A cable partial discharge signal adaptive denoising method and related equipment

Abstract

The application discloses a cable partial discharge signal adaptive denoising method and related equipment, which removes noise signals while retaining partial discharge pulse signal components to the maximum extent, ensures that the number of signal components of the partial discharge signal distortion is small, and greatly improves the accuracy of partial discharge detection; white noise components in the detection signal are eliminated by using time-frequency conversion and threshold processing, and discrete spectrum noise components in the remaining signal are separated by using a VMD method; the energy sum of the decomposition results under different intrinsic mode numbers is calculated, and the energy is used as the basis for determining the optimal intrinsic mode number; the kurtosis is used as the screening condition of different decomposition modes, compared with the set threshold, the partial discharge signal characteristic components are obtained, and the partial discharge signal after noise removal is obtained by reconstruction. The method can adaptively denoise various partial discharge signals, and can effectively denoise under low signal-to-noise ratio, and has strong applicability.

Description

Technical Field

[0001] This invention belongs to the field of online detection technology for power equipment and its application, specifically an adaptive noise reduction method for partial discharge signals of cables and related equipment. Background Technology

[0002] Partial discharge diagnosis is an effective means of assessing the insulation condition of power equipment. During long-term operation, power equipment suffers from insulation degradation due to manufacturing processes, installation damage, harsh environments, and prolonged exposure to complex operating conditions, resulting in partial discharge. Partial discharge occurs in the early stages of power equipment failure and is not only a major cause of insulation degradation but also serves as an effective tool for assessing cable insulation condition.

[0003] Partial discharge signals acquired during partial discharge diagnosis of power equipment are often subject to severe environmental noise interference, mainly including white noise and discrete spectrum noise. White noise is primarily thermal noise generated during power equipment operation, while discrete spectrum noise is mainly caused by power line carrier communication and radio interference. Effective partial discharge signals are submerged in the noise background, making further feature analysis impossible. Over the years, many scholars both domestically and internationally have proposed various partial discharge signal denoising algorithms, such as Wavelet Transform (WT), Empirical Mode Decomposition (EMD), and Variational Mode Decomposition (VMD). However, these techniques all have certain limitations. For example, WT requires pre-determining the partial discharge waveform characteristics, EMD suffers from mode aliasing and endpoint effects, and the decomposition effect of VMD is affected by the preset number of intrinsic modes K. These methods struggle to adaptively denoise complex partial discharge pulses in field tests; therefore, a method for adaptive denoising of partial discharge signals in complex noise environments is urgently needed. Summary of the Invention

[0004] This invention provides an adaptive denoising method and related equipment for cable partial discharge signals, which solves the problem that existing partial discharge signal denoising algorithms are difficult to adaptively denoise complex partial discharge pulses in field tests.

[0005] To achieve the above objectives, the present invention provides the following technical solution:

[0006] An adaptive noise reduction method for partial discharge signals in cables includes:

[0007] The first partial discharge signal containing noise was acquired during the test.

[0008] The first time-frequency matrix is ​​obtained by performing time-frequency transformation on the first partial discharge signal using STFT;

[0009] The second time-frequency matrix is ​​obtained by applying a threshold to the first time-frequency matrix;

[0010] The second partial discharge signal is obtained by performing time-frequency transformation on the second time-frequency matrix using ISTFT.

[0011] The initial variational mode decomposition (VMD) algorithm is used to decompose the second partial discharge signal to obtain decomposed modes and calculate the total modal energy. The number of intrinsic modes of the VMD algorithm is changed and the corresponding total decomposed mode energy is calculated. The optimal number of intrinsic modes of the VMD algorithm and the corresponding decomposition result are determined by the energy mutation criterion.

[0012] Based on the optimal intrinsic mode number and the corresponding decomposition results, the kurtosis of different modes is calculated, and the modal components with kurtosis greater than a set threshold are taken as effective modal components.

[0013] Data reconstruction is performed on the effective modal components to obtain the denoised third partial discharge signal.

[0014] Preferably, the step of using STFT to perform time-frequency transformation on the first partial discharge signal to obtain the first time-frequency matrix specifically includes:

[0015]

[0016] In the formula: X represents the first time-frequency matrix, x(t) i ) represents the i-th value in the first partial discharge signal acquired; N is the data length of the first partial discharge signal; g(·) represents the Gaussian window function, f m Represents a discrete-time variable, t n This represents a discrete frequency variable. Δt represents the time interval for acquiring the signal, t i This represents the i-th value of the acquired signal time series.

