Distribution line fault waveform denoising analysis method based on improved wavelet threshold function and computer equipment

Abstract

The invention provides a distribution line fault waveform denoising analysis method based on an improved wavelet threshold function and computer equipment, and relates to the technical field of power system fault detection and signal processing. The method comprises the following steps: S1, obtaining a fault waveform of a distribution line through actual measurement or analogue simulation, and converting the fault waveform into a fault signal; s2, through denoising effect comparison, selecting an applicable wavelet basis function and a decomposition layer number, and carrying out wavelet transformation to decompose a fault signal; s3, carrying out wavelet decomposition coefficient threshold processing on the fault signal by adopting a self-adaptive adjustment mechanism of a threshold and an improved threshold function; and S4, performing discrete wavelet inverse transformation on the wavelet decomposition coefficient subjected to threshold processing to reconstruct a signal so as to obtain a denoised fault signal. According to the method, through the improved threshold function and the multi-scale analysis technology, the fault waveform denoising effect can be remarkably improved, the accuracy and the real-time performance of fault detection are ensured, and therefore the reliability and the safety of a distribution line are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of power system fault detection and signal processing, and in particular to a distribution line fault waveform denoising analysis method and computer equipment based on an improved wavelet threshold function. Background Art

[0002] In modern power systems, the reliability and stability of distribution lines are crucial to the safe operation of the entire power grid. With the continuous growth of electricity demand, fault detection and location of distribution lines have become an important part of ensuring the continuity of power supply. However, distribution lines are affected by various factors during operation, such as lightning strikes, equipment aging, external force damage, bird line conflicts, etc., which can lead to short circuits, broken lines and other faults. In order to detect and handle these faults in a timely manner, it is usually necessary to accurately analyze and process the fault waveform.

[0003] In the prior art, wavelet transform is widely used for denoising and analyzing distribution line fault waveforms. As a commonly used signal processing tool, wavelet transform is widely used in fault detection and diagnosis of power systems due to its excellent performance in processing non-stationary signals. Wavelet transform can effectively extract characteristic information from signals by decomposing them into components of different scales. However, the traditional wavelet threshold denoising method has some significant shortcomings, which affects its practical application effect in complex power systems.

[0004] Traditional wavelet threshold functions, such as hard threshold function and soft threshold function, often show instability when processing complex non-stationary signals. The hard threshold function is discontinuous at the threshold point, which easily causes oscillation of the reconstructed signal, leading to the pseudo-Gibbs phenomenon and destroying the smoothness of the original signal. Although the soft threshold function overcomes this problem, it will introduce a constant deviation, which reduces the accuracy of the reconstructed signal and causes some high-frequency information to be lost.

[0005] Existing wavelet threshold methods usually use fixed thresholds, which are difficult to adapt to changes in noise levels and signal characteristics, lack flexibility and adaptability, and lead to large differences in denoising effects in different scenarios. For high-noise environments that may occur in power systems, denoising is often incomplete or causes signal components to be lost, which in turn affects the accurate identification of fault waveforms.

[0006] In addition, in the process of wavelet denoising, the high-frequency part of the signal is often identified as noise and removed, which leads to the loss of some useful high-frequency information when reconstructing the signal. Especially in the process of multi-scale decomposition, as the number of decomposition layers increases, the useful signal is gradually weakened or even lost, which eventually leads to signal distortion.

[0007] Therefore, the existing technology urgently needs an improved method that can adaptively adjust the threshold, optimize the threshold function design, and make full use of multi-scale analysis technology to overcome these shortcomings, improve the denoising effect of distribution line fault waveforms, and ensure the safe and stable operation of the power system. Summary of the invention

[0008] The purpose of the present invention includes providing a distribution line fault waveform denoising analysis method and computer equipment based on an improved wavelet threshold function, which can significantly improve the effect of fault waveform denoising through the improved threshold function and multi-scale analysis technology, ensure the accuracy and real-time performance of fault detection, and thus improve the reliability and safety of the distribution line.

