A method, system, and medium for multi-source data fusion inversion of multiple lightning strikes

Abstract

This application relates to the field of power system protection technology, and in particular to a method, system, and medium for multi-source data fusion-based inversion of multiple lightning strikes. Multi-source lightning characteristic data are collected from three types of measuring equipment: traveling wave, waveform recording, and lightning location system. After spatiotemporal synchronization of the data from these three sources, target lightning strike data belonging to the same lightning event at the same location and within the same time period are identified. The inter-strike interval and low-frequency dynamic features are extracted from the target lightning strike data. Based on the constructed multiple lightning strike function, the tunica albuginea swarm optimization algorithm is used to iteratively search for key parameters in the multiple lightning strike function, obtaining the optimal parameter set after iteration, thus achieving the inversion of multiple lightning strike currents. The aim is to solve the problem of how to fuse multi-source data for lightning parameter inversion.

Description

Technical Field

[0001] This application relates to the field of power system protection technology, and in particular to a multi-source data fusion method, system and medium for multiple lightning strike inversion. Background Technology

[0002] Frequent lightning activity, especially multiple lightning strikes, has become a significant factor threatening the safe and stable operation of transmission lines. Traditional lightning monitoring methods based on single data sources are insufficient to fully characterize the dynamic processes and parameter features of multiple lightning strikes, and existing lightning parameter identification methods generally suffer from insufficient accuracy when dealing with multiple lightning strike events.

[0003] The key to lightning fault analysis lies in accurately obtaining the lightning parameters of the lightning current waveform. However, the lightning current waveform is often affected by factors such as insulator flashover, impulse corona, and propagation characteristics, making it difficult to directly obtain the original data of the lightning strike point.

[0004] In recent years, with the development of sensor technology and monitoring equipment technology, multi-source data fusion has become a research hotspot. Therefore, this application proposes a multi-source data fusion method for multiple lightning strike inversion, which aims to fuse multiple collected data to achieve lightning parameter inversion. Summary of the Invention

[0005] The main purpose of this application is to provide a multi-source data fusion method for multiple lightning strike inversion, aiming to solve the problem of how to fuse multi-source data for lightning parameter inversion.

[0006] To achieve the above objectives, this application provides a multi-source data fusion method for multiple lightning strike inversion, applicable to power systems equipped with lightning location systems, traveling wave devices, and waveform recording devices. The method includes the following steps:

[0007] S10, acquire lightning location data collected by the lightning location system, traveling wave lightning fault data collected by the traveling wave device, and recording fault data collected by the recording device.

[0008] S20, based on the lightning location data, the traveling wave lightning strike fault data and the recorded fault data, filter out the target lightning strike data that is the same lightning strike event from the multiple lightning strike data, wherein the same lightning strike event is a lightning strike event that occurs at the same lightning strike location and within the same lightning strike time period.

[0009] S30, extract the strike interval and low-frequency dynamic features from the target lightning strike data, and construct a multiple lightning strike function based on the strike interval and the low-frequency dynamic features;

[0010] S40, the tunic algorithm is used to perform parameter inversion on the multiple lightning strike function, and the obtained lightning current parameters are output.

[0011] Optionally, S10 includes:

[0012] S11, compare the lightning strike time and lightning strike location in the lightning location data with the fault record time and installation location in each of the traveling wave lightning fault data and / or the waveform recording fault data;

[0013] S12, the target lightning strike data, target traveling wave lightning strike fault data, and target waveform recording fault data that satisfy the condition that the lightning strike time and the fault recording time are within a preset time window, and that the lightning strike location and the installation location are in the same area, are determined as the target lightning strike data as the same lightning strike event.

[0014] Optionally, in S30, the low-frequency dynamic characteristics include amplitude, initial waveform steepness, wavefront time constant, and wavetail time constant, wherein the expression for the multiple lightning strike function is:

[0015]

[0016] in, ,

[0017] In the formula, and This represents the first and second amplitude values ​​of two consecutive lightning strikes. and This represents the steepness of the first and second initial waveforms between two consecutive lightning strikes. and The first and second wavefront time constants are the time constants of two consecutive lightning strikes. and The first and second tail time constants of two consecutive lightning strikes are given. and These are two amplitude correction factors for two consecutive lightning strikes. This refers to the interval between two consecutive lightning strikes.

