Transient electric energy quality disturbance detection method and system
Through the improved GWO-VMD algorithm and wavelet decomposition technology, the accuracy problem of traditional detection methods under noise interference is solved, and effective noise reduction processing and accurate detection of transient power quality disturbance signals are achieved.
Patent Information
- Application Number
- CN202411510681.6
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-10-28
- Publication Date
- 2025-01-10
AI Technical Summary
The traditional transient power quality disturbance detection method has poor detection accuracy under noise interference and cannot effectively reduce noise and process disturbed signals.
The improved GWO-VMD algorithm is used to adaptively decompose the disturbed signal. Combined with adaptive filtering and wavelet decomposition technology, the mutation point of the disturbed signal is obtained through the mode maximum point after noise reduction.
The accuracy of disturbed signals detection under noise interference is improved, and effective noise reduction processing is effectively used to enhance the reliability of detection results.
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Figure CN119291345A_ABST
Abstract
Description
Technical Field
[0001] The present application relates to the technical field of transient power quality, and in particular to a method and system for detecting transient power quality disturbances. Background Art
[0002] As new energy power generation equipment and other electrical equipment outside the power grid are connected to the power grid through nonlinear electronic devices, the power quality of the power grid has been seriously affected, among which transient power quality problems are particularly prominent. In the actual operation of the power grid, the collected signals are more or less mixed with useless information and noise. These noises cause the collected power quality disturbance signals to be distorted and distorted, making it impossible for us to accurately analyze and understand the overall operation of the entire power system. In addition, the traditional transient power quality disturbance detection method will be greatly affected by noise. Under the interference of noise, it is impossible to detect the accuracy of the disturbance signal. Summary of the invention
[0003] The technical problem to be solved by the present application is: how to effectively perform noise reduction processing on the disturbance signal to improve the accuracy of its detection under noise interference.
[0004] The technical solution adopted by this application to solve its technical problem is:
[0005] In a first aspect, the present application provides a method for detecting transient power quality disturbances, comprising the following steps:
[0006] S1, sampling the disturbance signal based on a preset sampling frequency (e.g., 2kHz) to obtain the target sequence s(t);
[0007] S2, the VMD algorithm (i.e., variational mode decomposition) is improved by using the GWO algorithm (i.e., Grey Wolf Optimization Algorithm), and the target sequence s(t) is adaptively decomposed by the improved GWO-VMD algorithm to obtain multiple VIMF (i.e., single-component amplitude-frequency modulation signal) components;
[0008] S3, calculating the noise level of the VIMF component to generate a first VIMF component and a second VIMF component; the second VIMF component is a low-frequency VIMF component;
[0009] S4, performing adaptive filtering and noise reduction on the first VIMF component to generate a third VIMF component;
[0010] S5, reconstructing the second VIMF component and the third VIMF component to obtain a target noise reduction signal;
[0011] S6, performing multi-layer decomposition of the target denoised signal based on the wavelet function to obtain the first layer detail coefficient d1;
[0012] S7, obtaining the modulus maximum of the first layer detail coefficient d1, and obtaining the mutation point of the disturbance signal through the modulus maximum point;
[0013] S8, determining the time and time interval of the emergence of the modulus maximum point, and obtaining the disturbance signal detection result.
[0014] Based on the first aspect, further, the target sequence s(t) includes a pure signal x(t) and a noise signal n(t), that is:
[0015] s(t)=x(t)+n(t).
[0016] Based on the first aspect, further, the VMD algorithm is improved by using the GWO algorithm, including:
[0017] Based on the GWO algorithm, the minimum value of envelope entropy is used as the fitness function to obtain the optimal VMD parameters.
[0018] Based on the first aspect, further, the above-mentioned calculation of the noise level of the VIMF component includes:
[0019] The fast Fourier transform algorithm is used to calculate the noise level of the VIMF component to generate a first VIMF component and a second VIMF component.
[0020] Based on the first aspect, further, the first VIMF component is a high-frequency VIMF component containing noise, and adaptive filtering and noise reduction are performed on the high-frequency VIMF component.
[0021] Based on the first aspect, further, the target wavelet in the wavelet function is used to perform multi-layer decomposition on the target denoised signal to obtain the first layer detail coefficient d1, which includes:
[0022] The target wavelet is the db10 wavelet, and the target denoised signal is decomposed into four layers based on the db10 wavelet.