[0017] Preferably, the step of obtaining the second time-frequency matrix by applying a threshold to the first time-frequency matrix specifically involves:

[0018]

[0019] In the formula: X' represents the second time-frequency matrix, ε represents the standard deviation of the first time-frequency matrix X, q represents the influence factor, n is the total number of discrete frequency variables, and m is the total number of discrete time variables.

[0020] Preferably, the step of using ISTFT to perform time-frequency transformation on the second time-frequency matrix to obtain the second partial discharge signal specifically includes:

[0021]

[0022] x' represents the second partial discharge signal, t n f represents a discrete frequency variable. m Let M represent the window length of the Gaussian signal g(·), n be the total number of discrete frequency variables, m be the total number of discrete time variables, and t be the discrete-time variable. k This represents the k-th value of the acquired signal time series.

[0023] Preferably, the variational mode decomposition step of the second partial amplification signal includes:

[0024] Establish constraints:

[0025]

[0026] In the formula: K is the number of intrinsic modes, δ(t) represents the partial derivative with respect to time t, δ(t) is the Dirac function, * is the convolution symbol, and ω is the partial derivative with respect to time t. k U represents the center frequency of the k-th mode. k Let x'(t) represent the k-th intrinsic mode, and let x'(t) represent the second partial discharge signal.

[0027] By introducing the Lagrangian function for solution, the multiple modal components are obtained.

[0028]

[0029] In the formula, α is the preset balance parameter and λ is the Lagrange multiplier.

[0030] Preferably, the method for determining the optimal number of intrinsic modes and the corresponding intrinsic modes obtained from VMD decomposition is as follows: when θ K Greater than θ K-1 When K-1 is selected as the optimal number of eigenmodes, the corresponding decomposition result is taken as the optimal eigenmode.

[0031] Preferably, the method for calculating the kurtosis of different modes based on the optimal intrinsic mode number and the corresponding decomposition results is as follows:

[0032]

[0033] In the formula, K u It is the kurtosis of the k-th intrinsic mode, where k ranges from 1 to the optimal intrinsic mode number, and μ is the IMF of the k-th intrinsic mode. k The mean, E represents the calculated average;

[0034] An adaptive noise reduction system for partial discharge signals in cables includes:

[0035] Acquisition module: Used to acquire the first partial discharge signal containing noise during the test;

[0036] First time-frequency conversion module: used to perform time-frequency conversion on the first partial discharge signal using STFT to obtain the first time-frequency matrix;

[0037] Threshold processing module: used to apply threshold processing to the first time-frequency matrix to obtain the second time-frequency matrix;

[0038] Second time-frequency transformation module: used to perform time-frequency transformation on the second time-frequency matrix using ISTFT to obtain the second partial discharge signal;

[0039] The calculation module is used to decompose the second partial discharge signal using the initial variational mode decomposition (VMD) algorithm to obtain the decomposed modes and calculate the total modal energy; change the number of intrinsic modes of the VMD algorithm and calculate the corresponding total decomposed mode energy; and use the energy mutation criterion to determine the optimal number of intrinsic modes of the VMD algorithm and the corresponding decomposition result.

[0040] Effective modal component acquisition module: Calculates the kurtosis of different modes based on the optimal intrinsic mode number and the corresponding decomposition results, and takes the modal components with kurtosis greater than a set threshold as effective modal components;

[0041] Denoising module: Reconstructs the effective modal components to obtain the denoised third partial discharge signal.

[0042] A computer device includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of an adaptive noise reduction method for partial discharge signals in a cable.

[0043] A computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps of an adaptive noise reduction method for partial discharge signals in cables.