[0018] Optionally, the step of using the tunic algorithm to perform parameter inversion on the multiple lightning strike function and outputting the obtained lightning current parameters includes:

[0019] S41, taking the low-frequency dynamic characteristics and inter-strike interval in the multiple lightning strike function as individual *Symplocos salpinx*, and setting the search space as one... A European-style space, in which, Indicates the population size of salps. Dimensions representing the problem:

[0020]

[0021] The position of the i-th tunicate individual is represented as: Each individual can be generated using the following equation:

[0022] In the formula, This indicates the generation of a random number that is uniformly distributed between 0 and 1. Indicates the first The upper limit of dimensions Indicates the first The lower bound of the dimension;

[0023] S42, calculate the fitness value of each tunicate individual, and rank the individuals in the population according to the fitness value. The following formula is the update formula for the leader position:

[0024]

[0025] in,

[0026] In the formula, Indicates the leader In the Position in 3D space; Indicates the first The location of the food source in 3D space; parameters , It is an interval The random number on, where This determines the current search step size, and This determines the search direction in the j-th dimension; The convergence factor is and These represent the current iteration count and the maximum iteration count, respectively.

[0027] S43, the leader follows the formula Move towards the food source, approaching the current optimal solution; according to the formula Update the position of each follower, gradually bringing them closer to the leader;

[0028] in, ,

[0029] In the formula, The initial velocity, For acceleration, The final velocity of the motion, For the j-th dimension space Individual sea squirts, For the first The first dimension in 3D space Individual sea squirts;

[0030] S44, when the iteration termination condition is met, exit the iteration and take the current position of the individual tunicate as the optimal set of lightning current parameters.

[0031] Optionally, the iteration termination condition includes at least one of the following:

[0032] (1) The preset maximum number of iterations Nmax is reached;

[0033] (2) The fitness function value is lower than the preset threshold ε;

[0034] (3) The fitness change is less than the threshold after several consecutive iterations.

[0035] Optionally, the objective function of the multiple lightning strike function includes:

[0036]

[0037] in: This refers to the number of sampling points, i.e., the number of sampling points in the measured traveling wave data; The simulated traveling wave current value is obtained after calculation using multiple lightning strike functions; The measured traveling wave current value is given.

[0038] In addition, to achieve the above objectives, this application also provides a computer system comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the computer program, when executed by the processor, implements the steps of the multi-source data fusion multiple lightning strike inversion method as described in any of the preceding claims.

[0039] In addition, to achieve the above objectives, this application also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps of the multi-source data fusion and multiple lightning strike inversion method as described in any of the preceding claims.

[0040] This application has at least the following beneficial effects:

[0041] 1. Based on the joint processing of multi-source data, and through consistent spatiotemporal registration rules, multiple lightning strike events are effectively integrated, significantly improving the integrity and traceability of lightning strike information;

[0042] 2. Based on the multiple lightning strike inversion mechanism of the tunic group algorithm, a multiple lightning strike current waveform fitting function is constructed, and the parameter solution set is obtained through adaptive multidimensional search to realize the reconstruction of parameter variables and time series of subsequent lightning strikes;

[0043] 3. The method of the present invention does not rely on a large number of training samples or deep learning models, has good engineering feasibility and algorithm interpretability, and can be deployed in embedded devices or edge intelligent terminals to meet the needs of power transmission line operation and maintenance sites for rapid response and low resource consumption.

[0044] 4. This invention has strong compatibility and can be extended to various scenarios such as lightning protection assessment of transmission lines, review of lightning strike accidents, and identification of vulnerable sections affected by lightning strikes. It has significant practical engineering application value and promotion prospects.

[0045] Furthermore, the method involved in this application has been verified on a typical lightning traveling wave sample set of actual 220kV transmission lines in the Yunnan power grid. The constructed parameter inversion mechanism has extremely high engineering adaptability and is applicable to the identification and inversion of multiple lightning parameters of transmission lines, providing solid data support and decision-making basis for the dynamic optimization of power grid lightning protection strategies and post-lightning accident assessment. Attached Figure Description

[0046] Figure 1 This is a flowchart illustrating a multi-source data fusion method for multiple lightning strike inversion, as described in an embodiment of this application.

[0047] Figure 2 This is a schematic diagram of the fitness value iteration results based on the tunic group algorithm involved in the embodiments of this application;

[0048] Figure 3 This is a schematic diagram of the current inversion waveform for multiple lightning strike events involved in the embodiments of this application;

[0049] Figure 4 This is a schematic diagram of the hardware operating environment of the computer system involved in the embodiments of this application.

