Narrowband interference suppression method and system for partial discharge detection

Abstract

The invention discloses a partial discharge detection narrow-band interference suppression method and system, and relates to the technical field of partial discharge detection, and the method comprises the steps: carrying out the first judgment of interference, and carrying out the first iteration of the interference; separating the signal, and judging the separated signal; and correcting the partial discharge signal and outputting the partial discharge signal. According to the method, the number of narrowband interferences is determined through the information entropy theory, the problem of signal distortion or missing detection caused by improper threshold selection is avoided, and the adaptability is high; the narrowband interference frequency is estimated through the discrete Fourier transform power spectrum, so that accurate estimation can be realized, and the separation effect is improved; by simplifying a Simpson-Newton iterative formula, the algorithm operation time is shortened, and the real-time performance is improved; by improving a fast independent component analysis algorithm, the number of iterations is reduced, the calculation amount is reduced, and the algorithm efficiency is improved; the amplitude and phase of the separation signal are corrected by using the calibration signal, so that signal distortion is effectively avoided, and the original characteristics of the signal are reserved.

Description

Technical Field

[0001] The present invention relates to the technical field of partial discharge detection, and in particular to a method and system for suppressing narrowband interference in partial discharge detection. Background Art

[0002] Partial discharge detection is an effective means to evaluate the insulation status of power equipment. However, in field tests, electromagnetic interference is serious, such as random pulse interference, white noise and periodic narrowband interference, which results in the actual partial discharge signal being usually relatively weak. Among them, narrowband interference refers to interference signals with a relatively narrow frequency occupation but relatively large interference power. Since periodic narrowband interference has the characteristics of long duration, large amplitude and high probability of occurrence, its impact on partial discharge detection is particularly serious. Therefore, effectively suppressing periodic narrowband interference signals is an important part of partial discharge detection.

[0003] At present, the main methods for suppressing narrowband interference are the filter method and the threshold method. The filter method is less used due to problems such as large energy loss and phase deviation, while the threshold method has a large signal distortion caused by the difficulty in selecting the threshold, which limits the application of the threshold method after denoising. For example, in partial discharge positioning, it is required to effectively identify the maximum point of the partial discharge signal and in partial discharge pattern recognition, it is required to accurately calculate the waveform parameters of the partial discharge signal. In order to solve the problems of the threshold method, some studies have proposed to use blind signal separation technology to suppress interference of partial discharge signals, and have achieved certain results. However, due to the uncertainty of the blind separation technology itself, the separated signal has uncertainty in both amplitude and phase. At the same time, subsequent studies have found that the above algorithm has a large amount of calculation, which makes the denoising process time-consuming and poor in real time.

[0004] The present invention is proposed to solve the waveform distortion and uncertainty problems existing in the traditional threshold method and blind separation method in the process of suppressing partial discharge periodic narrowband interference. Summary of the invention

[0005] In view of the above-mentioned problems, the present invention is proposed.

[0006] Therefore, the technical problem solved by the present invention is: the existing partial discharge detection technology has the problems of difficulty in determining the number of narrowband interferences, insufficient estimation accuracy of narrowband interference frequency, signal distortion, and how to solve the problem of high algorithm complexity.

[0007] To solve the above technical problems, the present invention provides the following technical solutions: a method for suppressing narrowband interference in partial discharge detection, comprising a first judgment on the interference, a first iteration on the interference; separating the signal, judging the separated signal; correcting the partial discharge signal, and outputting it.

[0008] As a preferred solution of the method for suppressing narrowband interference in partial discharge detection of the present invention, the first judgment on the interference includes analyzing the power spectrum and using a first judgment method to judge the amount of narrowband interference.

[0009] As a preferred solution of the method for suppressing narrowband interference in partial discharge detection of the present invention, the first iteration of the interference includes accurately estimating the narrowband interference frequency by adopting a first iteration formula based on the power spectrum.

[0010] As a preferred solution of the method for suppressing narrowband interference in partial discharge detection according to the present invention, the signal separation includes separating the calibration signal.

[0011] As a preferred solution of the method for suppressing narrowband interference in partial discharge detection of the present invention, the method comprises: determining whether the separated signal is a partial discharge signal according to the separated signal

[0012] As a preferred solution of the method for suppressing narrowband interference in partial discharge detection according to the present invention, the correction of the partial discharge signal includes correcting the partial discharge signal by using the amplitude and phase of the calibration signal.

