Noise suppression method and device for cable partial discharge signal, electronic equipment and storage medium

Abstract

The invention discloses a noise suppression method and device for a cable partial discharge signal, electronic equipment and a storage medium, and belongs to the field of power equipment detection.The method comprises the steps that time-frequency transformation is conducted on an initial partial discharge signal to be denoised, and a corresponding first complex number time-frequency matrix is obtained; a signal segment with the minimum residual error is selected from the initial partial discharge signals to serve as a background noise signal, and the power spectrum density corresponding to the background noise signal is calculated; calculating a corresponding current spectral subtraction factor and a current spectral bottom coefficient, and performing spectral subtraction denoising processing on the first complex time-frequency matrix according to the power spectral density, the current spectral subtraction factor and the current spectral bottom coefficient to obtain a denoised first complex time-frequency matrix; and performing time-frequency inverse transformation on the denoised first complex time-frequency matrix to obtain a denoised partial discharge signal, therefore, by implementing the method and the device, the problems that modal decomposition parameters depend on manual setting, the calculation efficiency is low and the subjectivity is strong in the prior art can be solved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of power equipment detection, and in particular to a noise suppression method and device for cable partial discharge signals, an electronic device and a storage medium. BACKGROUND

[0002] Partial discharge is an important precursor of insulation aging or defects of electrical equipment, and timely detection and analysis of its signals are of great significance to the safety of power system operation. Since the partial discharge signal is usually weak in amplitude and often occurs in a strong noise background environment, it is easy to be overwhelmed by power frequency interference, random noise or other forms of non-stationary noise, thereby affecting the subsequent discharge recognition, classification and positioning accuracy. Therefore, how to effectively suppress noise components while preserving the main characteristics of the discharge signal has become a key problem in partial discharge detection technology.

[0003] Existing noise reduction methods for partial discharge signals mainly include wavelet transform, empirical mode decomposition (EMD) and variational mode decomposition (VMD), but the modal decomposition parameters of these methods depend on manual setting, lack of adaptability, resulting in insufficient generalization ability, low computational efficiency and strong subjectivity. SUMMARY

[0004] The present application provides a noise suppression method and device for cable partial discharge signals, an electronic device and a storage medium, which can solve the problem of modal decomposition parameter dependence on manual setting, low computational efficiency and strong subjectivity in the prior art.

[0005] To solve the above technical problems, the present application provides a noise suppression method for cable partial discharge signals, comprising:

[0006] An initial partial discharge signal to be denoised is obtained, and time-frequency transformation is performed on the initial partial discharge signal to obtain a corresponding first complex time-frequency matrix;

[0007] A signal segment with the smallest residual from the initial partial discharge signal is selected as a background noise signal, and the power spectral density corresponding to the background noise signal is calculated;

[0008] The energy of each frame amplitude spectrum in the first complex time-frequency matrix is calculated, the corresponding current spectral reduction factor and current spectral floor coefficient are calculated according to the energy, and then the first complex time-frequency matrix is subjected to spectral subtraction denoising processing according to the power spectral density, the current spectral reduction factor and the current spectral floor coefficient, to obtain a denoised first complex time-frequency matrix;

[0009] The denoised first complex time-frequency matrix is subjected to inverse time-frequency transformation to obtain a denoised partial discharge signal.

[0010] As a preferred solution, the signal segment with the minimum residual is selected from the initial partial discharge signal as a background noise signal, and the power spectral density corresponding to the background noise signal is calculated, comprising:

[0011] According to a preset sliding window, a plurality of signal segments with a preset length are extracted from the initial partial discharge signal as candidate segments, and time-frequency transformation is performed on each candidate segment to obtain a second complex time-frequency matrix corresponding to each candidate segment;

[0012] The power spectral residual of each second complex time-frequency matrix is calculated, the candidate segment with the minimum power spectral residual is selected as the background noise signal, and the power spectral density of the second complex time-frequency matrix corresponding to the background noise signal is calculated.

[0013] As a preferred solution, the power spectral density, the current spectral subtraction factor and the current spectral base coefficient are used to perform spectral subtraction denoising processing on each frame amplitude spectrum in the first complex time-frequency matrix to obtain the denoised amplitude spectrum corresponding to the first complex time-frequency matrix.