[0050] The realization of the purpose, functional features and advantages of this application will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0051] To better understand the above technical solutions, exemplary embodiments of this disclosure will be described in more detail below with reference to the accompanying drawings. While exemplary embodiments of this disclosure are shown in the drawings, it should be understood that this disclosure can be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of this disclosure to those skilled in the art.

[0052] First Embodiment

[0053] Reference Figure 1 This embodiment provides a multi-source data fusion method for multiple lightning strike inversion, which is applied to power systems including lightning location systems, traveling wave devices, and waveform recording devices.

[0054] The method includes the following steps:

[0055] S10, acquire lightning location data collected by the lightning location system, traveling wave lightning fault data collected by the traveling wave device, and recording fault data collected by the recording device.

[0056] In some alternative implementations, the lightning location data recorded by the lightning location system includes the time of each lightning strike, the polarity of the lightning strike, the peak value of the lightning current, the latitude and longitude location, the name of the location station, the number of the nearest tower and the line information to which it belongs.

[0057] In some alternative implementations, the traveling wave device records lightning fault current waveform data, including three-phase current traveling wave signals, the timing of traveling wave abrupt changes, channel number, and device installation location.

[0058] In some alternative implementations, the fault waveform data recorded by the waveform recording device includes current and voltage waveform signals, event start and end times, device type, measurement point number, and installation tower information.

[0059] S20, based on the lightning location data, the traveling wave lightning strike fault data and the recorded fault data, filter out the target lightning strike data that is the same lightning strike event from the multiple lightning strike data, wherein the same lightning strike event is a lightning strike event that occurs at the same lightning strike location and within the same lightning strike time period.

[0060] In this step, under the premise of satisfying temporal consistency and spatial approximation, it is confirmed that the same lightning strike event and a set of traveling wave data and recorded wave data form an effective correspondence, thereby obtaining the target lightning strike data as the same lightning strike time and realizing the spatiotemporal fusion matching of multi-source data on lightning strikes.

[0061] In some alternative implementations, considering the complex terrain, the propagation characteristics of lightning electromagnetic waves, and the latency of the acquisition equipment, a time difference threshold is used to coarsely align the three data sources for complex plateau and mountainous terrain. Setting a reasonable time difference threshold is crucial, primarily considering three factors: lightning signal propagation time, equipment sampling rate, and clock synchronization error. Based on the error range of different data sources, the time difference threshold ΔT can be set as shown in the table below:

[0062] Table 1. Time difference thresholds for different data matching types

[0063]

[0064] In this embodiment, the interval between lightning strikes is calculated from the recorded waveform data, and the peak lightning current is taken as the peak lightning current of the traveling wave fault.

[0065] S30, extract the strike interval and low-frequency dynamic features from the target lightning strike data, and construct a multiple lightning strike function based on the strike interval and the low-frequency dynamic features;

[0066] Further and optionally, the low-frequency dynamic characteristics include amplitude, initial waveform steepness, wavefront time constant, and wavetail time constant, wherein the multiple lightning strike function The expression is:

[0067]

[0068] in, ,

[0069] In the formula, and This represents the first and second amplitude values ​​of two consecutive lightning strikes. and This represents the steepness of the first and second initial waveforms between two consecutive lightning strikes. and The first and second wavefront time constants are the time constants of two consecutive lightning strikes. and The first and second tail time constants of two consecutive lightning strikes are given. and These are two amplitude correction factors for two consecutive lightning strikes. This refers to the interval between two consecutive lightning strikes.

[0070] S40, the tunic algorithm is used to perform parameter inversion on the multiple lightning strike function, and the obtained lightning current parameters are output.

[0071] In this step, based on the constructed multiple lightning strike function, the parameters such as amplitude, wavefront time constant, wave tail time constant, and inter-strike interval in the multiple lightning strike function are used as variables to be optimized. A suitable fitness function is set, and the swarm intelligence optimization algorithm of the tunicate is used to iteratively search for these parameters, and finally obtain the optimal parameter set to realize the inversion of the lightning current of multiple lightning strikes.

