Power distribution network fault signal noise reduction method and device based on CEEMDAN and S-G, and storage medium

Abstract

The invention discloses a power distribution network fault signal noise reduction method and device based on CEEMDAN and S-G. A fault zero-sequence current signal is decomposed through the CEEMDAN to obtain n fIMF components, correlation coefficients of all the fIMF components are calculated, fIMF components containing fault features are screened out, filtering is carried out on the fIMF components in combination with S-G, IMF1 components after filtering are reconstructed, and a noise reduction result is obtained. The fault feature precision is improved, and favorable support can be provided for fault feature quantity extraction and fault type identification.

Description

Technical Field

[0001] The present invention relates to the technical field of distribution network fault signal processing, and specifically to a distribution network fault signal denoising method, device and storage medium based on CEEMDAN and S-G. Background Art

[0002] In the process of distribution network fault identification, it is necessary to extract fault features. The traditional EMD algorithm has problems such as mode mixing and white noise residue. Although EEMD suppresses the phenomenon of mode mixing, this method requires multiple ensemble averaging coefficients during the decomposition process, resulting in a long calculation time and a large reconstruction error, making it difficult to obtain accurate fault features when extracting subsequent fault feature quantities. Summary of the Invention

[0003] In order to solve the above problems, the present invention provides a distribution network fault signal denoising method, device and storage medium based on CEEMDAN and S-G, which can provide favorable support for the extraction of fault feature quantities and the identification of fault types.

[0004] In order to achieve the above object, the present invention is realized through the following technical solutions:

[0005] The distribution network fault signal denoising method based on CEEMDAN and S-G of the present invention includes the following steps:

[0006] Step 1, collect the three-phase fault currents of the distribution network, and synthesize the three-phase fault currents of the distribution network to obtain an initial fault zero-sequence current signal;

[0007] Step 2, decompose the initial fault zero-sequence current signal by the CEEMDAN method to obtain n f IMF components, and the n f IMF components include components and a residual component Step 3, perform a correlation analysis on the f IMF components decomposed in Step 2 and the initial fault zero-sequence current signal, retain the f IMF components with a correlation coefficient greater than 0.5, and discard the f IMF components without fault information to obtain m f IMF components;

[0008] Step 4, use the S-G method to filter the noisy f IMF components to achieve fault signal denoising, and the denoised f IMF components are expressed as IMF l, where l ∈ [1, 2,..., m];

[0009] Step 5, reconstruct each IMF component to achieve fault signal denoising.

[0010] A further improvement of the present invention lies in that: the calculation expression of the correlation coefficient used in the correlation analysis in step 3 is as follows:

[0011]

[0012] wherein, R is the correlation coefficient, X is the initial fault zero-sequence current signal, and n is the order number of the f IMF component order, represents the i-th order f IMF component, where i ∈ [1, 2,..., n].

[0013] A further improvement of the present invention lies in that: the specific operation of step 4 is as follows:

[0014] Let r and w be the width of the noise reduction window and the length of the sliding array respectively, where

[0015] w = 2r + 1

[0016] The data points x = (-r, -r + 1, 0, 1, r, -1, -r) in the sliding window are fitted by a high-order polynomial, and the high-order polynomial expression is:

[0017]

[0018] wherein, q is the polynomial degree; a g is the coefficient of the g-th data; x g is the g-th data point in the window, where g ∈ [1, 2,..., q - 1];

[0019] Each movement of the window gives w equations:

[0020]

[0021] In the formula, r is the width of the noise reduction window; q is the polynomial degree; e is the least squares fitting residual; f is the f IMF component before noise reduction; Y is the first term in the above formula, A is the second term in the above formula, and F is the third term in the above formula;

[0022] f = YA + F

[0023] The least squares solution of A is:

[0024] A' = (YTY) -1 Y T f

[0025] In the formula, A' is the least squares solution of A, Y T is the transpose of Y, f is the f IMF component before noise reduction, and after noise reduction it is:

[0026] f' = YA' = Y(Y T Y) -1 Y T f.

[0027] The electronic device of the present invention includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps of the method as described above are implemented.

[0028] The computer-readable storage medium of the present invention stores a computer program. When the computer program is executed by a processor, the steps of the method as described above are implemented.

[0029] Based on the CEEMDAN method, the present invention combines the S-G filtering and noise reduction method to perform noise reduction processing on the fault zero-sequence current signal, improves the accuracy of fault feature extraction, and provides favorable support for subsequent extraction of fault feature quantities and identification of fault types. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] Figure 1 is a schematic diagram of the fault signal noise reduction process of the present invention;

[0031] Figure 2 is a schematic diagram of the noisy fault zero-sequence current in an embodiment of the present invention;

[0032] Figure 3 are the IMFs obtained by CEEMDAN decomposition of the noisy fault signal in an embodiment of the present invention;

[0033] Figure 4 is a schematic diagram of the ground fault zero-sequence current before noise addition in an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0034] In order to make the objectives, technical solutions, and advantages of the present invention clearer, the following further elaborates on the present invention in conjunction with the accompanying drawings and embodiments. It should be noted that the embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0035] It should also be understood that the following embodiments are only used to further illustrate the present invention and should not be construed as limiting the protection scope of the present invention. Those skilled in the art's non-essential improvements and adjustments based on the above content of the present invention fall within the protection scope of the present invention. The parameter settings in the specific embodiments below only represent a feasible example, and those skilled in the art can make targeted modifications according to the specific business scenarios.

