Waveform distortion identification method based on mobile short data window algorithm

Abstract

The application provides a waveform distortion identification method based on a mobile short data window algorithm. The method comprises the following steps: selecting a fundamental frequency of a signal waveform to be analyzed, using a short data window method to calculate peak values or effective values of a plurality of sampling time points of the signal waveform to be analyzed, and obtaining a sequence of the peak values or the effective values of the plurality of sampling time points; calculating a judgment value according to the sequence; and judging whether waveform distortion exists at any sampling time point in the plurality of sampling time points based on the judgment value.

Description

Technical Field

[0001] This application relates to the field of power system relay protection, and more specifically, to a waveform distortion identification method based on the moving short data window algorithm. Background Technology

[0002] Harmonic calculation is a commonly used numerical analysis method in power system relay protection. For example, transformer saturation discrimination, transformer inrush current discrimination, and transformer overexcitation discrimination all require harmonic content calculation to determine and analyze the current status of primary equipment.

[0003] In numerical analysis, integral algorithms based on Fourier decomposition are commonly used to calculate the effective values ​​of the fundamental frequency and harmonics, and then to calculate the harmonic content. However, more accurate Fourier algorithms require calculation of sampled data over a complete period, resulting in a relatively long analysis data window. Especially in cases with low fundamental frequencies and long periods, the analysis time required to calculate harmonic content and analyze waveform distortion using Fourier integrals increases significantly.

[0004] Although the half-wave Fourier algorithm with a shorter data window only requires sampling data within half a cycle for calculation, which can shorten the calculation time to some extent, the calculation error of the fundamental effective value and harmonic effective value increases due to the shortened calculation time window, which in turn leads to an increase in the error of the harmonic content calculation result.

[0005] Numerical calculation methods employing short data windows generally include: the sample-value product algorithm based on instantaneous sampling values, and the 1 / 4 cycle Fourier algorithm, etc. These algorithms further shorten the time window required for calculation, but lead to a further increase in the error of the harmonic content calculation results.

[0006] Therefore, it is necessary to propose a waveform distortion identification method based on the moving short data window algorithm to solve the problems of long calculation time and large harmonic content error in the existing relay protection scheme based on the harmonic content principle to determine the waveform distortion degree, and shorten the time required for relay protection analysis of the sampled waveform distortion degree.

[0007] The information disclosed in the background section is only intended to enhance the understanding of the background of this application, and therefore may include information that does not constitute prior art known to those skilled in the art. Summary of the Invention

[0008] This application aims to provide a waveform distortion identification method based on the moving short data window algorithm, thereby improving the speed of waveform distortion identification in relay protection analysis.

[0009] According to one aspect of this application, a waveform distortion identification method based on a moving short data window algorithm is proposed, comprising:

[0010] Select the fundamental frequency of the signal waveform to be analyzed;

[0011] The peak value or effective value of the waveform of the signal to be analyzed at multiple sampling times is calculated using the short data window method, so as to obtain a sequence of the peak value or effective value at the multiple sampling times;

[0012] Calculate the judgment value based on the sequence;

[0013] Based on the judgment value, it is determined whether waveform distortion exists at any of the multiple sampling times.

[0014] According to some embodiments, calculating the peak value or RMS value of the waveform of the signal to be analyzed at multiple sampling times using the short data window method includes:

[0015] Use a frequency-independent short data window algorithm, or

[0016] A frequency-dependent short data window algorithm is used, wherein the frequency is the fundamental frequency of the selected signal waveform to be analyzed.

[0017] According to some embodiments, the step of calculating the peak value or effective value of the waveform of the signal to be analyzed at multiple sampling times using the short data window method, and obtaining the sequence of the peak value or effective value at the multiple sampling times, includes:

[0018] Calculating the peak value or effective value of any one of the plurality of sampling times of the waveform to be analyzed requires three sampling points t. n t n+1 t n+2 The voltage sample value at time n represents the nth sampling point, where n is an integer greater than or equal to 1, and the sampling interval between adjacent sampling points is the same;

[0019] In the sequence, two adjacent sampling times among the plurality of sampling times include at least one identical sampling point.

[0020] According to some embodiments, determining whether waveform distortion exists at any one of the plurality of sampling times based on the judgment value includes:

[0021] The judgment value is the average value of the sequence;

[0022] If the difference between the peak value or the effective value and the average value is less than a first threshold multiple of the average value, it is determined that the waveform at the sampling time is not distorted.