[0023] Based on the first aspect, further, the above-mentioned method of obtaining the modulus maximum of the first layer detail coefficient d1 and obtaining the mutation point of the disturbance signal through the modulus maximum point is as follows:
[0024] Assume that the function is a low-pass smooth function and satisfies the following conditions:
[0025]
[0026] The wavelet function can be constructed by satisfying the above formula, taking the wavelet function θ(t) as the first-order derivative of the function, that is, ψ(t) = dθ(t) / dt; and let a be its scale parameter, satisfying θ a (t) = 1 / aψ(t / a); then the wavelet transform of the signal f(t) in the constructed wavelet function ψ(t) is:
[0027]
[0028] The first-order derivative ψ(t) of the θ(t) function is used as the mother function of the wavelet transform, and then the signal f(t) is subjected to wavelet transform. The modulus maximum point of the wavelet transform is the location of the signal mutation point.
[0029] In the second aspect, the present application provides a transient power quality disturbance detection system, including a signal sampling module, a signal decomposition module, a noise calculation module, a noise reduction processing module, a signal reconstruction module, a noise reduction signal multi-layer decomposition module, a modulus maximum point acquisition module and a detection result generation module, wherein:
[0030] A signal sampling module is used to sample the disturbance signal based on a preset sampling frequency to obtain a target sequence s(t);
[0031] The signal decomposition module is used to improve the VMD algorithm by using the GWO algorithm, and adaptively decompose the target sequence s(t) by using the improved GWO-VMD algorithm to obtain multiple VIMF components;
[0032] A noise calculation module, used for calculating the noise level of the VIMF component to generate a first VIMF component and a second VIMF component;
[0033] A noise reduction processing module, used for performing adaptive filtering and noise reduction on the first VIMF component to generate a third VIMF component;
[0034] A signal reconstruction module, used for reconstructing the second VIMF component and the third VIMF component to obtain a target noise reduction signal;
[0035] A denoising signal multi-layer decomposition module is used to perform multi-layer decomposition on the target denoising signal based on the target wavelet in the wavelet function to obtain the first layer detail coefficient d1;
[0036] A module for acquiring a modulus maximum point is used to acquire the modulus maximum of the first layer detail coefficient d1, and obtain the mutation point of the disturbance signal through the modulus maximum point;
[0037] The detection result generation module is used to determine the time and time interval of the occurrence of the modulus maximum point to obtain the disturbance signal detection result.
[0038] In a third aspect, the present application further provides an electronic device, comprising a memory for storing one or more programs and a processor. When the one or more programs are executed by the processor, any method in the first aspect described above is implemented.
[0039] In a fourth aspect, the present application further provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements any one of the methods in the first aspect described above.
[0040] The beneficial effects of this application are:
[0041] The present application can effectively perform noise reduction processing on the disturbance signal to improve the accuracy of its detection under noise interference. BRIEF DESCRIPTION OF THE DRAWINGS
[0042] Figure 1 A flow chart of a transient power quality disturbance detection method provided in an embodiment of the present application;
[0043] Figure 2 A principle block diagram of a transient power quality disturbance detection system provided in an embodiment of the present application;
[0044] Figure 3 A structural block diagram of an electronic device provided in an embodiment of the present application.
[0045] The accompanying drawings are marked as follows: 100, signal sampling module; 200, signal decomposition module; 300, noise calculation module; 400, noise reduction processing module; 500, signal reconstruction module; 600, noise reduction signal multi-layer decomposition module; 700, modulus maximum point acquisition module; 800, detection result generation module; 101, memory; 102, processor; 103, communication interface. DETAILED DESCRIPTION
[0046] In order to make the purpose, technical solution and advantages of the embodiments of the present application clearer, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, rather than all the embodiments. The components of the embodiments of the present application described and shown in the drawings here can be arranged and designed in various different configurations.
[0047] Therefore, the following detailed description of the embodiments of the present application provided in the accompanying drawings is not intended to limit the scope of the present application for which protection is sought, but merely represents selected embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in the field without creative work are within the scope of protection of the present application.
[0048] It should be noted that similar reference numerals and letters denote similar items in the following drawings, and therefore, once an item is defined in one drawing, further definition and explanation thereof is not required in subsequent drawings.
[0049] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "comprise a ..." do not exclude the existence of other identical elements in the process, method, article or device including the elements.
[0050] In the description of the embodiments of the present application, "plurality" means at least 2.