[0014] According to the power spectral density, the current spectral subtraction factor and the current spectral base coefficient, the spectral subtraction denoising processing is performed on each frame amplitude spectrum in the first complex time-frequency matrix to obtain the denoised amplitude spectrum corresponding to the first complex time-frequency matrix.

[0015] According to the denoised amplitude spectrum and the phase in the first complex time-frequency matrix, the denoised first complex time-frequency matrix is generated.

[0016] As a preferred solution, the spectral subtraction denoising processing is performed on each frame amplitude spectrum in the first complex time-frequency matrix according to the following formula:

[0017]

[0018]

[0019] Wherein, |Y(n,ω)| is the denoised amplitude spectrum; X(n,ω) is the first complex time-frequency matrix; is the power spectral density of the second complex time-frequency matrix; α is the current spectral subtraction factor; β is the current spectral base coefficient; α0 is the initial spectral subtraction factor; β0 is the initial spectral base coefficient; E n is the energy of the nth frame amplitude spectrum; is the average energy of all frame amplitude spectrums; γ and λ are empirical adjustment parameters.

[0020] On the basis of the above embodiment, another embodiment of the present application provides a noise suppression device for cable partial discharge signal, comprising: a time-frequency transformation module, a background noise signal selection module, a spectral subtraction denoising module and a time-frequency inverse transformation module.

[0021] The time-frequency conversion module is configured to acquire an initial partial discharge signal to be denoised, and perform time-frequency conversion on the initial partial discharge signal to obtain a corresponding first complex time-frequency matrix.

[0022] The background noise signal selection module is configured to select a signal segment with a minimum residual from the initial partial discharge signal as a background noise signal, and calculate a power spectral density corresponding to the background noise signal.

[0023] The spectral subtraction noise module is configured to calculate an energy of an amplitude spectrum of each frame in the first complex time-frequency matrix, calculate a current spectral subtraction factor and a current spectral floor coefficient according to the energy, and then perform spectral subtraction denoising processing on the first complex time-frequency matrix according to the power spectral density, the current spectral subtraction factor and the current spectral floor coefficient to obtain a first complex time-frequency matrix after denoising.

[0024] The time-frequency inverse conversion module is configured to perform time-frequency inverse conversion on the first complex time-frequency matrix after denoising to obtain a partial discharge signal after denoising.

[0025] As a preferred solution, the background noise signal is selected from the initial partial discharge signal, and the power spectral density corresponding to the background noise signal is calculated, including:

[0026] According to a preset sliding window, a plurality of signal segments with a preset length are extracted from the initial partial discharge signal as candidate segments, and time-frequency conversion is performed on each candidate segment to obtain a second complex time-frequency matrix corresponding to each candidate segment.

[0027] The power spectral residual of each second complex time-frequency matrix is calculated, a candidate segment with a minimum power spectral residual is selected as a background noise signal, and the power spectral density of the second complex time-frequency matrix corresponding to the background noise signal is calculated.

[0028] As a preferred solution, the first complex time-frequency matrix is subjected to spectral subtraction denoising processing according to the power spectral density, the current spectral subtraction factor and the current spectral floor coefficient to obtain the first complex time-frequency matrix after denoising, including:

[0029] Each frame of amplitude spectrum in the first complex time-frequency matrix is subjected to spectral subtraction denoising processing according to the power spectral density, the current spectral subtraction factor and the current spectral floor coefficient to obtain a denoised amplitude spectrum corresponding to the first complex time-frequency matrix.

[0030] The first complex time-frequency matrix after denoising is generated according to the denoised amplitude spectrum and a phase in the first complex time-frequency matrix.

[0031] As a preferred solution, each frame of amplitude spectrum in the first complex time-frequency matrix is subjected to spectral subtraction denoising processing according to the following formula:

[0032]

[0033] wherein |Y(n, ω)| is a denoising amplitude spectrum; X(n, ω) is a first complex time-frequency matrix; is a power spectral density of a second complex time-frequency matrix; α is a current spectral subtraction factor; β is a current spectral floor coefficient; α0 is an initial spectral subtraction factor; β0 is an initial spectral floor coefficient; E n is an energy of an n-th frame amplitude spectrum; is an average energy of all frame amplitude spectra; γ and λ are empirical adjustment parameters.

[0034] On the basis of the above-mentioned embodiments, a further embodiment of the application provides an electronic device, the device comprising a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, the processor implementing the cable partial discharge signal noise suppression method described in the above-mentioned embodiments of the application when executing the computer program.