[0009] The embodiments of the present invention can be implemented as follows:

[0010] In a first aspect, an embodiment of the present invention provides a distribution line fault waveform denoising analysis method based on an improved wavelet threshold function, the method comprising:

[0011] S1: Obtain the fault waveform of the distribution line through actual measurement or simulation, and convert it into a fault signal;

[0012] S2: By comparing the denoising effects, select the appropriate wavelet basis function and decomposition layer number, and perform wavelet transform to decompose the fault signal;

[0013] S3: Adopting the adaptive threshold adjustment mechanism and improved threshold function to perform wavelet decomposition coefficient threshold processing on the fault signal;

[0014] S4: reconstruct the signal by using the inverse discrete wavelet transform of the wavelet decomposition coefficients after threshold processing to obtain the denoised fault signal;

[0015] S5: Output the fault signal after denoising.

[0016] In an optional embodiment, S2 includes:

[0017] In order to select the wavelet basis function that is most suitable for denoising distribution line fault signals, different existing wavelet basis functions are selected for the fault signal, and the signal-to-noise ratio after denoising is calculated. Based on the denoising evaluation rule, the signal is simulated by adding Gaussian white noise to test the influence of each wavelet basis function on the denoising of the fault signal.

[0018] In an optional embodiment, S2 includes:

[0019] In order to select the most suitable decomposition layer number for fault signal denoising, the influence of different decomposition layers on fault signal denoising was tested, and the denoising effect was compared. The signal was simulated by adding Gaussian white noise to test the influence of different decomposition layers on fault signal denoising.

[0020] In an optional embodiment, S2 includes:

[0021] By comparing the denoising effects, the db5 wavelet basis function and 5 decomposition layers are selected to perform wavelet transform to decompose the fault signal.

[0022] In an optional embodiment, S3 includes:

[0023] An adaptive threshold adjustment mechanism is introduced to dynamically adjust the threshold according to the noise level and signal characteristics, thereby optimizing the denoising effect.

[0024] In an optional implementation, in S3, the formula of the threshold adaptive adjustment mechanism is as follows:

[0025]

[0026]

[0027]

[0028] In the above formula, λ′ is the original threshold, λ is the threshold after adaptive adjustment, σ is the global noise standard deviation estimated based on the median absolute deviation of the first layer detail coefficients, N is the signal length, k is the level of the current wavelet decomposition, lev is the total number of wavelet decomposition levels, median(·) is the median function, which is defined to return the median of a given value, and cD1 is the detail coefficient of the first iteration of the decomposition level.

[0029] In an optional embodiment, S3 includes:

[0030] The parameter factor in the threshold function is adjusted to maximize the retention of the characteristics of the original signal while removing noise.

[0031] In an optional implementation, the threshold function is improved as follows:

[0032]

[0033] In the above formula, is the improved wavelet decomposition coefficient, w j,k is the original wavelet decomposition coefficient, j is the maximum decomposition scale, k is the length of the wavelet coefficient, the sgn function is used to maintain the sign of the coefficient, and the max function ensures that the coefficient value will not become negative.

[0034] In a second aspect, an embodiment of the present invention provides a computer device, comprising a processor and a non-volatile memory storing computer instructions. When the computer instructions are executed by the processor, the computer device executes any one of the above-mentioned distribution line fault waveform denoising and analysis methods based on the improved wavelet threshold function.

[0035] The distribution line fault waveform denoising analysis method and computer device based on the improved wavelet threshold function proposed in the embodiment of the present invention have the following significant advantages and technical effects compared with the prior art:

[0036] (1) Adaptive threshold adjustment: The improved wavelet threshold function introduces an adaptive threshold adjustment mechanism, which enables the threshold to be dynamically adjusted according to the noise level and signal characteristics. This improvement greatly improves the denoising effect, especially in high-noise environments. It can effectively remove noise while retaining more useful signal components, reducing signal distortion and ensuring the complete retention of fault waveform characteristics. Compared with the traditional fixed threshold method, it significantly reduces signal distortion and improves the signal fidelity and the ability to accurately identify fault waveforms.