[0051] like Figure 1 As shown, an embodiment of a transient power quality disturbance detection method of the present application includes the following steps:
[0052] S1, samples the disturbance signal based on the preset sampling frequency to obtain the target sequence s(t);
[0053] S2, the VMD algorithm is improved by using the GWO algorithm, and the target sequence s(t) is adaptively decomposed by the improved GWO-VMD algorithm to obtain multiple VIMF components;
[0054] S3, calculating the noise level of the VIMF component to generate a first VIMF component and a second VIMF component; the second VIMF component is a low-frequency VIMF component;
[0055] S4, performing adaptive filtering and noise reduction on the first VIMF component to generate a third VIMF component;
[0056] S5, reconstructing the second VIMF component and the third VIMF component to obtain a target noise reduction signal;
[0057] S6, performing multi-layer decomposition on the target denoised signal based on the target wavelet in the wavelet function to obtain the first layer detail coefficient d1;
[0058] S7, obtaining the modulus maximum of the first layer detail coefficient d1, and obtaining the mutation point of the disturbance signal through the modulus maximum point;
[0059] S8, determining the time and time interval of the emergence of the modulus maximum point, and obtaining the disturbance signal detection result.
[0060] In one embodiment of the present application, the target sequence s(t) includes a pure signal x(t) and a noise signal n(t), that is:
[0061] s(t)=x(t)+n(t).
[0062] In one embodiment of the present application, the improvement of the VMD algorithm by using the GWO algorithm includes:
[0063] Based on the GWO algorithm, the minimum value of envelope entropy is used as the fitness function to obtain the optimal VMD parameters.
[0064] Among them, the GWO algorithm improves the VMD algorithm by utilizing the sensitivity of VMD to noise and decomposing the noisy transient power quality disturbance signal into a series of noises around the center frequency ω k The VIMF component signal u k ; Envelope entropy is sensitive to the sparsity of the signal. The GWO algorithm uses the minimum envelope entropy as the fitness function to determine the optimal VMD parameters; Envelope entropy E p Calculated using the following formula:
[0065]
[0066] In the Kth VIMF, where q(i) is the envelope signal, p i is the normalized probability distribution sequence, N is the number of sampling points, E p is the envelope entropy.
[0067] Although the VMD algorithm has strong signal analysis capabilities, it is difficult to select VMD hyperparameters. The GWO algorithm is used to optimize the VMD hyperparameters c and α to obtain the optimal c and α combination, so that GWO can quickly find the optimal position and improve the efficiency of the algorithm.
[0068] In one embodiment of the present application, calculating the noise level of the VIMF component includes:
[0069] The noise level of the VIMF component is calculated by using a fast Fourier transform algorithm to generate a first VIMF component and a second VIMF component. The second VIMF component is a low-frequency VIMF component.
[0070] In one embodiment of the present application, the first VIMF component is a high-frequency VIMF component containing noise, and the high-frequency VIMF component is denoised by the following method:
[0071] 1) Let the starting time i = 0, the coefficient vector H(0) of the adaptive filter can be any value;
[0072] 2) Based on the input signal vector X(i), the error signal e(i) is calculated using the desired signal d(i) and the filter output signal y(i);
[0073] 3) According to the error signal, the filter is updated using the following formula:
[0074] H(i+1)=H(i)+μe(i)X(i);
[0075] Where μ represents the coefficient error vector, and the relationship is as follows:
[0076]
[0077] λ max Represents the maximum eigenvalue of the autocorrelation matrix of the input signal;
[0078] 4) Repeat steps 2) and 3) until the signal reaches a stable state (ie, the coefficient H remains almost unchanged).
[0079] In one embodiment of the present application, the target denoised signal is decomposed into multiple layers based on the target wavelet in the wavelet function to obtain the first layer detail coefficient d1, which includes:
[0080] The target wavelet is the db10 wavelet, and the target noise reduction signal is decomposed into four layers based on the db10 wavelet.
[0081] After the noise reduction is completed, the transient power quality disturbance detection is started to determine the wavelet basis function and decomposition scale of the DWT algorithm. Since the db10 wavelet has good performance, it is selected to perform a 4-layer decomposition of the target noise reduction signal after noise reduction; and since the modulus maximum information of the signal is mainly distributed in the first layer detail coefficient d1 of the wavelet decomposition, the number of decomposition layers of the wavelet decomposition is determined by the following formula:
[0082]
[0083] Among them, the sampling frequency of the disturbance signal is j s , the fundamental frequency is j0, m is the number of wavelet decomposition layers; [·] indicates the calculation result is rounded.