[0035] On the basis of the above-mentioned embodiments, a further embodiment of the application provides a storage medium, the storage medium comprising a stored computer program, wherein the device in which the storage medium is located executes the cable partial discharge signal noise suppression method described in the above-mentioned embodiments of the application when the computer program runs.

[0036] Compared with the prior art, the embodiments of the application have the following beneficial effects:

[0037] The application provides a cable partial discharge signal noise suppression method, an initial partial discharge signal to be denoised is acquired, and time-frequency transformation is performed on the initial partial discharge signal to obtain a corresponding first complex time-frequency matrix; a signal segment with the minimum residual from the initial partial discharge signal is selected as a background noise signal, and a power spectral density corresponding to the background noise signal is calculated; the energy of each frame amplitude spectrum in the first complex time-frequency matrix is calculated, the current spectral subtraction factor and the current spectral floor coefficient corresponding to the energy are calculated, then the first complex time-frequency matrix is subjected to spectral subtraction denoising processing according to the power spectral density, the current spectral subtraction factor and the current spectral floor coefficient, to obtain a first complex time-frequency matrix after denoising; time-frequency inverse transformation is performed on the first complex time-frequency matrix after denoising to obtain a partial discharge signal after denoising. In the partial discharge signal denoising process of the application, parameters do not need to be manually set, the system can directly calculate the real-time current spectral subtraction factor and the current spectral floor coefficient according to the energy of each frame amplitude spectrum in the first complex time-frequency matrix, and automatically performs spectral subtraction denoising processing on the initial partial discharge signal to be denoised according to these parameters, thereby avoiding the process of manually setting parameters, and good robustness, adaptability and engineering practicability are achieved. BRIEF DESCRIPTION OF DRAWINGS

[0038] Figure 1 is a flow diagram of a method for suppressing noise of a cable partial discharge signal according to an embodiment of the present application;

[0039] Figure 2 is a structural diagram of a device for suppressing noise of a cable partial discharge signal according to an embodiment of the present application. DETAILED DESCRIPTION

[0040] In order to make the objects, technical solutions and advantages of the present application clearer, the technical solutions in the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only some of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative work fall within the scope of protection of the present application.

[0041] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which the present application belongs; the terms used herein are only for the purpose of describing specific embodiments and are not intended to limit the present application; the terms "include" and "have" and any variations thereof in the specification and claims of the present application and the above description of drawings are intended to cover not exclusive inclusion.

[0042] In the description of the embodiments of the present application, the technical terms "first", "second", etc. are only used to distinguish different objects, and cannot be understood as indicating or implying relative importance or implicitly indicating the number, specific order or primary and secondary relationship of the indicated technical features. In the description of the embodiments of the present application, the meaning of "a plurality of" is two or more, unless otherwise explicitly and specifically limited.

[0043] Reference herein to "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment can be included in at least one embodiment of the present application. The appearance of the phrase in various places in the specification does not necessarily all refer to the same embodiment, nor is it necessarily independent or alternative embodiments to each other. Those skilled in the art explicitly and implicitly understand that the embodiments described herein can be combined with other embodiments.

[0044] In the description of the embodiments of the present application, the term "and / or" is only a description of the association relationship of the associated objects, which means that there can be three relationships, for example, A and / or B, which can represent the three cases of A alone, A and B together, and B alone. In addition, the character " / " in the present application generally represents an "or" relationship between the associated objects.

[0045] In the description of the embodiments of the present application, the terms "a plurality of", "several" refer to two or more (including two), and similarly, "a plurality of groups" refers to two or more groups (including two groups), and "a plurality of pieces" refers to two or more pieces (including two pieces).

[0046] In the description of the embodiments of the present application, unless otherwise explicitly specified and limited, the technical terms "mounting", "connection", "connection", "fixing" and other terms should be broadly understood, for example, it can be fixedly connected, or it can be detachably connected, or it can be integrated; it can be mechanically connected, or it can be electrically connected; it can be directly connected, or it can be indirectly connected through an intermediate medium; it can be the internal communication of two elements or the interaction relationship between two elements. For those skilled in the art, the specific meaning of the above terms in the embodiments of the present application can be understood according to the specific circumstances.