[0037] (2) Multi-scale analysis: This method uses multi-scale analysis technology to further improve the accuracy of denoising. Denoising is performed at different scales so that each frequency component of the signal can be properly processed, and noise can be identified and removed at different scales. This multi-scale analysis method not only improves the denoising effect, but also retains the detailed characteristics of the signal (low-frequency fault characteristics), making fault location more accurate and reliable. Compared with the traditional wavelet denoising method, the method provided in the embodiment of the present invention can more effectively deal with complex non-stationary signals and show higher accuracy and stability in fault diagnosis.

[0038] (3) Threshold function improvement: The improved wavelet threshold function combines the advantages of hard threshold and soft threshold. By adjusting the parameter factors in the threshold function, the original signal characteristics are retained to the maximum extent while removing noise. Compared with the traditional wavelet threshold denoising method, the method provided in the embodiment of the present invention further reduces the information loss in the denoising process while maintaining the original characteristics of the signal, thereby ensuring the overall quality of the reconstructed signal. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for use in the embodiments are briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present invention and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without creative work.

[0040] Figure 1 A flow chart of a distribution line fault waveform denoising analysis method based on an improved wavelet threshold function provided by an embodiment of the present invention;

[0041] Figure 2 This is the denoising effect diagram of each threshold function;

[0042] Figure 3 A structural block diagram of a computer device provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0043] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Generally, the components of the embodiments of the present invention described and shown in the drawings here can be arranged and designed in various different configurations.

[0044] Therefore, the following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the invention claimed for protection, but merely represents selected embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0045] It should be noted that similar reference numerals and letters denote similar items in the following drawings, and therefore, once an item is defined in one drawing, further definition and explanation thereof is not required in subsequent drawings.

[0046] In the description of the present invention, it should be noted that if the terms "upper", "lower", "inside", "outside", etc. appear to indicate an orientation or position relationship, they are based on the orientation or position relationship shown in the accompanying drawings, or are the orientation or position relationship in which the product of the invention is usually placed when used. They are only for the convenience of describing the present invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as a limitation on the present invention.

[0047] In addition, the terms “first”, “second”, etc., if used, are merely used to distinguish between the descriptions and should not be understood as indicating or implying relative importance.

[0048] It should be noted that, in the absence of conflict, the features in the embodiments of the present invention may be combined with each other.

[0049] Please refer to Figure 1 This embodiment provides a distribution line fault waveform denoising analysis method based on an improved wavelet threshold function (hereinafter referred to as: method). The method improves the traditional wavelet threshold denoising method to improve the signal processing effect of the distribution line fault waveform.

[0050] The principle of wavelet threshold denoising method is that after the signal with interference noise is decomposed by wavelet, the energy of the signal to be extracted and retained is mainly concentrated in the area with large amplitude of wavelet decomposition coefficient, while the energy of the interference signal is mainly distributed in the area with small amplitude of wavelet decomposition coefficient. Therefore, the threshold can be set to an appropriate judgment threshold as the criterion for determining whether the wavelet decomposition coefficient is retained after decomposition.

[0051] The specific operation process is: the wavelet decomposition coefficients less than the judgment threshold are used as the wavelet decomposition coefficients of the interference signal, which are filtered out, and the remaining coefficients are used as the wavelet decomposition coefficients of the effective signal. The wavelet decomposition coefficients retained as the effective part are inversely transformed using the preset rules, and the reconstructed signal is regarded as the denoised signal after filtering out the clutter.

[0052] The method comprises the following steps:

[0053] S1: Obtain the fault waveform of the distribution line through actual measurement or simulation, and convert it into a fault signal.

[0054] Specifically, a one-dimensional array of fault waveforms may be used as a fault signal containing noise.

[0055] S2: By comparing the denoising effects, select the appropriate wavelet basis function and decomposition layer number, and perform wavelet transform to decompose the fault signal.

