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