A lightning strike fault detection method, system, and storage medium based on improved wavelet transform threshold denoising and wavelet entropy recognition.

Abstract

This application relates to the field of power system protection technology, and in particular to a lightning fault detection method, system, and storage medium based on improved wavelet transform threshold denoising and wavelet entropy identification. An improved wavelet threshold function is used to denoise the original acquired traveling wave signal, thereby enhancing the significant abrupt change characteristics in the lightning waveform. Furthermore, a wavelet entropy segmentation mechanism based on peak detection is proposed. By constructing the entropy function changes of signals at various scales under wavelet decomposition, the abrupt change information brought about by the lightning wavefront is automatically identified, achieving highly robust identification of lightning faults and non-lightning faults. The aim is to solve the problem of how to identify lightning faults.

Description

Technical Field

[0001] This application relates to the field of power system protection technology, and in particular to a lightning strike fault detection method, system and storage medium based on improved wavelet transform threshold denoising and wavelet entropy identification. Background Technology

[0002] In the current widespread deployment of power transmission networks, traveling wave acquisition devices have been widely used in transmission lines with voltage levels of 110kV and above to achieve fault location, waveform analysis and condition monitoring.

[0003] To ensure reliable recording even under weak fault conditions such as high-resistance grounding and small fault angle, the equipment usually adopts a low-threshold triggering mechanism. However, this will also collect a large number of interference signals during non-fault periods, resulting in a serious imbalance between the ratio of lightning-induced traveling waves and non-lightning-induced disturbance traveling waves.

[0004] In view of this, this application proposes an improved lightning strike detection method based on wavelet transform threshold denoising and wavelet entropy recognition. By leveraging the ability of wavelet transform thresholding to effectively suppress high-frequency noise interference and the sensitivity of wavelet entropy recognition to abrupt changes, the two methods are combined and improved upon traditional methods to better adapt to lightning strike fault detection scenarios. Summary of the Invention

[0005] The main purpose of this application is to provide a lightning strike fault detection method based on improved wavelet transform threshold denoising and wavelet entropy recognition, which aims to solve the problem of how to identify lightning strike faults.

[0006] To achieve the above objectives, this application provides a lightning strike fault detection method based on improved wavelet transform threshold denoising and wavelet entropy recognition, the method comprising:

[0007] Collect target three-phase current traveling wave signals on transmission lines with a sampling frequency greater than a preset frequency threshold;

[0008] The target three-phase current traveling wave signal is denoised based on an improved wavelet transform threshold function, wherein the expression of the improved wavelet transform threshold function is:

[0009]

[0010] In the formula, It is a unit step function. Soft threshold, The target is the three-phase current traveling wave signal. As a regulating factor, It is a smoothing factor;

[0011] Extract the local maxima in the denoised target three-phase current traveling wave signal and calculate the wavelet entropy corresponding to the local maxima;

[0012] When the wavelet entropy is greater than a preset threshold, a lightning strike fault is determined to have occurred.

[0013] Optionally, the step of calculating the wavelet entropy corresponding to the local maximum point includes:

[0014] Wavelet decomposition is performed on the local maxima to obtain wavelet coefficients at multiple scales;

[0015] Calculate the energy value corresponding to the wavelet coefficients at each scale;

[0016] Normalize each of the energy values ​​to obtain the energy distribution probability;

[0017] Substituting the energy distribution probability into the information entropy calculation formula, the wavelet entropy is obtained.

[0018] Optionally, the expression for the smoothing factor S in the improved wavelet transform threshold function is:

[0019]

[0020] In the formula, These are the preset control parameters for a smooth transition.

[0021] Optionally, the denoising effect of the improved wavelet transform threshold function is positively correlated with the adjustment factor, which is a variable.

[0022] Optionally, the size of the preset threshold is determined based on the mean of the local maxima and the standard deviation of the local maxima.

[0023] Optionally, the expression for the preset threshold includes:

[0024]

[0025] In the formula, μ(x) is the mean of the local maximum point x(t), σ(x) is the standard deviation of the local maximum point, and k is a constant used to control the sensitivity.

[0026] Optionally, before the step of denoising the target three-phase current traveling wave signal based on the improved wavelet transform threshold function, the method further includes:

[0027] Remove damaged channels from the target three-phase current traveling wave signal; and,

[0028] Remove abnormal outliers from the target three-phase current traveling wave signal.

[0029] Optionally, after the steps of extracting local maxima points from the denoised target three-phase current traveling wave signal and calculating the wavelet entropy corresponding to the local maxima points, the method further includes:

[0030] When the wavelet entropy is less than or equal to the preset threshold, it is determined that a non-lightning strike fault has occurred.