[0044] Compared with existing technologies, this invention has the following advantages: This invention provides an adaptive denoising method for cable partial discharge signals. While removing noise signals, it retains the components of the partial discharge pulse signal to the greatest extent, ensuring minimal signal distortion and significantly improving the accuracy of partial discharge detection. It utilizes time-frequency transformation and threshold processing to eliminate white noise components in the detected signal, and then uses the VMD method to separate discrete spectral noise components from the remaining signal. It calculates the total energy of the decomposition results under different intrinsic mode numbers, using energy as the basis for determining the optimal intrinsic mode number. By using kurtosis as a screening condition for different decomposition modes and comparing it with a set threshold, it obtains the characteristic components of the partial discharge signal and reconstructs the denoised partial discharge signal. This method can adaptively denoise various partial discharge signals and remains effective even at low signal-to-noise ratios, demonstrating strong applicability. Attached Figure Description

[0045] Figure 1 This is a flowchart of an adaptive noise reduction method for partial discharge signals in cables according to the present invention.

[0046] Figure 2 This is a waveform diagram of a noise-free partial discharge signal used in a simulation experiment of a cable partial discharge signal denoising method according to an embodiment of the present invention.

[0047] Figure 3 This is a waveform diagram of the partial discharge signal after superimposed noise used in a simulation experiment of a cable partial discharge signal denoising method according to an embodiment of the present invention.

[0048] Figure 4 The waveform of the partial discharge signal obtained after initial noise reduction through time-frequency transformation threshold processing is the second partial discharge signal.

[0049] Figure 5 It is the final denoised partial discharge signal, i.e., the third partial discharge signal;

[0050] Figure 6 This is the final decomposition result of the second partial discharge signal;

[0051] Figure 7 The third partial discharge signal obtained through screening;

[0052] Figure 8 This is a block diagram of an adaptive noise reduction system for partial discharge signals in cables according to the present invention. Detailed Implementation

[0053] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. The components of the embodiments of the present invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations.

[0054] Therefore, the following detailed description of the embodiments of the invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely to illustrate selected embodiments of the invention. All other embodiments obtained by those skilled in the art based on the embodiments of the invention without inventive effort are within the scope of protection of the invention.

[0055] It should be noted that similar labels and letters in the following figures indicate similar items. Therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures.

[0056] To enable those skilled in the art to better understand the technical solution of the present invention, the present invention will be further described in detail below with reference to the accompanying drawings.

[0057] like Figure 1 As shown, the present invention provides an adaptive noise reduction method for cable partial discharge signals, including S101 acquiring a first partial discharge signal containing noise during the test;

[0058] S102 uses STFT to perform time-frequency transformation on the first partial discharge signal to obtain the first time-frequency matrix;

[0059] S103 applies a thresholding process to the first time-frequency matrix to obtain the second time-frequency matrix;

[0060] S104 uses ISTFT to perform time-frequency transformation on the second time-frequency matrix to obtain the second partial discharge signal;

[0061] S105 uses the initial variational mode decomposition (VMD) algorithm to decompose the second partial discharge signal to obtain decomposed modes and calculate the total modal energy; changes the number of intrinsic modes of the VMD algorithm and calculates the corresponding total decomposed mode energy; uses the energy mutation criterion to determine the optimal number of intrinsic modes of the VMD algorithm and the corresponding decomposition result;

[0062] S106 calculates the kurtosis of different modes based on the optimal intrinsic mode number and the corresponding decomposition results, and takes the mode components with kurtosis greater than the set threshold as effective mode components.

[0063] S107 reconstructs the effective modal components to obtain a denoised third partial discharge signal.

[0064] In the embodiments described in this disclosure, Figure 2 Taking the pure partial discharge signal shown as an example, the signal length is 9000, the sampling frequency is 125MHz, and the time interval between sampling points is 8ns. This signal includes a double exponential pulse s1, a single exponentially decaying oscillating pulse s2, and a double exponentially decaying oscillating pulse s3, which are commonly used to simulate partial discharge signals. Their expressions are as follows:

[0065]

[0066] Where A1 is the amplitude of the double exponential pulse, A2 is the amplitude of the single exponential decaying oscillation pulse, A3 is the amplitude of the double exponential decaying oscillation pulse, τ1 and τ2 are the time constants of the double exponential pulse, fc1 represents the oscillation frequency of the single exponential decaying oscillation pulse, τ3 is the time constant of the single exponential decaying oscillation pulse, fc2 represents the oscillation frequency of the double exponential decaying oscillation pulse, τ4 and τ5 are the time constants of the double exponential decaying oscillation pulse, and t represents the time variable.