[0036] As Figure 1 shown, the method for reducing noise of distribution network fault signals based on CEEMDAN and S-G of the present invention includes the following steps:

[0037] Step 1: Collect the three-phase fault currents of the distribution network, and synthesize the three-phase fault currents of the distribution network to obtain an initial fault zero-sequence current signal;

[0038] Step 2: Decompose the initial fault zero-sequence current signal by the CEEMDAN method to obtain n f IMF components, and the n f IMF components include components and a residual component Step 3: Conduct a correlation analysis on the f IMF components obtained by the decomposition in Step 2 and the initial fault zero-sequence current signal, and retain the f IMF components with a correlation coefficient greater than 0.5, and discard the f IMF components without fault information to obtain m f IMF components;

[0039] Step 4: Filter the noisy f IMF components by the S-G method to achieve noise reduction of the fault signal. The noise-reduced f IMF components are denoted as IMF l, where l ∈ [1, 2,..., m];

[0040] Step 5: Reconstruct each IMF component to achieve noise reduction of the fault signal.

[0041] In Step 1, the synthesis of the three-phase fault currents of the distribution network fault is as follows:

[0042]

[0043] where I0 is the initial fault zero-sequence current signal, and I a , I b , I c are the a, b, and c phase currents at the fault point of the distribution network respectively.

[0044] The calculation expression of the correlation coefficient used in the correlation analysis in Step 3 is as follows:

[0045]

[0046] where R is the correlation coefficient, X is the initial fault zero-sequence current signal, n is the number of d IMF components, represents the i-th order d IMF component, where i ∈ [1, 2,..., n].

[0047] The specific operation of Step 4 is:

[0048] Let r and w be the width of the noise reduction window and the length of the sliding array respectively, where

[0049] w = 2r + 1

[0050] The data points x = (-r, -r + 1, 0, 1, r, -1, -r) in the sliding window are fitted by a high - order polynomial, and the expression of the high - order polynomial is:

[0051]

[0052] where q is the degree of the polynomial; a g is the coefficient of the g - th data; x g is the g - th data point in the window, where g ∈ [1, 2,..., q - 1];

[0053] Each movement of the window yields w equations:

[0054]

[0055] In the formula, r is the width of the noise reduction window; q is the degree of the polynomial; e is the least - squares fitting residual; f is the f IMF component before noise reduction; Y is the first term in the above formula, A is the second term, and F is the third term;

[0056] f = YA + F

[0057] The least - squares solution of A is:

[0058] A'=(Y T Y) -1 Y T f

[0059] In the formula, A' is the least - squares solution of A, Y T is the transpose of Y, and f is the f IMF component before noise reduction, and after noise reduction is:

[0060] f' = YA' = Y(Y T Y) -1 Y T f.

[0061] In order to verify the effect of the fault signal noise reduction method based on CEEMDAN and S - G, white noise is added to the fault waveform in the present invention. The fault zero - sequence current after adding noise is as Figure 2 shown, and the fault zero - sequence current before adding noise is as Figure 4 shown.

[0062] It can be seen from Figure 2 that after adding white noise, the smoothness of the signal becomes weaker and a large number of 'glitches' appear. By decomposing the signal with the method proposed in the present invention, the obtained f IMF component is as Figure 3 shown.

[0063] After decomposition by CEEMDAN, 7-order f IMF components and 1 residual component are obtained. In order to accurately extract the effective f IMF components containing fault information, first calculate each f IMF the correlation coefficient between the components and the noisy signal. The solution results are shown in Table 1;

[0064] Table 1 The correlation coefficient between each f IMF component and the noisy signal

[0065]

[0066] The f IMF components with R value greater than 0.5 are effective. According to Table 1, in the noisy signal are effective signals. However, there is noise interference in the effective f IMF components, and noise reduction processing is required. In order to verify the superiority of the method proposed in the present invention, the noise reduction results of the method of the present invention are compared with those of several existing methods. The experimental results are shown in Table 2;

[0067] Table 2 Comparison results of noise reduction effects

[0068] indicators CEEMDAN The literature

[20] In this paper SNR 9.326 10.854 11.430 RMSE 2.347 1.823 1.641

[0069] SNR and RMSE are indicators to measure the signal quality. The larger the SNR value and the smaller the RMSE value, the better the noise reduction effect. It can be seen from Table 2 that the noise reduction effect of the method proposed in the present invention is better than that of the existing methods, which proves that the combination of CEEMDAN and S-G has a good noise reduction effect.