[0031] 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 lightning strike fault detection method based on improved wavelet transform threshold denoising and wavelet entropy recognition as described in any of the preceding claims.

[0032] 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 lightning strike fault detection method based on improved wavelet transform threshold denoising and wavelet entropy recognition as described in any of the preceding claims.

[0033] This application has at least the following beneficial effects:

[0034] 1. An improved wavelet thresholding method is used to denoise the original traveling wave data, which effectively improves the identifiability of lightning strike signals in low signal-to-noise ratio environments, reduces human intervention and the probability of misjudgment, and significantly reduces the workload of manually screening lightning strike waveforms.

[0035] 2. The proposed wavelet entropy segmentation identification mechanism still has stable identification performance when facing traveling wave data with noise interference or unclear boundaries, which improves the robustness and real-time performance of lightning strike detection.

[0036] 3. It can be achieved based on simple wavelet transform and entropy feature analysis, without the need for a large amount of computing resources and training samples. While ensuring recognition accuracy, it significantly reduces computing costs and is suitable for deployment in field embedded terminals or edge nodes, which is conducive to building a low-carbon and efficient protection system.

[0037] Furthermore, this method has been validated on a typical lightning traveling wave sample set of actual 220kV transmission lines in the Yunnan power grid. It has the advantages of no need for model training, low computational overhead, and strong adaptability, making it suitable for on-site online deployment and effectively improving the response efficiency and identification accuracy of lightning strike monitoring and protection systems in actual engineering projects. Attached Figure Description

[0038] Figure 1 This is a flowchart illustrating the lightning strike fault detection method based on improved wavelet transform threshold denoising and wavelet entropy recognition, which is involved in the embodiments of this application.

[0039] Figure 2 The graphs show the soft threshold function and hard threshold function involved in the embodiments of this application;

[0040] Figure 3 This is a waveform diagram of the original traveling wave signal involved in the embodiments of this application;

[0041] Figure 4 This is a waveform diagram of a denoised signal using a soft threshold function, as described in an embodiment of this application.

[0042] Figure 5 This is a waveform diagram of a denoised signal using a hard threshold function, as described in an embodiment of this application.

[0043] Figure 6 The image shows a waveform of a denoised signal using an improved threshold function, as described in an embodiment of this application.

[0044] Figure 7 This is a schematic diagram of the result after improved wavelet entropy processing under non-lightning fault I in the embodiments of this application;

[0045] Figure 8 This is a schematic diagram of the result after improved wavelet entropy processing under non-lightning fault II in the embodiments of this application;

[0046] Figure 9 This is a schematic diagram of the result after improved wavelet entropy processing under lightning fault I involved in the embodiments of this application;

[0047] Figure 10 This is a schematic diagram of the result after improved wavelet entropy processing under lightning strike fault II as described in the embodiments of this application.

[0048] Figure 11 This is a schematic diagram of the hardware operating environment of the computer system involved in the embodiments of this application.

[0049] 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

[0050] 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.

[0051] First Embodiment

[0052] Reference Figure 1This embodiment provides a lightning strike fault detection method based on improved wavelet transform threshold denoising and wavelet entropy recognition. The method includes the following steps:

[0053] Step S10: Collect the target three-phase current traveling wave signal on the transmission line with a sampling frequency greater than a preset frequency threshold;

[0054] In this embodiment, the target three-phase current traveling wave signal with a frequency greater than a preset frequency threshold is collected as a signal that may contain lightning strike or non-lightning strike disturbances.

[0055] In some optional implementations, the preset frequency threshold can be 10MHz, and the acquisition time window can be 16ms.

[0056] Step S20: Denoise the target three-phase current traveling wave signal based on an improved wavelet transform threshold function, wherein the expression of the improved wavelet transform threshold function is:

[0057]

[0058] In the formula, It is a unit step function. Soft threshold, The target is the three-phase current traveling wave signal. As a regulating factor, It is a smoothing factor;

[0059] In this embodiment, a threshold processing function that can balance denoising effect and signal fidelity is designed by combining the smoothing characteristics of the Sigmoid function.

[0060] Further, and optionally, the expression for the smoothing factor S in the improved wavelet transform threshold function is:

[0061]

[0062] In the formula, These are the preset control parameters for a smooth transition.

[0063] In some alternative implementations, k can be 1 and a can be 10.

[0064] Further and optionally, the denoising degree of the improved wavelet transform threshold function is positively correlated with the adjustment factor, which is a variable.