[0067] In noisy partial discharge signals, the noise includes white noise and discrete spectrum noise caused by power equipment and communication, etc. White noise is simulated using Gaussian white noise with a mean of 0 and a variance of 1. Discrete spectrum noise is represented by two superimposed sinusoidal signals, namely:

[0068]

[0069] Where A4 and A5 are the amplitudes of two discrete-spectrum noises, and fn1 and fn2 are the oscillation frequencies of two discrete-spectrum noises. The noisy partial discharge signal obtained by adding noise components is as follows: Figure 3 As shown, this signal can be used to represent the first partial discharge signal containing noise detected in the field test, and it can be clearly seen that... Figure 1 The three partial discharge pulse waveforms shown were submerged in noise and could not be distinguished.

[0070] In step S2, the short-time Fourier transform is used to perform a time-frequency transformation on the first partial discharge signal to obtain the first time-frequency matrix. This matrix is ​​a two-dimensional time-frequency complex matrix, and its calculation formula is as follows:

[0071]

[0072] In the formula: X represents the first time-frequency matrix, x(t) i ) represents the i-th value in the first partial discharge signal acquired; N is the data length of the first partial discharge signal, 9000; g(·) represents the Gaussian window function with length M = 200, f m Represents a discrete-time variable, t nThis represents a discrete frequency variable. Δt represents the time interval (8 ns) between signal acquisitions.

[0073] In step S3, the first time-frequency matrix is ​​subjected to threshold processing to obtain the second time-frequency matrix as follows:

[0074]

[0075] In the formula: X' represents the second time-frequency matrix, ε represents the standard deviation of the first time-frequency matrix X, and q represents the influence factor, which is 1.

[0076] The formula for calculating the second partial discharge signal obtained by performing a short-time inverse Fourier transform on the second time-frequency matrix in step S4 is as follows:

[0077]

[0078] In the formula: x' represents the second partial discharge signal.

[0079] In steps S2 to S4, the first partial discharge signal is subjected to time-frequency transformation to obtain a first time-frequency spectrum, and the noise-reduced second partial discharge signal is obtained through soft thresholding. Figure 3 As shown, some noise components have been filtered out, and the partial discharge pulse can be seen directly. However, some noise still exists, causing some distortion in the waveform of the partial discharge pulse.

[0080] In step S5, the initial variational mode decomposition (VMD) algorithm is used to decompose the second partial discharge signal to obtain decomposed modes and calculate the total mode energy. The number of intrinsic modes K in the VMD algorithm is continuously increased from 1 until the optimal number of intrinsic modes K and the corresponding decomposition result are determined according to the energy mutation criterion. The optimal number of intrinsic modes K is selected as the number of intrinsic modes when the total decomposed energy suddenly increases.

[0081] Step S5, which involves variational mode decomposition of the second partially amplified signal, includes:

[0082] Establish constraints:

[0083]

[0084] In the formula: K is the number of intrinsic modes, δ(t) represents the partial derivative with respect to time t, δ(t) is the Dirac function, * is the convolution symbol, and ω is the partial derivative with respect to time t. k U represents the center frequency of the k-th mode. k Let x'(t) represent the k-th intrinsic mode, and let x'(t) represent the second partial discharge signal.

[0085] By introducing the Lagrangian function for solution, the multiple modal components are obtained.

[0086]

[0087] In the formula, α is the preset balance parameter, and λ is the Lagrange multiplier;

[0088] In step S5, the method for calculating the energy difference is as follows:

[0089]

[0090] In the formula, E K It is the sum of eigenmode energies obtained from the decomposition when the eigenmode number is K, IMF k It is the k-th eigenmode obtained by decomposition when the number of eigenmodes is K, θ K E is obtained with eigenmode numbers K and K-1 K Difference.

[0091] In step S5, the method for determining the optimal number of intrinsic modes and the corresponding intrinsic modes obtained from VMD decomposition is as follows: when θ K Greater than θ K-1 When K-1 is selected as the optimal eigenmode number, the corresponding decomposition result is taken as the optimal eigenmode. The total energy under different mode numbers is as follows: Figure 5 As shown, the final optimal number of intrinsic modes is 6, and the final decomposition result of the second partial discharge signal is as follows. Figure 6 As shown, obvious partial discharge information can be seen in modes IMF2, IMF5, and IMF6.