[0070] In order to further verify the noise reduction effect of the method proposed in the present invention, comparative experiments are carried out under four signal-to-noise ratios. The experimental results are shown in Table 3;

[0071] Table 3 Comparison results of noise reduction effects under different signal-to-noise ratios

[0072]

[0073] It can be seen from Table 3 that under different signal-to-noise ratios, the noise reduction effect of the method proposed in the present invention is higher than that of the existing noise reduction methods, further verifying the superiority of the method of the present invention.

[0074] Based on the above data, after denoising the noise signal by the fault signal denoising method based on CEEMDAN and S-G, the noise reduction effect is significantly improved, the effect and performance are stable and reliable, and the noise reduction effect is better than the prior art. Under different signal-to-noise ratios, the method proposed in the present invention performs excellently, ensuring the accurate extraction of subsequent fault features.

[0075] The present invention is described with reference to the flowcharts and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the present invention. It should be understood that each flow and / or block in the flowchart and / or block diagram, and combinations of flows and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to the processors of general-purpose computers, special-purpose computers, embedded processors, or other programmable data processing devices to produce a machine, such that the instructions executed by the processors of the computer or other programmable data processing devices produce means for implementing the functions specified in one or more of the flows Figure 1 one or more of the flows and / or blocks Figure 1 one or more of the blocks for implementing the specified functions.

[0076] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to operate in a specific manner, such that the instructions stored in the computer-readable memory produce a manufactured article including instruction means for implementing the functions specified in one or more of the flows Figure 1 one or more of the flows and / or blocks Figure 1 one or more of the blocks.

[0077] These computer program instructions can also be loaded onto a computer or other programmable data processing device, such that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, and thus the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in one or more of the flows Figure 1 one or more of the flows and / or blocks Figure 1 one or more of the blocks.

[0078] Although the preferred embodiments of the present invention have been described, those skilled in the art can make additional changes and modifications once they learn the basic creative concepts. Therefore, the appended claims are intended to be construed to include the preferred embodiments and all changes and modifications falling within the scope of the present invention.

[0079] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalent technologies, the present invention is also intended to include these changes and modifications.

Claims

1. A noise reduction method for distribution network fault signals based on CEEMDAN and S-G, characterized in that: It includes the following steps: Step 1: Collect the three-phase fault currents of the distribution network, and synthesize the three-phase fault currents of the distribution network to obtain an initial fault zero-sequence current signal; Step 2: Decompose the initial fault zero-sequence current signal by the CEEMDAN method to obtain n f IMF components, where the n f IMF components include the component and the residual component Step 3: Conduct a correlation analysis between the f IMF components obtained in Step 2 and the initial fault zero-sequence current signal, and retain the f IMF components with a correlation coefficient greater than 0.5, and discard the f IMF components without fault information, thereby obtaining m f IMF components; Step 4: Use the S-G method to filter the noisy f IMF component to achieve noise reduction of the fault signal. The noise-reduced f IMF component is denoted as IMF l , where l ∈ [1, 2, ..., m]; Step 5: Reconstruct each IMF component to achieve noise reduction of the fault signal.

2. The noise reduction method for the distribution network fault signal based on CEEMDAN and S-G according to claim 1, characterized in that: The calculation expression of the correlation coefficient used in the correlation analysis in Step 3 is as follows: where R is the correlation coefficient, X is the initial zero-sequence current signal of the fault, and n is the number of f IMF components, indicating the i-th order f IMF component, where i ∈ [1, 2,..., n].

3. The CEEMDAN and S-G based noise reduction method for distribution network fault signals according to claim 1, wherein: The specific operation of Step 4 is: Let r and w be the width of the noise reduction window and the length of the sliding array respectively, where w＝2r+1 The data points x = (-r, -s + 1, 0, 1, r, -1, -r) in the sliding window are fitted by a high-order polynomial, and the high-order polynomial expression is: where q is the degree of the polynomial; a g is the coefficient of the g-th data; x g is the g-th data point in the window, where g ∈ [1, 2,..., q - 1]; f w is a high-order polynomial; Each movement of the window gives w equations: where r is the width of the noise reduction window; q is the degree of the polynomial; e is the least squares fitting residual; f is the f before noise reduction; IMF component; Y is the first term in the above formula, A is the second term in the above formula, and F is the third term in the above formula; f = YA + F The least squares solution of A is: A'=(Y T Y) -1 Y T f Wherein, A' is the least squares solution of A, and Y T is the transpose of Y, and f is the f IMF component before noise reduction, and after noise reduction it is: f' = YA' = Y(Y T Y) -1 Y T f.

4. An electronic 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 method described in any one of claims 1 to 3.

5. 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 method described in any one of claims 1 to 3.

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