[0065] For example, refer to Figure 2 The graphs shown depict the soft threshold function and the hard threshold function. The target is the three-phase current traveling wave signal. To control the denoising effect of the result after thresholding the original function, an appropriate value can be set according to requirements. When the function approaches the soft threshold function, when... When the threshold is reached, the function approaches the hard threshold function.

[0066] In this step, the purpose of using the improved wavelet threshold function to denoise the original current traveling wave signal is to enhance the significant jump characteristics in the lightning waveform and suppress noise abrupt changes caused by non-lightning interference.

[0067] Step S30: Extract the local maxima in the denoised target three-phase current traveling wave signal and calculate the wavelet entropy corresponding to the local maxima.

[0068] After denoising, local maxima are extracted from the denoised target three-phase current traveling wave signal.

[0069] It should be noted that signals at local maxima typically carry more lightning strike fault information. Therefore, analyzing local maxima can filter out redundant information in traveling wave signals. On the other hand, wavelet entropy is highly sensitive to changes in local energy distribution, making it suitable for extracting physical features such as abrupt edges and steep wavefronts in lightning traveling waves, thereby enabling fast and accurate lightning strike identification in complex noise backgrounds.

[0070] For example, the extraction of local maxima points can be achieved using the following formula:

[0071] For a signal x(t), at a certain time point t, if the following conditions are met:

[0072]

[0073] Then t is a local maximum point.

[0074] The specific steps for calculating the wavelet entropy corresponding to the local maximum point include:

[0075] Step S31: Perform wavelet decomposition on the local maxima to obtain wavelet coefficients at multiple scales;

[0076] For example, for the signal Wavelet decomposition is performed to obtain the values ​​at each scale. wavelet coefficients ;in, It is a scale parameter; It is the translation parameter.

[0077] Step S32: Calculate the energy value corresponding to the wavelet coefficients at each scale;

[0078] For example, energy value The calculation formula:

[0079]

[0080] Step S33: Normalize each of the energy values ​​to obtain the energy distribution probability;

[0081] For example, the probability of energy distribution The calculation formula:

[0082]

[0083] Step S34: Substitute the energy distribution probability into the information entropy calculation formula to obtain the wavelet entropy.

[0084] For example, the formula for information entropy is:

[0085]

[0086] Among them, S w That is, wavelet entropy.

[0087] Step S40: When the wavelet entropy is greater than a preset threshold, it is determined that a lightning strike fault has occurred.

[0088] After calculating the wavelet entropy corresponding to the local maximum point, a threshold judgment method is used to analyze whether a lightning strike fault has occurred.

[0089] When the wavelet entropy is greater than a preset threshold, a lightning strike fault is determined to have occurred.

[0090] Further, and optionally, when the wavelet entropy is less than or equal to the preset threshold, a non-lightning strike fault is determined to have occurred. In some optional embodiments, the non-lightning strike fault can be a circuit short-circuit fault.

[0091] In the technical solution provided in this embodiment, an improved wavelet threshold function is used to denoise the original acquired traveling wave signal, thereby enhancing the significant abrupt change characteristics in the lightning waveform. Based on this, a wavelet entropy segmentation mechanism based on peak detection is further proposed. By constructing the entropy function changes of signals at each scale under wavelet decomposition, the abrupt change information brought about by the lightning wavefront is automatically identified, thereby achieving highly robust identification of lightning faults and non-lightning faults.

[0092] Second Embodiment

[0093] Based on the first embodiment, in this embodiment, to avoid inflexible segmentation due to a fixed threshold, this embodiment also provides a method for dynamically setting a preset threshold based on the signal amplitude, according to the statistical characteristics of the signal. Specifically:

[0094] The size of the preset threshold is determined based on the mean and standard deviation of the local maxima.

[0095] In a further and optional embodiment, the threshold θ(t) is set as a certain proportion of the current signal amplitude, defined using the mean or standard deviation of the signal; that is, the expression for the preset threshold includes:

[0096]

[0097] In the formula, μ(x) is the mean of the local maximum point x(t), σ(x) is the standard deviation of the local maximum point, and k is a constant used to control the sensitivity.

[0098] Third Embodiment

[0099] Based on any of the above embodiments, in order to further remove redundant parts in the signal, this embodiment also performs data cleaning on the acquired target three-phase current traveling wave signal before denoising, that is, before step S20, it also includes:

[0100] Step S50: Clear the damaged channels in the target three-phase current traveling wave signal; and,

[0101] In this embodiment, the presence of a concentrated cluster of values ​​in the sampled data is used to determine whether a channel is damaged. If the number of non-repeating values ​​is less than a preset threshold, the channel is generally considered to be completely damaged. Based on this, data removal and filtering strategies are employed to process the data from damaged channels, and the processing standards in the data analysis process are adjusted accordingly to ensure the accuracy and reliability of the data.