[0013] As a preferred solution of the method for suppressing narrowband interference in partial discharge detection of the present invention, the output includes outputting the corrected partial discharge signal.

[0014] Another object of the present invention is to provide a local discharge detection narrowband interference suppression system, which can determine the amount of narrowband interference through information entropy theory, accurately estimate the narrowband interference frequency using discrete Fourier transform power spectrum, improve the fast independent component analysis algorithm to reduce calculation complexity, and use calibration signals to correct the amplitude and phase of the separated signal, thereby solving the technical problem that the current local discharge detection technology contains difficulties in suppressing narrowband interference.

[0015] As a preferred solution of the local discharge detection narrowband interference suppression system described in the present invention, it includes: a local discharge signal acquisition module, a signal processing module, and a signal output module; the local discharge signal acquisition module is used to make a first judgment on the interference and perform a first iteration on the interference; the signal processing module is used to separate the signal and judge the separated signal; the signal output module is used to correct the local discharge signal and output it.

[0016] A computer device comprises a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of a method for suppressing narrowband interference in partial discharge detection.

[0017] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of a method for suppressing narrowband interference in partial discharge detection.

[0018] Beneficial effects of the present invention: The narrowband interference suppression method for partial discharge detection provided by the present invention can automatically determine the number of narrowband interferences according to the amount of signal information without setting a threshold in advance or performing complex statistical analysis, thereby avoiding signal distortion or missed detection problems caused by improper threshold selection, and has strong adaptability; the narrowband interference frequency is estimated by discrete Fourier transform power spectrum, and even if the narrowband interference frequency is not an integer multiple of the frequency resolution, accurate estimation can be achieved to improve the separation effect; by simplifying the Simpson-Newton iteration formula, the algorithm running time is reduced and the real-time performance is improved; by improving the fast independent component analysis algorithm, the number of iterations is reduced, the amount of calculation is reduced, and the algorithm efficiency is improved; by using the calibration signal to correct the amplitude and phase of the separated signal, signal distortion is effectively avoided and the original characteristics of the signal are retained; by comparing with the frequency slicing wavelet denoising method and Fourier transform filtering, the denoising effect of the present invention is more obvious, and the influence of edge effects can be effectively avoided, thereby improving the detection accuracy. The present invention achieves better effects in terms of adaptability, accuracy of estimating the narrowband interference frequency, and algorithm efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0020] Figure 1 This is an overall flow chart of a method for suppressing narrowband interference in partial discharge detection provided by the first embodiment of the present invention.

[0021] Figure 2 A diagram of specific implementation steps of a method for suppressing narrowband interference in partial discharge detection provided by the first embodiment of the present invention.

[0022] Figure 3 This is an overall flow chart of a partial discharge detection narrowband interference suppression system provided by the third embodiment of the present invention. DETAILED DESCRIPTION

[0023] In order to make the above-mentioned purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in the art without creative work should fall within the scope of protection of the present invention.

[0024] Example 1, reference Figure 1-Figure 2 , as an embodiment of the present invention, provides a method for suppressing narrowband interference in partial discharge detection, comprising:

[0025] S1: Perform a first judgment on interference, and perform a first iteration on the interference.

[0026] Furthermore, performing a first judgment on the interference includes analyzing the power spectrum and using a first judgment method to judge the amount of narrowband interference.

[0027] The power spectrum analysis includes performing discrete Fourier transform on the original observed partial discharge noisy signal to obtain a discrete Fourier transform power spectrum.

[0028] It should be noted that the first judgment method may be a variety of methods, such as introducing information entropy, peak detection method, adaptive threshold method or wavelet transform method to judge the amount of narrowband interference.

[0029] In the embodiment of the present application, the first judgment method is to judge the amount of narrowband interference by introducing information entropy, and the principle steps are as follows:

[0030] The partial discharge mixed signal is divided into the discrete Fourier transform power spectrum at each extreme point f peak_i The power ratio is defined as p i , and arranged in descending order of numerical value, the information entropy of the power spectrum is expressed as:

[0031]

[0032] Where n represents the number of extreme points in the discrete Fourier transform power spectrum of the partial discharge mixed signal, peak_i represents f peak_i The corresponding index subscript.