[0047] Embodiment one

[0048] Please refer to Figure 1 To solve the problem of modal decomposition parameter dependence on manual setting, low calculation efficiency and strong subjectivity in the prior art, an embodiment of the present application provides a flowchart of a cable partial discharge signal noise suppression method, which includes the following specific steps:

[0049] S1, obtaining an initial partial discharge signal to be denoised, and performing time-frequency transformation on the initial partial discharge signal to obtain a corresponding first complex time-frequency matrix;

[0050] Specifically, first, the initial partial discharge signal to be denoised is obtained, and STFT is used to perform time-frequency transformation on the initial partial discharge signal, and the time-frequency domain is converted to obtain more abundant information for denoising processing, and the specific implementation is as follows:

[0051] In one specific embodiment, in the simulation experiment of the cable partial discharge signal denoising method, a partial discharge signal is taken as an example, the length of the signal is 1000, the sampling frequency is 50MHz, and the time interval of the sampling points is 20ns. The signal is a double exponential decay oscillation function commonly used in partial discharge denoising method, and the calculation formula is:

[0052] PD(t)=A(e -1.3t / τ -e -2.2t / τ )sin(2πft);

[0053] In the formula, A represents the maximum amplitude of the partial discharge signal, A=8V; τ is the decay coefficient of the partial discharge signal, τ=2.5μs; f is the oscillation frequency, f=2MHz; t is the time variable.

[0054] To test the robustness of the proposed algorithm in a strong noise environment, a Gaussian white noise with a mean of 0 is superimposed on the simulated partial discharge signal, and the signal-to-noise ratio is set to -10 dB to generate a noisy signal as the initial partial discharge signal collected in the cable partial discharge detection. Then, the initial partial discharge signal is converted from the time domain to the time-frequency domain by STFT to obtain a first complex time-frequency matrix, and the calculation formula is as follows:

[0055]

[0056] In the formula: X(n, ω) represents the first complex time-frequency matrix, x(m) represents the mth value in the collected initial partial discharge signal; N is the data length of the initial partial discharge signal, and N=1000; W(·) represents a Hamming window function with a length of 128, and the window interval is set to 64 (i.e. 50% overlap), and the first complex time-frequency matrix X(n, ω) is obtained by sliding window processing.

[0057] S2, selecting a signal segment with the smallest residual from the initial partial discharge signal as a background noise signal, and calculating the power spectral density corresponding to the background noise signal;

[0058] Preferably, the step of selecting a signal segment with the smallest residual from the initial partial discharge signal as a background noise signal, and calculating the power spectral density corresponding to the background noise signal, comprises: extracting a plurality of signal segments with a preset length from the initial partial discharge signal as candidate segments according to a preset sliding window, and performing time-frequency transformation on each candidate segment to obtain a second complex time-frequency matrix corresponding to each candidate segment; calculating the power spectral residual of each second complex time-frequency matrix, selecting the candidate segment with the smallest power spectral residual as the background noise signal, and calculating the power spectral density of the second complex time-frequency matrix corresponding to the background noise signal.

[0059] Specifically, after time-frequency transformation of the initial partial discharge signal into a first complex time-frequency matrix, a plurality of candidate segment static noise signals are extracted from the initial partial discharge signal using a sliding window, STFT is performed on each candidate segment to obtain a second complex time-frequency matrix corresponding to each candidate segment, and the corresponding power spectral residual index is calculated, so as to select the candidate segment with the smallest residual as the background noise signal, and the specific implementation is as follows:

[0060] In adaptive background noise extraction and power spectrum estimation, a plurality of candidate segments with a length of 200 are extracted using a sliding window The length of the sliding window is 100. The candidate segments are converted into the time-frequency domain by STFT, and the corresponding power spectral residual index R is calculated i , and the calculation formula is as follows:

[0061]

[0062] wherein N n is the total number of candidate segments extracted by the sliding window.

[0063] The most suitable background noise signal is selected according to the minimum residual criterion as:

[0064]

[0065] wherein the power spectral density calculation formula is:

[0066] P(n, ω) = |X(n, ω)| 2 ;

[0067] In the formula, P(n, ω) is the power spectral density of the time-frequency matrix X at the nth frame and the ω frequency.

[0068] The second complex time-frequency matrix corresponding to the background noise signal is:

[0069]

[0070] In the formula, X noise represents the second complex time-frequency matrix, x noise (m) represents the mth value in the static noise signal; M = 200 is the data length of the static noise signal; ω(·) represents a Hamming window function with a length of 128;

[0071] The power spectral density calculation formula of the second complex time-frequency matrix is:

[0072]

[0073] In the formula, K represents the number of frames in which the static noise signal is divided, and K = 6 is taken. represents the noise complex spectrum of the kth frame.