[0056] Among them, appropriate wavelet basis functions and decomposition levels are selected to perform wavelet decomposition on noisy signals. Smooth signals use smooth wavelet basis functions; symmetrical signals use symmetrical wavelet basis functions; if the signal meets two or three properties, the corresponding wavelet basis functions are used at the same time. The peak signal-to-noise ratio is used as the criterion for judging the performance of the wavelet basis function. Then, the number of decomposition levels of the signal is determined according to relevant indicators, and the noisy signal is decomposed by wavelet threshold to obtain the corresponding decomposition coefficient. For more complex signals, the number of decomposition levels is generally selected to be 3 to 5.

[0057] Different wavelet bases have different time-frequency characteristics. In this embodiment, in order to select the wavelet basis function that is most suitable for denoising the distribution line fault signal, different existing wavelet basis functions are selected for the fault signal, the signal-to-noise ratio after denoising is calculated, and based on the noise reduction evaluation rule, the signal is simulated by adding Gaussian white noise to test the influence of the wavelet base haar, Sym, db and coif functions on the denoising of the fault signal. The signal-to-noise ratio after denoising of each wavelet basis function is shown in Table 1 below.

[0058] Table 1 Denoising effect of each wavelet basis function

[0059] Wavelet basis functions Signal-to-noise ratio (dB) harr 23.5592 sym8 32.4022 db5 32.5504 coif2 31.5430

[0060] Table 1 shows the denoising results of each wavelet basis function on the simulation signal under the same conditions, and each wavelet system only records the wavelet with the best denoising effect. Among them, the db5 wavelet in the db wavelet system has the best denoising effect on the simulation signal. Therefore, in this embodiment, the db5 wavelet basis function is selected as the wavelet basis function of the wavelet threshold algorithm.

[0061] In the selection of wavelet basis functions, although the present embodiment uses the Daubechies (db) wavelet basis function as the main wavelet basis function, other wavelet bases such as Symlet wavelet, Coiflet wavelet, Haar wavelet, etc. can also be used for signal decomposition and reconstruction. These wavelet basis functions have their own advantages in different signal analysis scenarios, and the most suitable wavelet basis function can be selected according to the actual application to achieve the best denoising effect.

[0062] Among them, in order to select the most suitable decomposition layer number for fault signal denoising, the influence of 2, 3, 4, 5, 6, and 7 different decomposition layers on fault signal denoising was tested, and the denoising effect was compared. The signal was simulated by adding Gaussian white noise to test the influence of different decomposition layers on fault signal denoising. Table 2 shows the denoising effect of each decomposition layer on the fault signal under the db5 wavelet basis function.

[0063] Table 2 Denoising effect of each decomposition layer

[0064] Decomposition level Signal-to-noise ratio (dB) 2 26.3255 3 28.6824 4 32.5504 5 34.9538 6 33.6613 7 32.6505

[0065] It can be seen from Table 3 that as the number of decomposition layers increases, the signal-to-noise ratio increases first and then decreases. This is because when the number of decomposition layers is too small, the signal denoising is not thorough and the denoising effect is not ideal. A higher number of decomposition layers can bring better denoising effects, but when the number of decomposition layers is too high, the effective information of the signal is seriously lost, which will lead to a decrease in the signal-to-noise ratio and an increase in the amount of calculation, which will slow down the processing speed of the algorithm. For the fault signal of this embodiment, selecting 5 decomposition layers can achieve the highest signal-to-noise ratio, so the subsequent denoising analysis uses 5 decomposition layers for wavelet transform.

[0066] In summary, this embodiment selects the db5 wavelet basis function and 5 decomposition layers to perform wavelet transform to decompose the fault signal through comparison of denoising effects.

[0067] S3: Adopting the adaptive threshold adjustment mechanism and improved threshold function to the fault signal, perform wavelet decomposition coefficient threshold processing.

[0068] Among them, with the help of appropriate threshold function and threshold parameter, the decomposed wavelet decomposition coefficients are threshold processed. The construction of threshold function is very important for denoising. The improved threshold function not only improves the discontinuous nature of the hard threshold function and retains the mutation characteristics of the signal, but also improves the constant deviation problem of the soft threshold function to achieve a more perfect noise reduction effect and provide an accurate data basis for subsequent processing. The selection of threshold will also affect the denoising effect. If the threshold is too large, part of the useful information of the reconstructed signal will be missing; if the threshold is too small, too many noise components will be retained.