[0072] Specifically, the following steps are included:

[0073] S41, taking the low-frequency dynamic characteristics and inter-strike interval in the multiple lightning strike function as individual *Symplocos salpinx*, and setting the search space as one... A European-style space, in which, Indicates the population size of salps. Dimensions representing the problem:

[0074]

[0075] The position of the i-th tunicate individual is represented as: Each individual can be generated using the following equation:

[0076] In the formula, This indicates the generation of a random number that is uniformly distributed between 0 and 1. Indicates the first The upper limit of dimensions Indicates the first The lower bound of the dimension;

[0077] It should be noted that, in this space, the distribution of the tunicate population in the search space can be clearly described by matrix X.

[0078] S42, calculate the fitness value of each tunicate individual, and rank the individuals in the population according to the fitness value. The following formula is the update formula for the leader position:

[0079]

[0080] in,

[0081] In the formula, Indicates the leader In the Position in 3D space; Indicates the first The location of the food source in 3D space; parameters , It is an interval The random number on, where This determines the current search step size, and This determines the search direction in the j-th dimension; The convergence factor is and These represent the current iteration count and the maximum iteration count, respectively.

[0082] It should be noted that the convergence factor It can balance the algorithm's global exploration and local development capabilities, ensuring that the population gradually converges to the optimal solution during the search process.

[0083] S43, the leader follows the formula Move towards the food source, approaching the current optimal solution; according to the formula Update the position of each follower, gradually bringing them closer to the leader;

[0084] in, ,

[0085] In the formula, The initial velocity, For acceleration, The final velocity of the motion, For the j-th dimension space Individual sea squirts, For the first The first dimension in 3D space Individual sea squirts;

[0086] S44, when the iteration termination condition is met, exit the iteration and take the current position of the individual tunicate as the optimal set of lightning current parameters.

[0087] Further, and optionally, the iteration conditions include one of the following:

[0088] (1) Reaching the preset maximum number of iterations N max ;

[0089] (2) The fitness function value is lower than the preset threshold ε;

[0090] (3) The fitness change is less than the threshold after several consecutive iterations.

[0091] In some optional implementations, to ensure that the Heidler function parameters during the optimization process conform to physical reality and international standards, the optimization variables are adjusted according to the lightning current waveform characteristics provided by IEEE 1410 and IEC 61312-1. , , , , , Apply mathematical constraints.

[0092] For optimization variables , , , , , The following constraints shall be imposed:

[0093] a) Rise time constant: ;

[0094] b) Descent time constant: ;

[0095] c) Steepness factor , Based on empirical constraints: .

[0096] By introducing constraints, the optimization process can improve the consistency between simulated and measured waveforms while ensuring the physical meaning of parameters, thus providing a more reliable theoretical basis for lightning protection design and power system overvoltage analysis.

[0097] Furthermore, and optionally, since the optimization objective of lightning strike inversion is to adjust the parameters of the Heidler function to calculate the simulated traveling wave data... Compared with measured traveling wave data The error between them is minimized. To achieve this goal, the Mean Square Error (MSE) is used as the objective function, and its mathematical expression is as follows:

[0098]

[0099] In the formula, This refers to the number of sampling points, i.e., the number of sampling points in the measured traveling wave data; The simulated traveling wave current value is obtained after calculation using the Heidler function; The measured traveling wave current value is given.

[0100] In the technical solution provided in this embodiment, multi-source lightning characteristic data are collected from three types of measuring devices: traveling wave, recording wave, and lightning location system. After synchronizing the data from the three sources in time and space, target lightning strike data belonging to the same lightning strike event in the same location and time period are determined. The inter-strike interval and low-frequency dynamic features in the target lightning strike data are extracted. Based on the constructed multiple lightning strike function, the key parameters in the multiple lightning strike function are iteratively searched using the tundra swarm algorithm to obtain the optimal parameter set after iteration, thereby realizing the inversion of the lightning current of multiple lightning strikes.

[0101] Verification of Examples

[0102] Based on the above embodiments, this embodiment verifies the effectiveness of the proposed multi-source data fusion method for multiple lightning strike inversion.

[0103] In some specific implementations, the following results are obtained: Figure 2 The diagram shows the iterative results of the fitness value based on the tunic group algorithm.

[0104] The results show that the optimization process exhibits a significant three-stage convergence characteristic. In the initial stage (0-200 generations), the MSE is on the order of 10⁸, indicating a large deviation between the initial parameters and the measured traveling wave data. In the intermediate optimization stage (200-500 generations), the MSE gradually decreases to the order of 10⁶-10⁴, reflecting that the algorithm gradually approaches the feasible solution region through global search. In the rapid convergence stage (500-700 generations), the MSE drops sharply to below 10², indicating a significant improvement in the matching degree between the calculated waveform and the measured data. After 700 generations, the algorithm enters the stable convergence stage, and the MSE finally converges to the order of 10¹, indicating that the algorithm has obtained near-optimal lightning current parameters.