[0092] In step S6, the formula for calculating kurtosis is:

[0093]

[0094] In the formula, K u It is the kurtosis of the k-th intrinsic mode, and μ is the IMF of the k-th intrinsic mode. k The mean value. E represents the calculated average value; the kurtosis threshold used to screen effective intrinsic modes is set to 5; the third partial discharge signal obtained through screening is as follows: Figure 7 As shown, the third partial discharge signal highly overlaps with the noise-free partial discharge signal used, demonstrating excellent noise reduction. Furthermore, all parameters are adaptively processed according to the characteristics of the signal itself, making it suitable for complex partial discharge signals and noise levels.

[0095] like Figure 8 As shown, the present invention also provides an adaptive noise reduction system for cable partial discharge signals, comprising:

[0096] Acquisition module: Used to acquire the first partial discharge signal containing noise during the test;

[0097] First time-frequency conversion module: used to perform time-frequency conversion on the first partial discharge signal using STFT to obtain the first time-frequency matrix;

[0098] Threshold processing module: used to apply threshold processing to the first time-frequency matrix to obtain the second time-frequency matrix;

[0099] Second time-frequency transformation module: used to perform time-frequency transformation on the second time-frequency matrix using ISTFT to obtain the second partial discharge signal;

[0100] The calculation module is used to decompose the second partial discharge signal using the initial variational mode decomposition (VMD) algorithm to obtain the decomposed modes and calculate the total modal energy; change the number of intrinsic modes of the VMD algorithm and calculate the corresponding total decomposed mode energy; and use the energy mutation criterion to determine the optimal number of intrinsic modes of the VMD algorithm and the corresponding decomposition result.

[0101] Effective modal component acquisition module: Calculates the kurtosis of different modes based on the optimal intrinsic mode number and the corresponding decomposition results, and takes the modal components with kurtosis greater than a set threshold as effective modal components;

[0102] Denoising module: Reconstructs the effective modal components to obtain the denoised third partial discharge signal.

[0103] An embodiment of the present invention provides a terminal device. This terminal device includes a processor, a memory, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the steps in the various method embodiments described above. Alternatively, when the processor executes the computer program, it implements the functions of each module / unit in the various device embodiments described above.

[0104] The computer program can be divided into one or more modules / units, which are stored in the memory and executed by the processor to complete the present invention.

[0105] The terminal device may be a desktop computer, laptop, handheld computer, or cloud server, etc. The terminal device may include, but is not limited to, a processor and a memory.

[0106] The processor may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc.

[0107] The memory can be used to store the computer program and / or module. The processor implements various functions of the terminal device by running or executing the computer program and / or module stored in the memory and calling the data stored in the memory.

[0108] If the modules / units integrated into the terminal device are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the methods of the above embodiments can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include: any entity or device capable of carrying the computer program code, recording media, USB flash drives, portable hard drives, magnetic disks, optical disks, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signals, telecommunication signals, and software distribution media, etc. It should be noted that the content included in the computer-readable medium can be appropriately added or removed according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable media do not include electrical carrier signals and telecommunication signals.

[0109] Although embodiments of the present invention have been described above in conjunction with the accompanying drawings, the present invention is not limited to the specific embodiments and application fields described above. The specific embodiments described above are merely illustrative and instructive, and not restrictive. Those skilled in the art, guided by the specification, can make many other modifications without departing from the scope of the claims of the present invention, and all of these modifications are within the scope of protection of the present invention.