[0102] Step S60: Remove abnormal outliers from the target three-phase current traveling wave signal.

[0103] Furthermore, outliers in each segment of the single-channel current traveling wave data are detected by sliding a time window of appropriate length. The length of the sliding time window needs to be selected based on the data characteristics. Statistical analysis is performed on the data segments within each time window, typically optimized by combining the sampling frequency and traveling wave propagation characteristics. Statistical quantities such as mean and standard deviation are calculated, and outliers are identified based on these statistical quantities. If an outlier exists within a segment, it is removed, and the outlier in the original data is replaced using linear interpolation. In this way, the interference of outliers on the overall traveling wave data can be effectively removed, ensuring more accurate and reliable data processing results.

[0104] It should be noted that the execution order of steps S50 and S60 can be performed sequentially based on certain rules, and this embodiment does not impose any restrictions.

[0105] Fourth embodiment

[0106] Based on any of the above embodiments, after step S30, the method further includes: when the wavelet entropy is less than or equal to the preset threshold, determining that a non-lightning strike fault has occurred.

[0107] In this embodiment, if the wavelet entropy is less than or equal to the preset threshold, it indicates that the fault waveform is not affected by the lightning current and there is no lightning fault waveform with high-frequency transient energy injection, so it is classified as a non-lightning fault.

[0108] Verification of Examples

[0109] This embodiment verifies the effectiveness of the lightning strike fault detection method based on improved wavelet transform threshold denoising and wavelet entropy recognition.

[0110] For the noise reduction effect of the method, see [link to relevant documentation]. Figures 3-6 The waveforms of the original traveling wave signal, the denoised signal using a soft threshold function, the denoised signal using a hard threshold function, and the denoised signal using an improved threshold function are shown respectively.

[0111] It can be seen that the soft thresholding denoising method effectively suppresses noise, but the amplitude of the denoised signal is still slightly lower than the original signal, and some details may be smoothed out. While the soft thresholding method reduces noise, its effect on the signal is more uniform, resulting in the loss of detail in some peak areas. The hard thresholding method is prone to oscillations, especially in regions of rapid signal change, such as... Figure 4 This weakens the denoising effect. Especially in denoising experiments on the original traveling wave signal, neither the soft thresholding method nor the hard thresholding method effectively removes clutter noise, while the improved thresholding function method shows the best denoising effect. Processing the original signal using the improved thresholding method combined with the Sigmoid function can effectively preserve the characteristics of the fault traveling wave, particularly the signal's peak characteristics, thus better balancing denoising and signal fidelity.

[0112] For the method's effectiveness in distinguishing between lightning strike faults and non-lightning strike faults, please refer to [link / reference needed]. Figures 7-10 The diagrams show the results after improved wavelet entropy processing for non-lightning fault I, non-lightning fault II, lightning fault I, and lightning fault II, respectively.

[0113] It can be seen that the multi-scale entropy values ​​of lightning fault waveforms are generally higher than 0.1, while the entropy values ​​of non-lightning faults such as short circuits are sometimes smaller. Therefore, a threshold of 0.1 is set as the criterion: when the entropy value of all nodes exceeds 0.1 after wavelet transform, it is determined to be a lightning fault waveform with high-frequency transient energy injection; if, after wavelet transform, the entropy value of some nodes is less than 0.1, it indicates that the fault waveform is not affected by lightning current and should be classified as a non-lightning fault.

[0114] As one implementation scheme, Figure 11This is a schematic diagram of the hardware operating environment of the computer system involved in the embodiments of this application.

[0115] like Figure 11 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.

[0116] Those skilled in the art will understand that Figure 11 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.

[0117] like Figure 11 As 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.

[0118] exist Figure 11 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.

[0119] 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:

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

[0121] Collect target three-phase current traveling wave signals on transmission lines with a sampling frequency greater than a preset frequency threshold;

[0122] The target three-phase current traveling wave signal is denoised based on an improved wavelet transform threshold function, wherein the expression of the improved wavelet transform threshold function is:

[0123]

[0124] In the formula, It is a unit step function. Soft threshold, The target is the three-phase current traveling wave signal. As a regulating factor, It is a smoothing factor;

[0125] Extract the local maxima in the denoised target three-phase current traveling wave signal and calculate the wavelet entropy corresponding to the local maxima;

[0126] When the wavelet entropy is greater than a preset threshold, a lightning strike fault is determined to have occurred.