[0069] In this embodiment, a threshold adaptive adjustment mechanism is introduced to dynamically adjust the threshold according to the noise level and signal characteristics, thereby optimizing the denoising effect. The specific formula is as follows:

[0070]

[0071]

[0072]

[0073] In the above formula, λ′ is the original threshold, λ is the threshold after adaptive adjustment, σ is the global noise standard deviation estimated based on the median absolute deviation of the first layer detail coefficient, N is the signal length, k is the level of the current wavelet decomposition, lev is the total number of wavelet decomposition layers, median(·) is the median function, defined as returning the median of a given value, and cD1 is the detail coefficient of the first iteration of the decomposition layer. The threshold is dynamically adjusted through the coefficients of each layer, taking into account the length and number of layers of the signal. This adjustment method increases the threshold accordingly as the number of layers increases.

[0074] In wavelet denoising, choosing a suitable threshold function is one of the key factors that determine the denoising effect. The main task of the threshold function is to determine how to process the wavelet decomposition coefficients to achieve the best noise suppression effect while retaining the important information of the signal as much as possible. If the wavelet decomposition coefficient is less than the threshold, the wavelet decomposition coefficient is set to zero. If the wavelet decomposition coefficient is greater than the threshold, the wavelet decomposition coefficient should be retained or modified.

[0075] The traditional hard threshold function is discontinuous at the threshold point, which can easily cause oscillation of the reconstructed signal, generate jump points, and cause pseudo-Gibbs phenomenon, which destroys the smoothness of the original signal; the traditional soft threshold function overcomes the problem of discontinuity of the hard threshold function, and the reconstructed signal obtained has better smoothness, but there is a constant deviation between the original coefficients of the soft threshold function and the wavelet decomposition coefficients, which will cause the reconstructed image boundary to be blurred and some high-frequency information to be missing. Both of these threshold functions cause certain deviations in the wavelet coefficients after signal denoising, resulting in large errors when reconstructing the signal.

[0076] In order to overcome the above-mentioned shortcomings of the traditional hard threshold function and the soft threshold function, this embodiment proposes an improved threshold function, which can be flexibly adjusted by changing the parameter factor between the values ​​of the hard threshold and the soft threshold function. The improved threshold function is as follows:

[0077]

[0078] In the above formula, is the improved wavelet decomposition coefficient, w j,k is the original wavelet decomposition coefficient, j is the maximum decomposition scale, k is the length of the wavelet coefficient, the sgn function is used to maintain the coefficient sign, and the max function ensures that the coefficient value does not become negative (this prevents the introduction of additional distortion due to over-adjustment during the denoising process). The threshold depends on the current wavelet decomposition level (k) and the total number of wavelet decomposition levels (lev). The threshold increases with the number of levels, and denoising is achieved by applying an adaptive soft threshold method to subtract half of the threshold from each coefficient and re-assign it.

[0079] The improved threshold function adopts an adaptive soft threshold method and performs layer-dependent adjustment on the threshold before applying it. In this embodiment, the improved wavelet threshold function combines the advantages of hard threshold and soft threshold, and by adjusting the parameter factors in the threshold function, the characteristics of the original signal are retained to the maximum extent while removing noise.

[0080] In this way, according to the automatic adjustment of the k value, it is divided into different levels, which is equivalent to the fault waveform being decomposed into components of multiple scales, and denoising is performed separately at each scale. This multi-scale analysis technology not only improves the fidelity of the signal, but also enhances the adaptability to different types of fault signals, ensuring that the denoised signal can accurately reflect the fault characteristics.

[0081] In terms of multi-scale analysis, other signal decomposition techniques, such as empirical mode decomposition (EMD) or ensemble empirical mode decomposition (EEMD), can be considered to replace wavelet transform. These techniques can show stronger adaptability in specific signal environments, especially when processing complex signals with nonlinear and non-stationary characteristics, and may provide more sophisticated denoising results.