[0105] The current inversion waveform of this multiple lightning strike event is as follows: Figure 3 As shown, the key parameters corresponding to the inversion results include the amplitude of the two discharges, the wavefront / wavetail time constant, the steepness factor, the correction factor, and the inter-strike interval, etc., which proves that the multi-source data fusion multiple lightning strike inversion method proposed in this application comprehensively characterizes the multi-stage evolution law of lightning current.

[0106] As one implementation scheme, Figure 4 This is a schematic diagram of the hardware operating environment of the computer system involved in the embodiments of this application.

[0107] like Figure 4 As shown, the computer system may include: a processor 1001, such as a CPU; a memory 1005; a user interface 1003; a network interface 1004; and a communication bus 1002. The communication bus 1002 is used to enable communication between these components. The user interface 1003 may include a display screen or an input unit such as a keyboard; optionally, the user interface 1003 may also include a standard wired interface or a wireless interface. The network interface 1004 may optionally include a standard wired interface or a wireless interface (such as a Wi-Fi interface). The memory 1005 may be high-speed RAM or non-volatile memory, such as a disk drive. Optionally, the memory 1005 may also be a storage device independent of the aforementioned processor 1001.

[0108] Those skilled in the art will understand that Figure 4 The computer system architecture shown does not constitute a limitation on the computer system and may include more or fewer components than shown, or combine certain components, or have different component arrangements.

[0109] like Figure 4As shown, the memory 1005, as a storage medium, may include an operating system, a network communication module, a user interface module, and computer programs. The operating system is a program that manages and controls the hardware and software resources of the computer system, as well as the operation of the computer programs and other software or programs.

[0110] exist Figure 4 In the computer system shown, the user interface 1003 is mainly used to connect to the terminal and communicate with the terminal; the network interface 1004 is mainly used to communicate with the backend server; and the processor 1001 can be used to call the computer program stored in the memory 1005.

[0111] In this embodiment, the computer system includes: a memory 1005, a processor 1001, and a computer program stored in the memory and executable on the processor, wherein:

[0112] When processor 1001 calls a computer program stored in memory 1005, it performs the following operations:

[0113] S10, acquire lightning location data collected by the lightning location system, traveling wave lightning fault data collected by the traveling wave device, and recording fault data collected by the recording device.

[0114] S20, based on the lightning location data, the traveling wave lightning strike fault data and the recorded fault data, filter out the target lightning strike data that is the same lightning strike event from the multiple lightning strike data, wherein the same lightning strike event is a lightning strike event that occurs at the same lightning strike location and within the same lightning strike time period.

[0115] S30, extract the strike interval and low-frequency dynamic features from the target lightning strike data, and construct a multiple lightning strike function based on the strike interval and the low-frequency dynamic features;

[0116] S40, the tunic algorithm is used to perform parameter inversion on the multiple lightning strike function, and the obtained lightning current parameters are output.

[0117] When processor 1001 calls a computer program stored in memory 1005, it performs the following operations:

[0118] S11, compare the lightning strike time and lightning strike location in the lightning location data with the fault record time and installation location in each of the traveling wave lightning fault data and / or the waveform recording fault data;

[0119] S12, the target lightning strike data, target lightning traveling wave lightning strike fault data, and target lightning recording wave fault data that satisfy the condition that the lightning strike time and the fault recording time are within a preset time window, and that the lightning strike location and the installation location are in the same area, are determined as the target lightning strike data as the same lightning strike event.

[0120] When processor 1001 calls a computer program stored in memory 1005, it performs the following operations:

[0121] The low-frequency dynamic characteristics include amplitude, initial waveform steepness, wavefront time constant, and wavetail time constant, wherein the expression for the multiple lightning strike function is:

[0122]

[0123] in, ,

[0124] In the formula, and This represents the first and second amplitude values ​​of two consecutive lightning strikes. and This represents the steepness of the first and second initial waveforms between two consecutive lightning strikes. and The first and second wavefront time constants are the time constants of two consecutive lightning strikes. and The first and second tail time constants of two consecutive lightning strikes are given. and These are two amplitude correction factors for two consecutive lightning strikes. This refers to the interval between two consecutive lightning strikes.