[0084] In one embodiment of the present application, the modulus maximum of the first layer detail coefficient d1 is obtained, and the specific method of obtaining the mutation point of the disturbance signal through the modulus maximum point is:
[0085] Assume that the function θ(t) is a low-pass smooth function and satisfies the following conditions:
[0086]
[0087] The wavelet function can be constructed by satisfying the above formula, taking the wavelet function as the first-order derivative of the function θ(t), that is, ψ(t) = dθ(t) / dt; and let a be its scale parameter, satisfying θ a (t) = 1 / aψ(t / a); then the wavelet transform of the signal f(t) in the constructed wavelet function ψ(t) is:
[0088]
[0089] The first-order derivative ψ(t) of the function θ(t) is used as the mother function of the wavelet transform, and then the signal f(t) is subjected to wavelet transform. The modulus maximum point of the wavelet transform is the position of the signal mutation point.
[0090] like Figure 2 As shown, an embodiment of a transient power quality disturbance detection system of the present application includes a signal sampling module 100, a signal decomposition module 200, a noise calculation module 300, a noise reduction processing module 400, a signal reconstruction module 500, a noise reduction signal multi-layer decomposition module 600, a modulus maximum point acquisition module 700 and a detection result generation module 800, wherein:
[0091] The signal sampling module 100 is used to sample the disturbance signal based on a preset sampling frequency to obtain a target sequence s(t);
[0092] The signal decomposition module 200 is used to improve the VMD algorithm by using the GWO algorithm, obtain and adaptively decompose the target sequence s(t) by using the improved GWO-VMD algorithm, and obtain multiple VIMF components;
[0093] A noise calculation module 300 is used to calculate the noise level of the VIMF component to generate a first VIMF component and a second VIMF component;
[0094] A noise reduction processing module 400 is used to perform adaptive filtering and noise reduction on the first VIMF component to generate a third VIMF component;
[0095] A signal reconstruction module 500 is used to reconstruct the second VIMF component and the third VIMF component to obtain a target noise reduction signal;
[0096] The denoising signal multi-layer decomposition module 600 is used to perform multi-layer decomposition on the target denoising signal based on the target wavelet in the wavelet function to obtain the first layer detail coefficient d1;
[0097] The module 700 for acquiring the modulus maximum point is used to acquire the modulus maximum of the first layer detail coefficient d1, and obtain the mutation point of the disturbance signal through the modulus maximum point;
[0098] The detection result generating module 800 is used to determine the time and time interval of the occurrence of the modulus maximum point to obtain the disturbance signal detection result.
[0099] like Figure 3 As shown, in a third aspect, an embodiment of the present application further provides an electronic device, which includes a memory 101 for storing one or more programs and a processor 102. When the one or more programs are executed by the processor 102, any method in the first aspect described above is implemented.
[0100] The memory 101, the processor 102 and the communication interface 103 are directly or indirectly electrically connected to each other to achieve data transmission or interaction. For example, these elements can be electrically connected to each other through one or more communication buses or signal lines. The memory 101 can be used to store software programs and modules, and the processor 102 executes various functional applications and data processing by executing the software programs and modules stored in the memory 101. The communication interface 103 can be used to communicate signaling or data with other node devices.
[0101] Among them, the memory 101 can be, but is not limited to, a random access memory (RAM), a read only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable read-only memory (EEPROM), etc.
[0102] The processor 102 may be an integrated circuit chip with signal processing capability. The processor 102 may be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it may also be a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components.
[0103] In a fourth aspect, the embodiment of the present application further provides a computer-readable storage medium on which a computer program is stored, and when the computer program is executed by the processor 102, a method as in any one of the first aspects described above is implemented. If the function is implemented in the form of a software function module and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on such an understanding, the technical solution of the present application is essentially 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, and the computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM, Read-Only Memory), a random access memory (RAM, Random Access Memory), a disk or an optical disk.
[0104] The above are only preferred embodiments of the present application and are not intended to limit the present application. For those skilled in the art, the present application may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included in the protection scope of the present application.
[0105] It will be apparent to those skilled in the art that the present application is not limited to the details of the exemplary embodiments described above, and that the present application can be implemented in other specific forms without departing from the spirit or essential features of the present application. Therefore, the embodiments should be considered exemplary and non-limiting in all respects, and the scope of the present application is defined by the appended claims rather than the above description, and it is intended that all changes falling within the meaning and scope of the equivalent elements of the claims be included in the present application. Any reference numeral in a claim should not be considered as limiting the claim to which it relates.