[0033] When the xth power extreme value is reached, the information entropy G(Y) is closest to the bounded value. At this time, the power intensity is very small and the amount of narrowband interference information that can be reflected is very small. Therefore, the amount of narrowband interference can be determined as x-1.

[0034] In an alternative embodiment, the method used for the first determination is to judge the number of narrowband interferences through the wavelet transform method. The principle is as follows: Select a suitable wavelet basis function according to the characteristics of the signal. For a signal with sharp features, a wavelet basis function with a sharp shape can be selected, such as the dbN wavelet basis function; for a signal with smooth features, a wavelet basis function with a smooth shape can be selected, such as the symN wavelet basis function, etc. Determine the decomposition level of the wavelet transform according to the frequency range and sampling frequency of the signal, perform wavelet decomposition on the original signal to obtain the decomposition coefficients of different frequency channels. Secondly, perform a sum-of-squares operation on the decomposition coefficients of each frequency channel to obtain the energy of each channel, analyze the energy distribution of each channel, judge whether there is a channel with energy significantly higher than other channels, set an energy threshold according to the actual situation, for example, regard the channel with energy exceeding the average energy as a potential narrowband interference channel, and count the number of channels with energy exceeding the threshold, which is the judged number of narrowband interferences.

[0035] It should also be noted that performing the first iteration on the interference includes accurately estimating the narrowband interference frequency on the basis of the power spectrum by using the first iteration formula.

[0036] It should be noted that the iteration method used for the first iteration can be various methods, such as using a simplified Simpson-Newton iteration formula, Newton's iteration method, or binary search method, etc., to accurately estimate the narrowband interference frequency.

[0037] In the embodiment of the present application, the first iteration uses a simplified Simpson-Newton iteration formula to accurately estimate the narrowband interference frequency. The principle steps are as follows:

[0038] (1) Convert the discrete points of the discrete Fourier transform power spectrum into frequencies, and use the frequency of the narrowband interference power extreme point as the initial estimated frequency f 0 .

[0039] (2) Let P 0 = P(f 0 ), f = f 0 + Δf / 2, and calculate P(f).

[0040] (3) If P 0 < P(f), then let P 0 = P(f), f 0 = f, Δf = Δf / 2. If Δf < ε, then end; otherwise, go to step (2); if P 0 ≥ P(f), then go to step (4).

[0041] (4) Let P 0 = P(f 0 ), f = f 0 - Δf / 2, and calculate P 0(f).

[0042] (5) If P 0 < P(f), then let P 0 = P(f), f 0 = f, Δf = Δf / 2. If Δf < ε, then end. Otherwise, go to step (2); if P 0 ≥ P(f), then let Δf = Δf / 2. If Δf < ε, then end. Otherwise, go to step (2).

[0043] In this step, Δf represents the frequency resolution in the discrete Fourier transform power spectrum, and ε represents the error precision. Steps (2) to (5) represent the fine estimation of the true frequency f0′. The main purpose is to reduce the estimation error when f0′ / Δf is not an integer and make the error satisfy the error range, which is expressed as:

[0044]

[0045] In an alternative embodiment, the first iteration formula uses the binary search method to accurately estimate the narrowband interference frequency. The principle steps are as follows: According to the position of the distorted peak in the power spectrum, determine the initial frequency range and calculate the midpoint frequency f mid .

[0046] Judge the relationship between P mid and the power value P peak of the distorted peak in the power spectrum.

[0047] If P mid > P peak , it means that the f mid frequency point is on the right side of the distorted peak. Narrow the frequency range to [f mid , f end .

[0048] If P mid < P peak , it means that the f mid frequency point is on the left side of the distorted peak. Narrow the frequency range to [f start , f mid .

[0049] If P mid = P peak , it means that the distorted peak frequency is found and the iteration ends.

[0050] According to the iteration result, update the frequency range and repeat the iteration until the frequency range is small enough to meet the predicted accuracy requirement. Output the midpoint of the final frequency range as the estimated value of the narrowband interference frequency.

[0051] S2: Separate the signal and judge the separated signal.