[0125] When processor 1001 calls a computer program stored in memory 1005, it performs the following operations:

[0126] S41, taking the low-frequency dynamic characteristics and inter-strike interval in the multiple lightning strike function as individual *Symplocos salpinx*, and setting the search space as one... A European-style space, in which, Indicates the population size of salps. Dimensions representing the problem:

[0127]

[0128] The position of the i-th tunicate individual is represented as: Each individual can be generated using the following equation:

[0129] In the formula, This indicates the generation of a random number that is uniformly distributed between 0 and 1. Indicates the first The upper limit of dimensions Indicates the first The lower bound of the dimension;

[0130] S42, calculate the fitness value of each tunicate individual, and rank the individuals in the population according to the fitness value. The following formula is the update formula for the leader position:

[0131]

[0132] in,

[0133] In the formula, Indicates the leader In the Position in 3D space; Indicates the first The location of the food source in 3D space; parameters , It is an interval The random number on, where This determines the current search step size, and This determines the search direction in the j-th dimension; The convergence factor is and These represent the current iteration count and the maximum iteration count, respectively.

[0134] S43, the leader follows the formula Move towards the food source, approaching the current optimal solution; according to the formula Update the position of each follower, gradually bringing them closer to the leader;

[0135] in, ,

[0136] In the formula, The initial velocity, For acceleration, The final velocity of the motion, For the j-th dimension space Individual sea squirts, For the first The first dimension in 3D space Individual sea squirts;

[0137] S44, when the iteration termination condition is met, exit the iteration and take the current position of the individual tunicate as the optimal set of lightning current parameters.

[0138] When processor 1001 calls a computer program stored in memory 1005, it performs the following operations:

[0139] The iteration termination condition includes at least one of the following:

[0140] (1) The preset maximum number of iterations Nmax is reached;

[0141] (2) The fitness function value is lower than the preset threshold ε;

[0142] (3) The fitness change is less than the threshold after several consecutive iterations.

[0143] When processor 1001 calls a computer program stored in memory 1005, it performs the following operations:

[0144] The objective function of the multiple lightning strike function includes:

[0145]

[0146] in: This refers to the number of sampling points, i.e., the number of sampling points in the measured traveling wave data; The simulated traveling wave current value is obtained after calculation using multiple lightning strike functions; The measured traveling wave current value is given.

[0147] Furthermore, 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 includes program instructions and can be stored in a storage medium, which is a computer-readable storage medium. The program instructions are executed by at least one processor in a computer system to implement the process steps of the embodiments of the above methods.

[0148] Therefore, this application also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the various steps of a multi-source data fusion method for multiple lightning strike inversion as described in the above embodiments.

[0149] The computer-readable storage medium can be any computer-readable storage medium capable of storing program code, such as a USB flash drive, portable hard drive, read-only memory (ROM), magnetic disk, or optical disk.

[0150] It should be noted that, since the storage medium provided in the embodiments of this application is the storage medium used to implement the methods of the embodiments of this application, those skilled in the art can understand the specific structure and variations of the storage medium based on the methods described in the embodiments of this application, and therefore will not be repeated here. All storage media used in the methods of the embodiments of this application fall within the scope of protection of this application.

[0151] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0152] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0153] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0154] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0155] It should be noted that any reference signs placed between parentheses in the claims should not be construed as limiting the claims. The word "comprising" does not exclude the presence of components or steps not listed in the claims. The word "a" or "an" preceding a component does not exclude the presence of a plurality of such components. This application can be implemented by means of hardware comprising several different components and by means of a suitably programmed computer. In a unit claim enumerating several means, several of these means may be embodied by the same item of hardware. The use of the words first, second, and third, etc., does not indicate any order. These words can be interpreted as names.

[0156] Although preferred embodiments of this application have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments as well as all changes and modifications falling within the scope of this application.

[0157] Obviously, those skilled in the art can make various modifications and variations to this application without departing from the spirit and scope of this application. Therefore, if such modifications and variations fall within the scope of the claims of this application and their equivalents, this application also intends to include such modifications and variations.