[0127] When processor 1001 calls a computer program stored in memory 1005, it performs the following operations:

[0128] Wavelet decomposition is performed on the local maxima to obtain wavelet coefficients at multiple scales;

[0129] Calculate the energy value corresponding to the wavelet coefficients at each scale;

[0130] Normalize each of the energy values ​​to obtain the energy distribution probability;

[0131] Substituting the energy distribution probability into the information entropy calculation formula, the wavelet entropy is obtained.

[0132] When processor 1001 calls a computer program stored in memory 1005, it performs the following operations:

[0133] The expression for the smoothing factor S in the improved wavelet transform threshold function is as follows:

[0134]

[0135] In the formula, These are the preset control parameters for a smooth transition.

[0136] When processor 1001 calls a computer program stored in memory 1005, it performs the following operations:

[0137] The denoising effect of the improved wavelet transform threshold function is positively correlated with the adjustment factor, which is a variable.

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

[0139] The preset threshold is determined based on the mean and standard deviation of the local maxima.

[0140] When processor 1001 calls a computer program stored in memory 1005, it performs the following operations:

[0141] The expression for the preset threshold includes:

[0142]

[0143] In the formula, μ(x) is the mean of the local maximum point x(t), σ(x) is the standard deviation of the local maximum point, and k is a constant used to control the sensitivity.

[0144] When processor 1001 calls a computer program stored in memory 1005, it performs the following operations:

[0145] Remove damaged channels from the target three-phase current traveling wave signal; and,

[0146] Remove abnormal outliers from the target three-phase current traveling wave signal.

[0147] When processor 1001 calls a computer program stored in memory 1005, it performs the following operations:

[0148] When the wavelet entropy is less than or equal to the preset threshold, it is determined that a non-lightning strike fault has occurred.

[0149] 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.

[0150] 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 the lightning strike fault detection method based on improved wavelet transform threshold denoising and wavelet entropy recognition as described in the above embodiments.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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 lightning strike fault detection method based on improved wavelet transform threshold denoising and wavelet entropy recognition, characterized in that, The method includes the following steps: Collect target three-phase current traveling wave signals on transmission lines with a sampling frequency greater than a preset frequency threshold; The target three-phase current traveling wave signal is denoised based on an improved wavelet transform threshold function, wherein the expression of the improved wavelet transform threshold function is: ； In the formula, It is a unit step function. Soft threshold, The target is the three-phase current traveling wave signal. As a regulating factor, It is a smoothing factor; Extract the local maxima in the denoised target three-phase current traveling wave signal and calculate the wavelet entropy corresponding to the local maxima; When the wavelet entropy is greater than a preset threshold, it is determined that a lightning strike fault has occurred; The step of calculating the wavelet entropy corresponding to the local maximum point includes: Wavelet decomposition is performed on the local maxima to obtain wavelet coefficients at multiple scales; Calculate the energy value corresponding to the wavelet coefficients at each scale; Normalize each of the energy values ​​to obtain the energy distribution probability; Substituting the energy distribution probability into the information entropy calculation formula, we obtain the wavelet entropy; The expression for the smoothing factor S in the improved wavelet transform threshold function is as follows: ； In the formula, Pre-set control parameters for smooth transition; The denoising effect of the improved wavelet transform threshold function is positively correlated with the adjustment factor, which is a variable. The value of the preset threshold is determined based on the mean of the local maxima and the standard deviation of the local maxima. The expression for the preset threshold includes: ； In the formula, μ(x) is the mean of the local maximum point x(t), σ(x) is the standard deviation of the local maximum point, and k is a constant used to control the sensitivity.

2. The lightning strike fault detection method based on improved wavelet transform threshold denoising and wavelet entropy recognition as described in claim 1, characterized in that, Before the step of denoising the target three-phase current traveling wave signal based on the improved wavelet transform threshold function, the method further includes: Remove damaged channels from the target three-phase current traveling wave signal; and, Remove abnormal outliers from the target three-phase current traveling wave signal.

3. The lightning strike fault detection method based on improved wavelet transform threshold denoising and wavelet entropy recognition as described in claim 1, characterized in that, After the steps of extracting local maxima points from the denoised target three-phase current traveling wave signal and calculating the wavelet entropy corresponding to the local maxima points, the method further includes: When the wavelet entropy is less than or equal to the preset threshold, it is determined that a non-lightning strike fault has occurred.

4. 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. When the computer program is executed by the processor, it implements the steps of the lightning strike fault detection method based on improved wavelet transform threshold denoising and wavelet entropy recognition as described in any one of claims 1 to 3.

5. 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 lightning strike fault detection method based on improved wavelet transform threshold denoising and wavelet entropy recognition as described in any one of claims 1 to 3.

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