[0074] S3, calculate the energy of the amplitude spectrum of each frame in the first complex time-frequency matrix, calculate the corresponding current spectral reduction factor and current spectral floor coefficient according to the energy, and then perform spectral subtraction denoising processing on the first complex time-frequency matrix according to the power spectral density, the current spectral reduction factor and the current spectral floor coefficient, to obtain the denoised first complex time-frequency matrix.

[0075] Preferably, the spectral subtraction denoising processing of the first complex time-frequency matrix according to the power spectral density, the current spectral subtraction factor and the current spectral floor factor to obtain the denoised first complex time-frequency matrix comprises: performing spectral subtraction denoising processing on each frame amplitude spectrum in the first complex time-frequency matrix according to the power spectral density, the current spectral subtraction factor and the current spectral floor factor to obtain the denoised amplitude spectrum corresponding to the first complex time-frequency matrix; and reconstructing the denoised first complex time-frequency matrix according to the denoised amplitude spectrum and the phase in the first complex time-frequency matrix.

[0076] Preferably, the spectral subtraction denoising processing of each frame amplitude spectrum in the first complex time-frequency matrix is performed according to the following formula:

[0077]

[0078] wherein |Y(n,ω)| is the denoised amplitude spectrum; X(n,ω) is the first complex time-frequency matrix; is the power spectral density of the second complex time-frequency matrix; a is the current spectral subtraction factor; β is the current spectral floor factor; a0 is the initial spectral subtraction factor; β0 is the initial spectral floor factor; E n is the energy of the nth frame amplitude spectrum; is the average energy of all frame amplitude spectrums; γ and λ are empirical adjustment parameters.

[0079] Specifically, after the power spectral density of the second complex time-frequency matrix is calculated, the first complex time-frequency matrix is processed by using the spectral subtraction method to obtain the corresponding denoised amplitude spectrum, and the denoised first complex time-frequency matrix is reconstructed by combining the phase information of the first complex time-frequency matrix, and the specific implementation is as follows:

[0080] The spectral subtraction processing of each frame amplitude spectrum in the first complex time-frequency matrix is performed, and the denoised amplitude spectrum is calculated as:

[0081]

[0082] In the formula, a is the spectral subtraction factor; β is the spectral floor factor, both of which are adaptively adjusted, and the calculation formula is:

[0083]

[0084] In the formula, a0 is the initial spectral subtraction factor, a0=2; β0 is the initial spectral floor factor, β0=0.1; E n is the energy of the nth frame, is the average energy of all frames; γ and λ are empirical adjustment parameters, γ=0.8 and λ=0.4.

[0085] The denoised amplitude spectrum is used to reconstruct the complex spectrum with the phase ∠X(n,ω) in the first complex time-frequency matrix:

[0086] Y(n,ω)=|Y(n,ω)|·e j·∠X(n,ω) ;

[0087] In the formula, Y(n,ω) is the first complex time-frequency matrix after denoising.

[0088] S4, performing time-frequency inverse transformation on the first complex time-frequency matrix after denoising to obtain a local discharge signal after denoising.

[0089] Specifically, after reconstructing the first complex time-frequency matrix after denoising, the short-time inverse Fourier transform ISTFT is used to convert the first complex time-frequency matrix after denoising back to the time domain to obtain the local discharge signal after denoising, and the specific implementation is as follows:

[0090]

[0091] The local discharge signal after denoising is highly coincident with the original signal without noise, the noise reduction effect is excellent, and all parameters are adaptively processed according to the characteristics of the signal itself, suitable for different local discharge signals and noise levels.

[0092] It can be seen that the present application provides a noise suppression method for cable partial discharge signals, which can effectively retain the key characteristics of the partial discharge pulse signal in a high-noise environment while significantly suppressing the background interference. The method first expands the noisy signal in the time-frequency domain based on the short-time Fourier transform (STFT), and extracts the static section composed of the first M sampling points of the signal as the background noise input to construct the noise power spectrum density model. In the spectral subtraction process, the adaptive adjustment mechanism of the spectral subtraction factor and the spectral floor factor is designed in combination with the frame energy information, so that the noise suppression degree of each frame can be dynamically adjusted according to the local signal characteristics, thereby effectively avoiding the problems of excessive suppression or spectral artifacts caused by fixed parameters in the traditional spectral subtraction method. The method has a simple structure, low computational overhead, and does not require complex model fitting or training process, and has good robustness, adaptability and engineering practicability.