Claims

1. A multi-source data fusion method for multiple lightning strike inversion, characterized in that, Applied to power systems equipped with lightning location systems, traveling wave devices, and waveform recording devices, the method includes the following steps: S10, acquire lightning location data collected by the lightning location system, traveling wave lightning fault data collected by the traveling wave device, and recording fault data collected by the recording device. S20, based on the lightning location data, the traveling wave lightning strike fault data and the recorded fault data, filter out the target lightning strike data that is the same lightning strike event from the multiple lightning strike data, wherein the same lightning strike event is a lightning strike event that occurs at the same lightning strike location and within the same lightning strike time period. S30, extract the strike interval and low-frequency dynamic features from the target lightning strike data, and construct a multiple lightning strike function based on the strike interval and the low-frequency dynamic features; S40, The tunic algorithm is used to perform parameter inversion on the multiple lightning strike function and output the obtained lightning current parameters; The method employs the tunic algorithm to perform parameter inversion on the multiple lightning strike function, outputting the obtained lightning current parameters, including: S41, taking the low-frequency dynamic characteristics and inter-strike interval in the multiple lightning strike function as individual *Symplocos salpinx*, and setting the search space as one... The Euclidean space, in which, Indicates the population size of salps. Dimensions representing the problem: ； The position of the i-th tunicate individual is represented as: Each individual can be generated using the following equation: In the formula, This indicates the generation of a random number that is uniformly distributed between 0 and 1. Indicates the first The upper limit of dimensions, Indicates the first The lower bound of the dimension; S42, calculate the fitness value of each tunicate individual, and rank the individuals in the population according to the fitness value. The following formula is the update formula for the leader position: ； in, ; In the formula, Indicates the leader In the Position in 3D space; Indicates the first The location of the food source in 3D space; parameters , It is an interval The random number on, where This determines the current search step size, and This determines the search direction in the j-th dimension; The convergence factor is t' and These represent the current iteration count and the maximum iteration count, respectively. S43, the leader follows the formula Move towards the food source, approaching the current optimal solution; according to the formula Update the position of each follower, gradually bringing them closer to the leader; in, , ; In the formula, The initial velocity, For acceleration, The final velocity of the motion, For the j-th dimension space Individual sea squirts, For the first The first dimension in 3D space Individual sea squirts; S44, when the iteration termination condition is met, exit the iteration and take the current position of the individual tunicate as the optimal set of lightning current parameters.

2. The multi-source data fusion method for multiple lightning strike inversion as described in claim 1, characterized in that, S10 includes: S11, compare the lightning strike time and lightning strike location in the lightning location data with the fault record time and installation location in each of the traveling wave lightning fault data and / or the waveform recording fault data; S12, the target lightning strike data, target lightning traveling wave lightning strike fault data, and target lightning recording wave fault data that satisfy the condition that the lightning strike time and the fault recording time are within a preset time window, and that the lightning strike location and the installation location are in the same area, are determined as the target lightning strike data as the same lightning strike event.

3. The multi-source data fusion method for multiple lightning strike inversion as described in claim 1, characterized in that, In S30, the low-frequency dynamic characteristics include amplitude, initial waveform steepness, wavefront time constant, and wavetail time constant, wherein the expression for the multiple lightning strike function is: ； in, , ; In the formula, and This represents the first and second amplitude values ​​of two consecutive lightning strikes. and This represents the steepness of the first and second initial waveforms between two consecutive lightning strikes. and The first and second wavefront time constants are the time constants of two consecutive lightning strikes. and The first and second tail time constants of two consecutive lightning strikes are given. and These are two amplitude correction factors for two consecutive lightning strikes. This refers to the interval between two consecutive lightning strikes.

4. The multi-source data fusion method for multiple lightning strike inversion as described in claim 1, characterized in that, The iteration termination condition includes at least one of the following: (1) The preset maximum number of iterations Nmax is reached; (2) The fitness function value is lower than the preset threshold ε; (3) The fitness change is less than the threshold after several consecutive iterations.

5. The multi-source data fusion method for multiple lightning strike inversion as described in claim 1, characterized in that, The objective function of the multiple lightning strike function includes: ； in: This refers to the number of sampling points, i.e., the number of sampling points in the measured traveling wave data; The simulated traveling wave current value is obtained after calculation using multiple lightning strike functions; This is the measured traveling wave current value.

6. A computer system, characterized in that, The computer system includes: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the computer program is executed by the processor, it implements the steps of the multi-source data fusion multiple lightning strike inversion method as described in any one of claims 1 to 5.

7. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, implements the steps of the multi-source data fusion method for multiple lightning strike inversion as described in any one of claims 1 to 5.

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