[0052] Furthermore, separating the signal includes separating the calibration signal.

[0053] Separating the calibration signal includes adding the calibration signal at the end of the partial discharge mixed signal and separating the signal using an improved fast independent component analysis algorithm.

[0054] The basic steps to improve the fast independent component algorithm are:

[0055] Calculate the mean μ of the observation vector and normalize the multidimensional observation data X, that is, x′=x-μ.

[0056] Calculate the covariance matrix of the centered data C = E[x′x′ T ], perform eigenvalue decomposition on the covariance matrix, expressed as:

[0057] C=EDE T

[0058] Where D represents the eigenvalue diagonal matrix and E represents the corresponding eigenvector matrix.

[0059] The data is transformed using eigenvalues ​​and eigenvectors to obtain whitened data.

[0060] Select a random unit vector w as the initial value of the weight vector and apply the update rule until convergence. The update rule is expressed as:

[0061]

[0062] Among them, g(·) represents the nonlinear function used to capture non-Gaussianity, and g′(·) represents the derivative of the nonlinear function.

[0063] After the update, + Normalize.

[0064] If multiple independent components are extracted, the new weight vector needs to be orthogonalized to ensure that the multiple independent components are independent of each other.

[0065] The final weight vector w is used to project the whitened data onto the independent components, expressed as:

[0066] s=w T x”

[0067] Repeatedly select a random unit vector as the initial value of the weight vector, apply the update rule until convergence, and use the final weight vector to project the whitened data onto the independent component to extract more independent components. Each time the new weight vector is extracted, it is ensured to be orthogonal or independent to the previously extracted independent component.

[0068] It should be noted that the fast independent component algorithm uses the standard Newton iteration method, and its convergence speed is usually second order. In order to improve the convergence speed, the Simpson-Newton iteration formula is introduced, so that only the function value and the derivative value need to be calculated once and twice for solution, which can effectively reduce the amount of calculation in the iterative process.

[0069] It should also be noted that determining the separation signal includes determining whether it is a partial discharge signal based on the separation signal.

[0070] Judging whether the separated signal is a partial discharge signal includes judging whether the separated signal is a partial discharge signal according to whether the separated signal has a calibration signal.

[0071] S3: Correct the partial discharge signal and output it.

[0072] Furthermore, correcting the partial discharge signal includes correcting the partial discharge signal using the amplitude and phase of the calibration signal.

[0073] It should be noted that the output includes outputting the corrected partial discharge signal.

[0074] It is also explained that when using the fast independent component analysis algorithm to separate the mixed partial discharge signal, it is generally impossible to fully identify the source signal because the source signal is completely unknown. That is, the separated signal is only an approximation of the source signal, and there are uncertainties in the amplitude, phase (PD pulse polarity) and arrangement order. Therefore, a calibration signal is introduced to eliminate the uncertainty of the separated signal, that is, a calibration signal with a certain amplitude and phase is added to the end of the noisy partial discharge signal, and a zero value of the same length is added to the end of the reference narrowband interference signal to achieve calibration and judgment of the amplitude and phase.

[0075] It should also be noted that the calibrated original observation signal and the reference signal constitute a new multi-channel observation signal, and the observation signal is separated using the improved fast independent component analysis algorithm. The separated signal is judged to be a partial discharge signal based on whether there is a calibration signal. The PD signal is calibrated according to the amplitude and phase of the calibration signal to obtain a calibrated denoised signal.

[0076] Embodiment 2 is an embodiment of the present invention, which provides a method for suppressing narrowband interference in partial discharge detection. In order to verify the beneficial effects of the present invention, scientific demonstration is carried out through economic benefit calculation and simulation experiments.

[0077] Firstly, the denoising effect and real-time performance of a narrowband interference suppression method for partial discharge detection are verified and compared with the traditional method. The required experimental equipment includes partial discharge detector, signal generator, data acquisition card and computer.

[0078] Secondly, during the verification process, a signal generator is used to generate a simulated partial discharge signal, and a periodic narrowband interference signal is superimposed to simulate the characteristics of an actual partial discharge signal. A data acquisition card is used to collect the simulated signal and store it in a computing center. The collected signal is denoised using traditional methods and the method of the present invention.