[0093] Embodiment two

[0094] Please refer to Figure 2 , a structure schematic view of a noise suppression device for cable partial discharge signals provided by an embodiment of the present application, the device comprising: a time-frequency transformation module, a background noise signal selection module, a spectral subtraction noise reduction module, and a time-frequency inverse transformation module;

[0095] The time-frequency transformation module is used to obtain an initial local discharge signal to be denoised, and perform time-frequency transformation on the initial local discharge signal to obtain a corresponding first complex time-frequency matrix.

[0096] The background noise signal selection module is configured to select a signal segment with minimum residual error from the initial partial discharge signal as a background noise signal, and calculate a power spectral density corresponding to the background noise signal.

[0097] The spectral subtraction noise module is configured to calculate an energy of each frame amplitude spectrum in the first complex time-frequency matrix, calculate a current spectral subtraction factor and a current spectral floor coefficient according to the energy, and then perform spectral subtraction noise processing on the first complex time-frequency matrix according to the power spectral density, the current spectral subtraction factor and the current spectral floor coefficient to obtain a denoised first complex time-frequency matrix.

[0098] The time-frequency inverse transform module is configured to perform time-frequency inverse transform on the denoised first complex time-frequency matrix to obtain a denoised partial discharge signal.

[0099] Preferably, the selection of a signal segment with minimum residual error from the initial partial discharge signal as a background noise signal and the calculation of a power spectral density corresponding to the background noise signal include:

[0100] According to a preset sliding window, a plurality of signal segments with a preset length are extracted from the initial partial discharge signal as candidate segments, and time-frequency transform is performed on each candidate segment to obtain a second complex time-frequency matrix corresponding to each candidate segment.

[0101] The power spectral residual error of each second complex time-frequency matrix is calculated, a candidate segment with minimum power spectral residual error is selected as a background noise signal, and the power spectral density of the second complex time-frequency matrix corresponding to the background noise signal is calculated.

[0102] Preferably, the spectral subtraction noise processing on the first complex time-frequency matrix according to the power spectral density, the current spectral subtraction factor and the current spectral floor coefficient to obtain a denoised first complex time-frequency matrix includes:

[0103] The spectral subtraction noise processing on each frame amplitude spectrum in the first complex time-frequency matrix according to the power spectral density, the current spectral subtraction factor and the current spectral floor coefficient to obtain a denoised amplitude spectrum corresponding to the first complex time-frequency matrix;

[0104] The denoised first complex time-frequency matrix is generated according to the denoised amplitude spectrum and the phase in the first complex time-frequency matrix.

[0105] Preferably, the spectral subtraction noise processing on each frame amplitude spectrum in the first complex time-frequency matrix is performed according to the following formula:

[0106]

[0107] wherein |Y(n,ω)| is the denoised amplitude spectrum; X(n,ω) is the first complex time-frequency matrix. is the power spectral density of the second complex time-frequency matrix; a is the current spectral subtraction factor; β is the current spectral floor coefficient; a0is the initial spectral subtraction factor; β0is the initial spectral floor coefficient; E n is the energy of the n-th frame amplitude spectrum; is the average energy of all frame amplitude spectra; γ and λ are empirical adjustment parameters.

[0108] It should be noted that the apparatus embodiments described above are merely illustrative, wherein the units described as separate components can or can not be physically separate, and the components shown as units can or can not be physical units, i.e., can be located in one place or distributed on multiple network units. Part or all of the modules can be selected to achieve the purpose of the embodiment according to actual needs. In addition, the connection between the modules in the apparatus embodiments provided by the present application indicates that there is a communication connection between them, which can be implemented as one or more communication buses or signal lines. Those skilled in the art can understand and implement it without creative labor.

[0109] Those skilled in the art can clearly understand that, for the convenience and brevity, the specific working process of the apparatus described above can refer to the corresponding process in the foregoing method embodiments, which will not be described here.