[0023] The first threshold value ranges from (0, 1).

[0024] According to some embodiments, calculating the judgment value based on the sequence includes:

[0025] Find a subset of the given sequence.

[0026] A subset of the sequence includes at least three consecutive peaks or valid values ​​in the sequence;

[0027] Calculate the variance or standard deviation of a subset of the sequence.

[0028] According to some embodiments, determining whether waveform distortion exists at any one of the plurality of sampling times based on the judgment value includes:

[0029] Based on the variance or standard deviation, determine whether waveform distortion exists at any of the multiple sampling times.

[0030] According to some embodiments, determining whether waveform distortion exists at any of the plurality of sampling times based on the variance or standard deviation includes:

[0031] If the variance of the subset is not greater than a second threshold multiple of any value in the subset, then in the sequence, the waveform at the sampling time of the subset is determined to be undistorted; wherein the second threshold ranges from (0, 1).

[0032] According to some embodiments, determining whether waveform distortion exists at any of the plurality of sampling times based on the variance or standard deviation includes:

[0033] If the standard deviation of the subset is not greater than a third threshold multiple of any value in the subset, the waveform at the sampling time of the subset is not distorted in the sequence; wherein the value range of the third threshold is (0, 1).

[0034] According to some embodiments, calculating the judgment value based on the sequence includes:

[0035] Calculate the average value of the sequence;

[0036] A subset of the sequence is obtained, wherein the subset of the sequence includes at least three consecutive peaks or valid values ​​in the sequence;

[0037] Calculate the variance or standard deviation of a subset of the sequence.

[0038] According to some embodiments, determining whether waveform distortion exists at any of the plurality of sampling times based on the judgment value includes:

[0039] Based on the average value of the sequence, the variance or standard deviation of a subset of the sequence, determine whether the waveform of the signal to be analyzed has waveform distortion.

[0040] According to some embodiments, determining whether the waveform of the signal to be analyzed has waveform distortion based on the average value of the sequence, the variance or standard deviation of a subset of the sequence includes:

[0041] The difference between the peak value or effective value in the sequence and the average value is less than a fourth threshold multiple of the average value; and

[0042] The variance of the subset is no greater than a fifth threshold multiple of any value in the subset; or the standard deviation of the subset is no greater than a sixth threshold multiple of any value in the subset.

[0043] The waveform of the signal to be analyzed is not distorted;

[0044] The values ​​of the fourth threshold, the fifth threshold, and the sixth threshold are in the range of (0, 1).

[0045] According to another aspect of this application, an electronic device is proposed, comprising:

[0046] Processor; and

[0047] The memory stores computer instructions that, when executed by the processor, cause the processor to perform the methods described above.

[0048] According to another aspect of this application, a non-transient computer storage medium is proposed, which stores a computer program that, when executed by a plurality of processors, causes the processors to perform the method described above.

[0049] This application provides a waveform distortion identification method based on a moving short data window algorithm. The short data window algorithm overcomes the problem of long overall calculation time caused by long data window algorithms in existing technologies. By judging the dispersion of the calculation results of the short data window algorithm, the regularity of the sampled waveform is judged, thus overcoming the problem of large error in the calculation of harmonic content by the short data window algorithm in existing technologies.

[0050] It should be understood that the above general description and the following detailed description are merely exemplary and do not limit this application. Attached Figure Description

[0051] The above and other objects, features, and advantages of this application will become more apparent from the detailed description of exemplary embodiments with reference to the accompanying drawings. The drawings described below are merely some embodiments of this application and are not intended to limit the scope of this application.

[0052] Figure 1 A flowchart illustrating an exemplary embodiment of a waveform distortion discrimination method is provided.

[0053] Figure 2Another embodiment of an exemplary method for determining waveform distortion is shown in the flowchart;

[0054] Figure 3 This diagram illustrates the structure of an electronic device provided in this application. Detailed Implementation

[0055] Exemplary embodiments will now be described more fully with reference to the accompanying drawings. However, these exemplary embodiments can be implemented in many forms and should not be construed as limited to the embodiments set forth herein; rather, they are provided so that this application will be thorough and complete, and will fully convey the concept of the exemplary embodiments to those skilled in the art. The same reference numerals in the drawings denote the same or similar parts, and therefore repeated descriptions of them will be omitted.