Claims

1. An adaptive noise reduction method for partial discharge signals in cables, characterized in that, include: The first partial discharge signal containing noise was acquired during the test. The first time-frequency matrix is ​​obtained by performing time-frequency transformation on the first partial discharge signal using STFT; The second time-frequency matrix is ​​obtained by applying a threshold to the first time-frequency matrix; The second partial discharge signal is obtained by performing time-frequency transformation on the second time-frequency matrix using ISTFT. The initial variational mode decomposition (VMD) algorithm is used to decompose the second partial discharge signal to obtain decomposed modes and calculate the total modal energy. The number of intrinsic modes of the VMD algorithm is changed and the corresponding total decomposed mode energy is calculated. The optimal number of intrinsic modes of the VMD algorithm and the corresponding decomposition result are determined by the energy mutation criterion. Based on the optimal intrinsic mode number and the corresponding decomposition results, the kurtosis of different modes is calculated, and the modal components with kurtosis greater than a set threshold are taken as effective modal components. Data reconstruction is performed on the effective modal components to obtain the denoised third partial discharge signal; The specific steps for obtaining the second time-frequency matrix by applying a threshold to the first time-frequency matrix are as follows: In the formula: X ' represents the second time-frequency matrix, ε Represents the first time-frequency matrix X standard deviation q This represents the influencing factor, where n is the total number of discrete frequency variables and m is the total number of discrete time variables. The optimal number of intrinsic modes and the method for determining the intrinsic modes obtained from the corresponding VMD decomposition are as follows: when θ K Greater than θ K-1 hour, K -1 was selected as the optimal number of eigenmodes, and the corresponding decomposition result was taken as the optimal eigenmode. The method for calculating the kurtosis of different modes based on the optimal intrinsic mode number and the corresponding decomposition results is as follows: In the formula, K u It is the first k Kuness of each intrinsic mode k The value range is 1 - the optimal intrinsic mode number. μ It is the first k Individual eigenmodes IMF k The mean, E This indicates that the average value has been calculated.

2. The adaptive noise reduction method for partial discharge signals of cables according to claim 1, characterized in that, The specific steps for obtaining the first time-frequency matrix by performing time-frequency transformation on the first partial discharge signal using STFT are as follows: In the formula: X This represents the first time-frequency matrix. x ( t i ) represents the first partial discharge signal acquired. i One value; N The length of the first partial discharge signal is given; g(·) represents the Gaussian window function. f m Represents discrete frequency variables. t n Denotes a discrete-time variable, Δ t Indicates the time interval for signal acquisition. t i This represents the i-th value of the acquired signal time series.

3. The adaptive noise reduction method for partial discharge signals of cables according to claim 1, characterized in that, The specific steps for obtaining the second partial discharge signal by performing time-frequency transformation on the second time-frequency matrix using ISTFT are as follows: x 'Indicates the second partial discharge signal, f m Represents discrete frequency variables. t n Let M represent the window length of the Gaussian signal g(·), n be the total number of discrete frequency variables, and m be the total number of discrete time variables. This represents the k-th value of the acquired signal time series.

4. The adaptive noise reduction method for partial discharge signals of cables according to claim 1, characterized in that, The steps of variational mode decomposition of the second partial amplifier signal include: Establish constraints: In the formula: K The number of intrinsic modes, t This represents the partial derivative with respect to time t. δ ( t () represents the Dirac function, and * represents the convolution symbol. ω k Indicates the first k The center frequency of each mode u k Indicates the first k Each intrinsic mode, x '( t ) represents the second partial discharge signal; By introducing the Lagrangian function for solution, the multiple modal components are obtained. In the formula α These are preset balance parameters. λ It is a Lagrange multiplier.

5. An adaptive noise reduction system for partial discharge signals in cables, characterized in that, An adaptive noise reduction method for cable partial discharge signals according to any one of claims 1-4 includes: Acquisition module: Used to acquire the first partial discharge signal containing noise during the test; First time-frequency conversion module: used to perform time-frequency conversion on the first partial discharge signal using STFT to obtain the first time-frequency matrix; Threshold processing module: used to apply threshold processing to the first time-frequency matrix to obtain the second time-frequency matrix; Second time-frequency transformation module: used to perform time-frequency transformation on the second time-frequency matrix using ISTFT to obtain the second partial discharge signal; The calculation module is used to decompose the second partial discharge signal using the initial variational mode decomposition (VMD) algorithm to obtain the decomposed modes and calculate the total modal energy; change the number of intrinsic modes of the VMD algorithm and calculate the corresponding total decomposed mode energy; and use the energy mutation criterion to determine the optimal number of intrinsic modes of the VMD algorithm and the corresponding decomposition result. Effective modal component acquisition module: Calculates the kurtosis of different modes based on the optimal intrinsic mode number and the corresponding decomposition results, and takes the modal components with kurtosis greater than a set threshold as effective modal components; Denoising module: Reconstructs the effective modal components to obtain the denoised third partial discharge signal.

6. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the adaptive noise reduction method for cable partial discharge signals as described in any one of claims 1 to 4.

7. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the steps of the adaptive noise reduction method for cable partial discharge signals as described in any one of claims 1 to 4.

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