[0110] Embodiment Three

[0111] Correspondingly, the embodiment of the present application provides an electronic device, the device includes a processor, a memory and a computer program stored in the memory and configured to be executed by the processor, when the processor executes the computer program, the method for suppressing noise of cable partial discharge signal in the above-mentioned embodiment of the application is realized.

[0112] The electronic device can be a desktop computer, a notebook, a palm computer and a cloud server, etc. The device can include but not limited to a processor, a memory.

[0113] The processor can be a central processing unit (CPU), and can also be other general-purpose processors, digital signal processors (DSP), application specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gates or transistor logic components, discrete hardware components, or the like. The general-purpose processor can be a microprocessor or the processor can also be any conventional processor. The processor is a control center of the device, and connects various parts of the device through various interfaces and lines.

[0114] Embodiment four

[0115] Correspondingly, an embodiment of the present application provides a storage medium including a stored computer program, wherein the computer program controls a device where the storage medium is located to perform the method for suppressing noise of a cable partial discharge signal according to the above-mentioned embodiments of the present application when the computer program is running.

[0116] The memory can be used to store the computer program, and the processor realizes various functions of the device by running or executing the computer program stored in the memory and calling data stored in the memory. The memory can mainly include a program storage area and a data storage area, wherein the program storage area can store an operating system, at least one application required by a function, and the like; and the data storage area can store data created according to the use of the mobile phone, and the like. In addition, the memory can include a high-speed random access memory, and can also include a nonvolatile memory, for example, a hard disk, a memory, a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, at least one disk storage device, a flash memory device, or other volatile solid-state memory device.

[0117] The storage medium is a computer readable storage medium, and the computer program is stored in the computer readable storage medium. When the computer program is executed by a processor, steps of each method embodiment described above can be implemented. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or some intermediate forms, etc. The computer readable medium can include any entity or device capable of carrying the computer program code, recording medium, U disk, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signal, telecommunication signal, and software distribution medium, etc. It should be noted that the content included in the computer readable medium can be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction, for example, in some jurisdictions, according to legislation and patent practice, the computer readable medium does not include electrical carrier signals and telecommunication signals.

[0118] The above is the preferred embodiment of the present application. It should be noted that for those skilled in the art, without departing from the principles of the present application, a number of improvements and refinements can be made, which are also considered within the scope of protection of the present application.

Claims

1. A method for noise suppression of partial discharge signals in cables, characterized in that, include: The initial partial discharge signal to be denoised is obtained, and the initial partial discharge signal is subjected to time-frequency transformation to obtain the corresponding first complex time-frequency matrix; Select the signal segment with the smallest residual from the initial partial discharge signal as the background noise signal, and calculate the power spectral density corresponding to the background noise signal; Calculate the energy of the amplitude spectrum of each frame in the first complex time-frequency matrix, calculate the corresponding current spectral subtraction factor and current spectral base coefficient based on the energy, and then perform spectral subtraction denoising on the first complex time-frequency matrix based on the power spectral density, current spectral subtraction factor and current spectral base coefficient to obtain the denoised first complex time-frequency matrix. Perform an inverse time-frequency transform on the first complex time-frequency matrix after denoising to obtain the denoised partial discharge signal.

2. The noise suppression method for partial discharge signals in cables as described in claim 1, characterized in that, The step of selecting a signal segment with the smallest residual from the initial partial discharge signal as the background noise signal and calculating the power spectral density corresponding to the background noise signal includes: According to a preset sliding window, several signal segments of preset length are extracted from the initial partial discharge signal as candidate segments, and time-frequency transformation is performed on each candidate segment to obtain the second complex time-frequency matrix corresponding to each candidate segment. Calculate the power spectral residual of each of the second complex time-frequency matrices, select the candidate segment with the smallest power spectral residual as the background noise signal, and calculate the power spectral density of the second complex time-frequency matrix corresponding to the background noise signal.

3. The noise suppression method for partial discharge signals in cables as described in claim 2, characterized in that, The step of performing spectral subtraction denoising on the first complex time-frequency matrix based on the power spectral density, the current spectral subtraction factor, and the current spectral base coefficient to obtain the denoised first complex time-frequency matrix includes: Based on the power spectral density, the current spectral subtraction factor, and the current spectral base coefficient, spectral subtraction denoising is performed on the amplitude spectrum of each frame in the first complex time-frequency matrix to obtain the denoised amplitude spectrum corresponding to the first complex time-frequency matrix. The first complex time-frequency matrix is ​​generated by reconstructing the denoised amplitude spectrum and the phase in the first complex time-frequency matrix.