[0082] The adaptive adjustment mechanism of the threshold and the improved threshold function realize adaptive soft threshold processing based on hierarchical dynamic adjustment. The threshold is applied layer by layer, the wavelet decomposition coefficients are adjusted, and then the modified coefficients are used to reconstruct the denoised signal. This method is particularly suitable for processing signals with inhomogeneous or complex noise characteristics. By adjusting the threshold layer by layer, the denoising process can be controlled more finely and false positives (i.e., the signal is mistakenly removed as noise) can be reduced, especially in the high-frequency details of the signal. This method helps to retain the important features and details of the original signal as much as possible while removing noise, thereby improving the denoising quality. S4: The wavelet decomposition coefficients that have been thresholded are reconstructed through the inverse discrete wavelet transform (IDWT) to obtain the denoised fault signal.

[0083] Among them, in wavelet transform theory, continuous wavelet transform (CWT) and discrete wavelet transform (DWT) are two main transform methods. Discrete wavelet transform uses a specific wavelet system and step size to decompose the signal into multiple levels. By recursively applying wavelet transform to the low-frequency part of each level, a multi-resolution analysis of a signal can be constructed, which is particularly suitable for data compression and noise removal.

[0084] After the fault signal is processed by wavelet denoising, the fault signal after noise filtering is reconstructed through inverse transform. Among them, the inverse wavelet transform is opposite to the wavelet transform, which combines the wavelet decomposition coefficients and reconstructs the signal back to the time domain. At the same time, the inverse transform needs to use the same wavelet basis function as the forward transform.

[0085] S5: Output the fault signal after denoising.

[0086] In addition, bird faults in overhead transmission and distribution lines are divided into: bird nest faults, bird droppings faults, bird body short circuit faults and bird pecking faults. For distribution lines in grassland wetlands, bird faults are mainly bird body short circuit faults and bird nest faults, among which bird body short circuit faults are the main cause of distribution line faults. Bird body short circuit faults are mostly presented as single-phase grounding or phase-to-phase short circuits. These faults will generate specific fault waveforms in the power signal. Due to the different characteristics of the fault flashover channel, the transient current waveform characteristics at the moment of fault formation are also different.

[0087] The distribution line fault waveform denoising analysis method based on the improved wavelet threshold function proposed in the embodiment of the present invention can be used to analyze, diagnose and prevent distribution line faults caused by bird short circuits. The method relies on advanced signal processing technology and a distribution line fault waveform denoising algorithm based on an improved wavelet threshold function to accurately identify and classify fault waveforms caused by bird short circuits and bird nests. By real-time monitoring of the current and voltage waveforms of the power line, optimizing the wavelet threshold processing data, and quickly and accurately extracting fault features, early detection and early warning of faults can be achieved, the accuracy and response speed of fault detection can be improved, and the fault diagnosis capability of the power system can be enhanced.

[0088] In order to verify that the method provided by this embodiment is more ideal than the traditional denoising method, this embodiment uses Matlab for relevant programming, adds Gaussian white noise to the current signal of the single-phase fault phase, and simultaneously performs denoising processing on the noisy signal using three threshold functions, namely, improved threshold function, soft threshold function, and hard threshold function. The number of decomposition layers is 5. The denoising effect of each threshold function is analyzed through the obtained waveforms and related indicators. The simulation results are as follows Figure 2 As shown, from top to bottom are the relevant schematic diagrams of the simulated original signal, the noisy original signal, the improved threshold function denoising, the soft threshold function denoising and the hard threshold function denoising.

[0089] from Figure 2 It can be clearly seen that the waveform changes of the noisy fault signal after denoising by the three threshold functions. After the noisy fault signal is processed by the improved threshold, soft threshold and hard threshold, the relevant performance index data are obtained. The final signal-to-noise ratio and mean square error data are shown in Table 3.