[0056] The described features, structures, or characteristics can be combined in any suitable manner in one or more embodiments. Numerous specific details are provided in the following description to give a full understanding of embodiments of this disclosure. However, those skilled in the art will recognize that the technical solutions of this disclosure can be practiced without one or more of these specific details, or other methods, components, materials, devices, etc. In these cases, well-known structures, methods, devices, implementations, materials, or operations will not be shown or described in detail.

[0057] The flowcharts shown in the accompanying drawings are merely illustrative and do not necessarily include all content and operations / steps, nor do they necessarily have to be performed in the described order. For example, some operations / steps can be broken down, while others can be combined or partially combined; therefore, the actual execution order may change depending on the specific circumstances.

[0058] The terms "first," "second," etc., in the specification, claims, and accompanying drawings of this application are used to distinguish different objects, not to describe a specific order. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or apparatus that includes a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units not listed, or may optionally include other steps or units inherent to these processes, methods, products, or apparatuses.

[0059] Those skilled in the art will understand that the accompanying drawings are merely schematic diagrams of exemplary embodiments, and the modules or processes in the drawings are not necessarily essential for implementing this application, and therefore cannot be used to limit the scope of protection of this application.

[0060] Figure 1 A flowchart of a waveform distortion discrimination method of an exemplary embodiment is shown.

[0061] S101, Select the rated frequency.

[0062] According to some embodiments, the rated frequency of a conventional power system is 50Hz, and the relay protection uses electrical quantities at 50Hz as the main characteristic quantities of system faults. Therefore, the fundamental frequency f is selected as 50Hz.

[0063] According to the example embodiment, the fundamental frequency f of the signal waveform to be analyzed is selected. This application takes 50Hz as an example, but this application is not limited to this.

[0064] S102, calculate the peak value or RMS value.

[0065] According to the example embodiment, the peak or RMS value of the signal waveform to be analyzed is calculated using a short data window algorithm.

[0066] According to some embodiments, there are various methods for short data window algorithms. Generally speaking, the time window length required for a short data window algorithm should be less than the fundamental frequency period. Taking the sampled-value product algorithm as an example, the sampled-value product algorithm can also be divided into two categories: frequency-dependent and frequency-independent. A typical calculation expression for each is given below:

[0067] When there are three sampled values, let U(n), U(n+K), and U(n+2K) be the sampled values ​​at sample point t, respectively. n t n+K t n+2K The voltage sample values ​​at time t, and the sampling interval of these three sample values ​​is K.

[0068] Using a frequency-independent sampling value product algorithm, the peak value expression of the sampled waveform is:

[0069]

[0070] Using a frequency-dependent sampling value product algorithm, the peak value expression of the sampled waveform is:

[0071]

[0072] In Equation 2, ω = 2πf.

[0073] Taking the expressions shown in Equations 1 and 2 as examples, the short data window algorithm can complete the peak value calculation of the sampled waveform without requiring a full cycle of sampling data.

[0074] S103, a sequence is formed based on the calculated values ​​of each point.

[0075] According to the example embodiment, as the sampling time window progresses, the sequence P(n) of calculated values ​​for each peak point of the waveform can be obtained according to the above formula. For example, the sequence P(n) is: [P t1 ,P t2 ,P t3 ,P t4 ,Pt5 ,P t6 ,P t7 ].

[0076] According to some embodiments, the computation time windows corresponding to two adjacent values ​​in the sequence P(n) should overlap. t1 P t2 The computation time windows need to overlap, P t5 P t6 The computation time windows need to overlap.

[0077] According to the example embodiment, P t1 P t2 The computation time windows need to overlap, P t1 For sampling point t n t n+1 t n+2 The peak values ​​of the waveform obtained from the voltage sampling values ​​U(n+1), U(n+1), and U(n+2) at time P are then... t2 For sampling point t n+1 t n+2 t n+3 The peak value of the waveform obtained at three sampling times, or P t2 Sampling time t n+2K t n+3K t n+4K The peak value of the waveform obtained at three sampling times, i.e., in the sequence P(n), at least one of the sampling times of adjacent peak points is the same sampling point, and the sampling time interval of adjacent sampling points is the same.

[0078] S11, Calculate the judgment value based on the sequence, and determine whether the waveform is distorted at any sampling time among multiple sampling times based on the judgment value.