4. The noise suppression method for partial discharge signals in cables as described in claim 3, characterized in that, The amplitude spectrum of each frame in the first complex time-frequency matrix is ​​denoised by spectral subtraction according to the following formula: Where |Y(n,ω)| is the denoised amplitude spectrum; X(n,ω) is the first complex time-frequency matrix; E represents the power spectral density of the second complex time-frequency matrix; α is the current spectral reduction factor; β is the current spectral base coefficient; α0 is the initial spectral reduction factor; β0 is the initial spectral base coefficient; n The energy of the amplitude spectrum of the nth frame; is the average energy of the amplitude spectrum across all frames; γ and λ are empirically adjusted parameters.

5. A noise suppression device for partial discharge signals in cables, characterized in that, include: The module includes a time-frequency transformation module, a background noise signal selection module, a spectrum subtraction and denoising module, and an inverse time-frequency transformation module. The time-frequency transformation module is used to acquire the initial partial discharge signal to be denoised, and to perform time-frequency transformation on the initial partial discharge signal to obtain the corresponding first complex time-frequency matrix; The background noise signal selection module is used to select a signal segment with the smallest residual from the initial partial discharge signal as the background noise signal, and to calculate the power spectral density corresponding to the background noise signal. The spectral subtraction denoising module is used to calculate the energy of the amplitude spectrum of each frame in the first complex time-frequency matrix, calculate the corresponding current spectral subtraction factor and current spectral base coefficient based on the energy, and then perform spectral subtraction denoising on the first complex time-frequency matrix based on the power spectral density, the current spectral subtraction factor and the current spectral base coefficient to obtain the denoised first complex time-frequency matrix. The inverse time-frequency transformation module is used to perform an inverse time-frequency transformation on the denoised first complex time-frequency matrix to obtain the denoised partial discharge signal.

6. The noise suppression device for partial discharge signals of cables as described in claim 5, characterized in that, The step of selecting a signal segment with the smallest residual from the initial partial discharge signal as the background noise signal and calculating the power spectral density corresponding to the background noise signal includes: According to a preset sliding window, several signal segments of preset length are extracted from the initial partial discharge signal as candidate segments, and time-frequency transformation is performed on each candidate segment to obtain the second complex time-frequency matrix corresponding to each candidate segment. Calculate the power spectral residual of each of the second complex time-frequency matrices, select the candidate segment with the smallest power spectral residual as the background noise signal, and calculate the power spectral density of the second complex time-frequency matrix corresponding to the background noise signal.

7. The noise suppression device for partial discharge signals of cables as described in claim 6, characterized in that, The step of performing spectral subtraction denoising on the first complex time-frequency matrix based on the power spectral density, the current spectral subtraction factor, and the current spectral base coefficient to obtain the denoised first complex time-frequency matrix includes: Based on the power spectral density, the current spectral subtraction factor, and the current spectral base coefficient, spectral subtraction denoising is performed on the amplitude spectrum of each frame in the first complex time-frequency matrix to obtain the denoised amplitude spectrum corresponding to the first complex time-frequency matrix. The first complex time-frequency matrix is ​​generated by reconstructing the denoised amplitude spectrum and the phase in the first complex time-frequency matrix.

8. The noise suppression device for partial discharge signals of cables as described in claim 7, characterized in that, The amplitude spectrum of each frame in the first complex time-frequency matrix is ​​denoised by spectral subtraction according to the following formula: Where |Y(n,ω)| is the denoised amplitude spectrum; X(n,ω) is the first complex time-frequency matrix; E represents the power spectral density of the second complex time-frequency matrix; α is the current spectral reduction factor; β is the current spectral base coefficient; α0 is the initial spectral reduction factor; β0 is the initial spectral base coefficient; n The energy of the amplitude spectrum of the nth frame; is the average energy of the amplitude spectrum across all frames; γ and λ are empirically adjusted parameters.

9. An electronic device, characterized in that, The device includes a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein the processor, when executing the computer program, implements a noise suppression method for cable partial discharge signals as described in any one of claims 1 to 4.

10. A storage medium, characterized in that, The storage medium includes a stored computer program, wherein, when the computer program is executed, it controls the device where the storage medium is located to perform the noise suppression method for cable partial discharge signals as described in any one of claims 1 to 4.

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