[0090] Table 3 Performance indicators of each method for fault signals

[0091] method Signal-to-Noise Ratio Mean square error Noise source signal 20.1300 29.4081 Improved thresholding 34.9538 5.3368 Soft thresholding 32.6766 6.9365 Hard thresholding 24.8667 17.0463

[0092] according to Figure 2 From a visual and intuitive perspective, after the noisy signal is denoised by the hard threshold method, there will be some oscillation points distributed on the waveform, making the waveform of the signal relatively rough; after the soft threshold denoising process, the waveform obtained is relatively smoother than the waveform after the hard threshold process; after the noisy signal is processed by the improved threshold function, the waveform obtained has no obvious oscillation points, which means that the denoising effect of the improved threshold function is obviously better than that of the hard threshold function. At the same time, the waveform obtained is relatively smoother than the waveform after the soft threshold process, which also shows that the denoising effect of the improved threshold function is better than that of the soft threshold.

[0093] According to Table 3, from the objective data indicators obtained, after the noise signal is denoised by the traditional and improved three threshold function methods, the relevant data indicators show that among the three methods, the denoising effect of the soft threshold function is obviously better than that of the hard threshold. The signal-to-noise ratio of the denoising effect produced by the improved threshold function is the largest, and the mean square error is the smallest, indicating that the improved threshold function has the best denoising effect. Combining the waveform and data indicators, it can be concluded that the improved threshold function improves the denoising effect of the fault signal, indicating that this method is feasible and effective.

[0094] Please refer to Figure 3 The embodiment of the present invention further provides a computer device 100, which includes a processor and a non-volatile memory storing computer instructions. When the computer instructions are executed by the processor, the computer device 100 executes the above-mentioned distribution line fault waveform denoising analysis method based on the improved wavelet threshold function. Figure 3 As shown, Figure 3 This is a structural block diagram of a computer device 100 provided in an embodiment of the present invention. The computer device 100 includes a memory 111 , a processor 112 , and a communication unit 113 .

[0095] To achieve data transmission or interaction, the memory 111, the processor 112, and the communication unit 113 are electrically connected to each other directly or indirectly. For example, these components can be electrically connected to each other through one or more communication buses or signal lines. The processor 112 is used to execute the computer instructions stored in the memory 111 to implement the distribution line fault waveform denoising analysis method based on the improved wavelet threshold function in the above embodiment.

[0096] In one embodiment, the memory 111 is a non-volatile memory storing computer instructions. When the computer instructions are executed by the processor 112, the computer device executes the steps of the distribution line fault waveform denoising analysis method based on the improved wavelet threshold function involved in the above embodiment.

[0097] An embodiment of the present invention provides a readable storage medium, which includes a computer program. When the computer program is running, it controls the computer device where the readable storage medium is located to execute the aforementioned distribution line fault waveform denoising analysis method based on the improved wavelet threshold function.

[0098] The distribution line fault waveform denoising analysis method and computer device based on the improved wavelet threshold function proposed in the embodiment of the present invention have the following significant advantages and technical effects compared with the prior art:

[0099] (1) Adaptive threshold adjustment: The improved wavelet threshold function introduces an adaptive threshold adjustment mechanism, which enables the threshold to be dynamically adjusted according to the noise level and signal characteristics. This improvement greatly improves the denoising effect, especially in high-noise environments. It can effectively remove noise while retaining more useful signal components, reducing signal distortion and ensuring the complete retention of fault waveform characteristics. Compared with the traditional fixed threshold method, it significantly reduces signal distortion and improves the signal fidelity and the ability to accurately identify fault waveforms.

[0100] (2) Multi-scale analysis: This method uses multi-scale analysis technology to further improve the accuracy of denoising. Denoising is performed at different scales so that each frequency component of the signal can be properly processed, and noise can be identified and removed at different scales. This multi-scale analysis method not only improves the denoising effect, but also retains the detailed characteristics of the signal (low-frequency fault characteristics), making fault location more accurate and reliable. Compared with the traditional wavelet denoising method, the method provided in the embodiment of the present invention can more effectively deal with complex non-stationary signals and show higher accuracy and stability in fault diagnosis.