[0079] According to the example embodiment, S11 includes steps S104-S109, but this application is not limited thereto.

[0080] S104, calculate the average value of the sequence.

[0081] According to the example embodiment, the average value of the sequence P(n) is calculated as P. ave .

[0082] S105 determines whether there is waveform distortion at the sampling time.

[0083] According to the example embodiment, each sampling time in P(n) is compared with P ave Compare the differences; if the difference between the sampling time and the average value is less than I of the average value... set1 If the difference between the sampling time and the average value is greater than I, then the waveform at that sampling time is considered undistorted; if the difference between the sampling time and the average value is greater than I, then the waveform at that sampling time is considered undistorted. set1 If the value is multiple times, then the waveform is considered to be distorted at that sampling moment.set1 The range of values ​​for is I set1 <1. And go to S109.

[0084] S106, Find a subset of the sequence.

[0085] According to the example embodiment, at least three consecutive calculated values ​​from sequence P(n) are taken to form a subset sequence P1(n), for example, P1(n) is: [P t3 ,P t4 ,P t5 ].

[0086] S107, calculate the variance of the subset sequence.

[0087] According to the example embodiment, the variance P of P1(n) is calculated. var .

[0088] S108, determine whether the sampling time of the subset sequence is distorted.

[0089] According to the example embodiment, if the variance P of P1(n) is... var I is not greater than any value in P1(n) set2 If the variance of P1(n) is multiplied by a factor of 1, then it is assumed that the waveform at the sampling time corresponding to P1(n) is not distorted; if the variance of P1(n) is P... var I greater than any value in P1(n) set2 If the value is multiple, then it is considered that the waveform at the sampling time corresponding to P1(n) in P(n) is distorted, I set2 The range of values ​​for is I set2 <1. And go to S109.

[0090] S109, determine whether the waveform of the signal to be analyzed is distorted.

[0091] According to the example embodiment, for any sampling time, if the discrimination results of S105 and S108 are both waveform distortion, then the sampling time in the waveform of the signal to be analyzed is considered to be waveform distortion; or, if any discrimination result of S105 and S108 is waveform distortion, then the sampling time in the waveform of the signal to be analyzed is considered to be waveform distortion.

[0092] According to an example embodiment, this application provides a waveform distortion identification method based on a moving short data window algorithm. The short data window algorithm overcomes the problem of long overall calculation time caused by long data window algorithms in the prior art. By judging the dispersion of the calculation results of the short data window algorithm, the regularity of the sampled waveform is judged, thus overcoming the problem of large error in the calculation of harmonic content by the short data window algorithm in the prior art.

[0093] Figure 2Another embodiment of an exemplary method for determining waveform distortion is shown in the flowchart.

[0094] S201, Select the rated frequency.

[0095] According to some embodiments, the rated frequency of a conventional power system is 50Hz, and the relay protection uses electrical quantities at 50Hz as the main characteristic quantities of system faults. Therefore, the fundamental frequency f is selected as 50Hz.

[0096] According to the example embodiment, the fundamental frequency f of the signal waveform to be analyzed is selected. This application takes 50Hz as an example, but this application is not limited to this.

[0097] S202, calculate peak value or RMS value.

[0098] According to the example embodiment, the peak or RMS value of the signal waveform to be analyzed is calculated using a short data window algorithm.

[0099] According to some embodiments, there are various methods for short data window algorithms. Generally speaking, the time window length required for a short data window algorithm should be less than the fundamental frequency period. Taking the sampled-value product algorithm as an example, the sampled-value product algorithm can also be divided into two categories: frequency-dependent and frequency-independent. A typical calculation expression for each is given below:

[0100] When there are three sampled values, let U(n), U(n+K), and U(n+2K) be the sampled values ​​at sample point t, respectively. n t n+K t n+2K The voltage sample values ​​at time t, and the sampling interval of these three sample values ​​is K.

[0101] Using a frequency-independent sampling value product algorithm, the peak value expression of the sampled waveform is:

[0102]

[0103] Using a frequency-dependent sampling value product algorithm, the peak value expression of the sampled waveform is:

[0104]

[0105] In Equation 2, ω = 2πf.

[0106] Taking the expressions shown in Equations 1 and 2 as examples, the short data window algorithm can complete the peak value calculation of the sampled waveform without requiring a full cycle of sampling data.