[0079] The traditional method uses frequency slicing wavelet denoising method and Fourier transform filtering to denoise the signal; the method of the present invention obtains the power spectrum of the original observed partial discharge noisy signal by discrete Fourier transform, determines the amount of narrowband interference by introducing information entropy, accurately estimates the narrowband interference frequency by using discrete Fourier transform spectrum, uses an improved fast independent component analysis algorithm to reduce the number of algorithm iterations, uses a calibration signal to calibrate the separated signal, and corrects the amplitude and phase of the separated partial discharge signal.

[0080] Table 1 Comparison data between traditional method and the experiment of the present invention

[0081]

[0082] By comparing the table data, it can be seen that the method of the present invention is superior to the traditional method in terms of denoising effect and real-time performance; in terms of denoising time, the denoising time of the method of the present invention is significantly less than that of the traditional method, and when the number of narrow-band interferences is large, the time advantage is more obvious, indicating that the method of the present invention has higher real-time performance and is more suitable for application in partial discharge online detection; in terms of signal distortion rate, the distortion rate of the signal after denoising by the method of the present invention is significantly lower than that of the traditional method, indicating that the present invention can better retain the original characteristics of the signal, avoid the destruction of the partial discharge signal waveform and parameters, and is beneficial to subsequent partial discharge positioning and pattern recognition and other analyses.

[0083] Example 3, reference Figure 2 , as an embodiment of the present invention, provides a local discharge detection narrowband interference suppression system, including a local discharge signal acquisition module, a signal processing module, and a signal output module.

[0084] The partial discharge signal acquisition module is used to make a first judgment on the interference and perform a first iteration on the interference; the signal processing module is used to separate the signal and judge the separated signal; the signal output module is used to correct the partial discharge signal and output it.

[0085] If the function is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the methods of each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc. Various media that can store program codes.

[0086] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in conjunction with such instruction execution systems, devices or apparatuses. For the purposes of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in conjunction with such instruction execution systems, devices or apparatuses.

[0087] More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic device), a portable computer disk case (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be a paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, deciphering, or processing in another suitable manner as necessary, and then stored in a computer memory.

[0088] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc. It should be noted that the above embodiments are only used to illustrate the technical solution of the present invention and are not limited. Although the present invention is described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that the technical solution of the present invention can be modified or replaced by equivalents without departing from the spirit and scope of the technical solution of the present invention, which should be included in the scope of the claims of the present invention.

[0089] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A method for suppressing narrowband interference in partial discharge detection, characterized in that: include: Performing a first judgment on interference, and performing a first iteration on the interference; Separate the signals and judge the separated signals; Correct the partial discharge signal and output it.

2. The method for suppressing narrowband interference in partial discharge detection according to claim 1, characterized in that: The first determination of the interference includes determining the amount of narrowband interference by analyzing the power spectrum using a first determination method.

3. The method for suppressing narrowband interference in partial discharge detection according to claim 2, characterized in that: The first iteration of the interference includes accurately estimating the narrowband interference frequency by using a first iteration formula based on the power spectrum.

4. The method for suppressing narrowband interference in partial discharge detection according to claim 3, characterized in that: The separating of the signals includes separating the calibration signal.

5. The method for suppressing narrowband interference in partial discharge detection according to claim 4, characterized in that: The determining of the separation signal includes determining whether the separation signal is a partial discharge signal according to the separation signal.

6. The method for suppressing narrowband interference in partial discharge detection according to claim 5, characterized in that: The correcting the partial discharge signal includes correcting the partial discharge signal by using the amplitude and phase of the calibration signal.

7. The method for suppressing narrowband interference in partial discharge detection according to claim 6, characterized in that: The output includes outputting the corrected partial discharge signal.

8. A system using the method for suppressing narrowband interference in partial discharge detection according to any one of claims 1 to 7, characterized in that: It includes a partial discharge signal acquisition module, a signal processing module, and a signal output module; The partial discharge signal acquisition module is used to make a first judgment on the interference and perform a first iteration on the interference; The signal processing module is used to separate the signal and determine the separated signal; The signal output module is used to correct the partial discharge signal and output it.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method for suppressing narrow-band interference in partial discharge detection according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method for suppressing narrow-band interference in partial discharge detection according to any one of claims 1 to 7 are implemented.

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