[0101] (3) Threshold function improvement: The improved wavelet threshold function combines the advantages of hard threshold and soft threshold. By adjusting the parameter factors in the threshold function, the original signal characteristics are retained to the maximum extent while removing noise. Compared with the traditional wavelet threshold denoising method, the method provided in the embodiment of the present invention further reduces the information loss in the denoising process while maintaining the original characteristics of the signal, thereby ensuring the overall quality of the reconstructed signal.

[0102] The above is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any changes or substitutions that can be easily thought of by a person skilled in the art within the technical scope disclosed by the present invention should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

Claims

1. A distribution line fault waveform denoising analysis method based on improved wavelet threshold function, characterized in that: The method comprises: S1: Obtain the fault waveform of the distribution line through actual measurement or simulation, and convert it into a fault signal; S2: By comparing the denoising effects, select the appropriate wavelet basis function and decomposition layer number, and perform wavelet transform to decompose the fault signal; S3: Adopting the adaptive threshold adjustment mechanism and improved threshold function to perform wavelet decomposition coefficient threshold processing on the fault signal; S4: reconstruct the signal by using the inverse discrete wavelet transform of the wavelet decomposition coefficients after threshold processing to obtain the denoised fault signal; S5: Output the fault signal after denoising.

2. The method for denoising and analyzing distribution line fault waveforms based on improved wavelet threshold function according to claim 1, characterized in that S2 include: In order to select the wavelet basis function that is most suitable for denoising distribution line fault signals, different existing wavelet basis functions are selected for the fault signal, and the signal-to-noise ratio after denoising is calculated. Based on the denoising evaluation rule, the signal is simulated by adding Gaussian white noise to test the influence of each wavelet basis function on the denoising of the fault signal.

3. The method for denoising and analyzing distribution line fault waveform based on improved wavelet threshold function according to claim 2 is characterized in that S2 include: In order to select the most suitable decomposition layer number for fault signal denoising, the influence of different decomposition layers on fault signal denoising was tested, and the denoising effect was compared. The signal was simulated by adding Gaussian white noise to test the influence of different decomposition layers on fault signal denoising.

4. The method for denoising and analyzing distribution line fault waveform based on improved wavelet threshold function according to claim 3 is characterized in that: S2 includes: By comparing the denoising effects, the db5 wavelet basis function and 5 decomposition layers are selected to perform wavelet transform to decompose the fault signal.

5. The method for denoising and analyzing distribution line fault waveform based on improved wavelet threshold function according to claim 1, characterized in that: S3 includes: An adaptive threshold adjustment mechanism is introduced to dynamically adjust the threshold according to the noise level and signal characteristics, thereby optimizing the denoising effect.

6. The method for denoising and analyzing distribution line fault waveform based on improved wavelet threshold function according to claim 5 is characterized in that: In S3, the formula of the threshold adaptive adjustment mechanism is as follows: In the above formula, λ ′ is the original threshold, λ is the threshold after adaptive adjustment, σ is the global noise standard deviation estimated based on the median absolute deviation of the first layer detail coefficients, N is the signal length, k is the level of the current wavelet decomposition, lev is the total number of wavelet decomposition levels, median(·) is the median function, which is defined to return the median of a given value, and cD1 is the detail coefficient of the first iteration of the decomposition level.

7. The method for denoising and analyzing distribution line fault waveform based on improved wavelet threshold function according to claim 6, characterized in that: S3 includes: The parameter factor in the threshold function is adjusted to maximize the retention of the characteristics of the original signal while removing noise.

8. The method for denoising and analyzing distribution line fault waveform based on improved wavelet threshold function according to claim 7 is characterized in that: The improved threshold function is as follows: In the above formula, is the improved wavelet decomposition coefficient, w j,k is the original wavelet decomposition coefficient, j is the maximum decomposition scale, k is the length of the wavelet coefficient, the sgn function is used to maintain the sign of the coefficient, and the max function ensures that the coefficient value will not become negative.

9. A computer device, characterized in that: The computer device includes a processor and a non-volatile memory storing computer instructions. When the computer instructions are executed by the processor, the computer device executes the distribution line fault waveform denoising analysis method based on the improved wavelet threshold function as described in any one of claims 1 to 8.

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