Claims
1. A method for detecting transient power quality disturbance, characterized in that: The following steps are involved: S1, samples the disturbance signal based on the preset sampling frequency to obtain the target sequence s(t); S2, using the GWO algorithm to improve the VMD algorithm, and adaptively decomposing the target sequence s(t) by the improved GWO-VMD algorithm to obtain multiple VIMF components; S3, calculating the noise level of the VIMF component to generate a first VIMF component and a second VIMF component; S4, performing adaptive filtering and noise reduction on the first VIMF component to generate a third VIMF component; S5, reconstructing the second VIMF component and the third VIMF component to obtain a target noise reduction signal; S6, performing multi-layer decomposition on the target denoised signal based on the target wavelet in the wavelet function to obtain a first layer detail coefficient d1; S7, obtaining the modulus maximum of the first layer detail coefficient d1, and obtaining the mutation point of the disturbance signal through the modulus maximum point; S8, determining the time and time interval of the emergence of the modulus maximum point, and obtaining the disturbance signal detection result.
2. The transient power quality disturbance detection method according to claim 1, characterized in that: The target sequence s(t) includes a pure signal x(t) and a noise signal n(t), namely: s(t)=x(t)+n(t).
3. The transient power quality disturbance detection method according to claim 1, characterized in that: The GWO algorithm is used to improve the VMD algorithm, including: Based on the GWO algorithm, the minimum value of envelope entropy is used as the fitness function to obtain the optimal VMD parameters.
4. The transient power quality disturbance detection method according to claim 1, characterized in that: The calculation of the noise level of the VIMF component includes: The fast Fourier transform algorithm is used to calculate the noise level of the VIMF component to generate a first VIMF component and a second VIMF component.
5. The transient power quality disturbance detection method according to claim 4, characterized in that: The first VIMF component is a high-frequency VIMF component containing noise, and adaptive filtering and noise reduction are performed on the high-frequency VIMF component.
6. The transient power quality disturbance detection method according to claim 1, characterized in that: The target denoised signal is decomposed into multiple layers based on the target wavelet in the wavelet function to obtain the first layer detail coefficient d1, which includes: The target wavelet is the db10 wavelet, and the target noise reduction signal is decomposed into four layers based on the db10 wavelet.
7. The transient power quality disturbance detection method according to claim 1, characterized in that: The specific method of obtaining the modulus maximum of the first layer detail coefficient d1 and obtaining the mutation point of the disturbance signal through the modulus maximum point is: Assume that the function θ(t) is a low-pass smooth function and satisfies the following conditions: The wavelet function can be constructed by satisfying the above formula, taking the wavelet function θ(t) as the first-order derivative of the function, that is, ψ(t) = dθ(t) / dt; and let a be its scale parameter, satisfying θ a (t) = 1 / aψ(t / a); the wavelet transform of the signal f(t) in the constructed wavelet function ψ(t) is: The first-order derivative ψ(t) of the θ(t) function is used as the mother function of the wavelet transform, and then the signal f(t) is subjected to wavelet transform. The modulus maximum point of the wavelet transform is the location of the signal mutation point.
8. A transient power quality disturbance detection system, characterized in that: It includes a signal sampling module, a signal decomposition module, a noise calculation module, a noise reduction processing module, a signal reconstruction module, a noise reduction signal multi-layer decomposition module, a modulus maximum point acquisition module and a detection result generation module, among which: A signal sampling module is used to sample the disturbance signal based on a preset sampling frequency to obtain a target sequence s(t); A signal decomposition module is used to improve the VMD algorithm by using the GWO algorithm, and to adaptively decompose the target sequence s(t) by using the improved GWO-VMD algorithm to obtain multiple VIMF components; A noise calculation module, used for calculating the noise level of the VIMF component to generate a first VIMF component and a second VIMF component; A noise reduction processing module, configured to perform adaptive filtering and noise reduction on the first VIMF component to generate a third VIMF component; A signal reconstruction module, used for reconstructing the second VIMF component and the third VIMF component to obtain a target noise reduction signal; A denoising signal multi-layer decomposition module is used to perform multi-layer decomposition on the target denoising signal based on the target wavelet in the wavelet function to obtain a first layer detail coefficient d1; A module for acquiring a modulus maximum point, used to acquire the modulus maximum of the first layer detail coefficient d1, and obtain the mutation point of the disturbance signal through the modulus maximum point; The detection result generation module is used to determine the time and time interval of the occurrence of the modulus maximum point to obtain the disturbance signal detection result.
9. An electronic device, characterized in that: include: A memory for storing one or more programs; processor; When the one or more programs are executed by the processor, the method according to any one of claims 1 to 7 is 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 method according to any one of claims 1 to 7 is implemented.