[0107] S203, a sequence is formed based on the calculated values ​​of each point.

[0108] According to the example embodiment, as the sampling time window progresses, the sequence P(n) of calculated values ​​for each peak point of the waveform can be obtained according to the above formula. For example, the sequence P(n) is: [Pt1 ,P t2 ,P t3 ,P t4 ,P t5 ,P t6 ,P t7 ].

[0109] According to some embodiments, the computation time windows corresponding to two adjacent values ​​in the sequence P(n) should overlap. For example, P t1 P t2 The computation time windows need to overlap, P t5 P t6 The computation time windows need to overlap.

[0110] According to the example embodiment, P t1 P t2 The computation time windows need to overlap, P t1 For sampling point t n t n+1 t n+2 The peak values ​​of the waveform obtained from the voltage sampling values ​​U(n+1), U(n+1), and U(n+2) at time P are then... t2 For sampling point t n+1 t n+2 t n+3 The peak value of the waveform obtained at three sampling times, or P t2 Sampling time t n+2K t n+3K t n+4K The peak value of the waveform obtained at three sampling times, i.e., in the sequence P(n), at least one of the sampling times of adjacent peak points is the same sampling point, and the sampling time interval of adjacent sampling points is the same.

[0111] S21, Calculate the judgment value based on the sequence, and determine whether the waveform is distorted at any sampling time among multiple sampling times based on the judgment value.

[0112] According to the example embodiment, S21 includes steps S204-S209, but this application is not limited thereto.

[0113] S204, calculate the average value of the sequence.

[0114] According to the example embodiment, the average value of the sequence P(n) is calculated as P. ave .

[0115] S205, determine whether there is waveform distortion at the sampling time.

[0116] According to the example embodiment, each sampling time in P(n) is compared with P ave Compare the differences; if the difference between the sampling time and the average value is less than I of the average value... set1If the difference between the sampling time and the average value is greater than I, then the waveform at that sampling time is considered undistorted; if the difference between the sampling time and the average value is greater than I, then the waveform at that sampling time is considered undistorted. set1 If the value is multiple times, then the waveform is considered to be distorted at that sampling moment. set1 The range of values ​​for is I set1 <1. And proceed to S209.

[0117] S206, Find a subset of the sequence.

[0118] According to the example embodiment, at least three consecutive calculated values ​​from sequence P(n) are taken to form a subset sequence P2(n), for example, P2(n) is: [P t3 ,P t4 ,P t5 ].

[0119] S207, calculate the standard deviation of the subset sequence.

[0120] Calculate the standard deviation of P2(n) according to the example embodiment.

[0121] S208, determine whether the sampling time of the subset sequence is distorted.

[0122] According to the example embodiment, if the standard deviation of P2(n) is not greater than I of any value in P2(n) set3 If the standard deviation of P2(n) is greater than I, then the waveform at the sampling time corresponding to P2(n) in P(n) is considered undistorted; if the standard deviation of P2(n) is greater than I of any value in P2(n)... set3 If the value is multiple, then the waveform distortion at the sampling time corresponding to P2(n) in P(n) is considered to be I. set3 The range of values ​​for is I set3 <1. And proceed to S209.

[0123] S209, determine whether the waveform of the signal to be analyzed is distorted.

[0124] According to the example embodiment, for any sampling time, if the discrimination results of S205 and S208 are both waveform distortion, then the waveform of the signal to be analyzed at that sampling time is considered to be a distorted waveform. Alternatively, if any discrimination result of S205 and S208 is waveform distortion, then the waveform of the signal to be analyzed at that sampling time is considered to be a distorted waveform.

[0125] Figure 3 This diagram illustrates the structure of an electronic device provided in this application.

[0126] See Figure 3 , Figure 3 An electronic device is provided, including a processor and a memory. The memory stores computer instructions, which, when executed by the processor, cause the processor to perform the computer instructions to achieve the following: Figure 1and / or Figure 2 The method and its detailed scheme are shown.

[0127] It should be understood that the above-described device embodiments are merely illustrative, and the device disclosed in this invention can be implemented in other ways. For example, the division of units / modules described in the above embodiments is only a logical functional division, and there may be other division methods in actual implementation. For example, multiple units, modules, or components may be combined, integrated into another system, or some features may be ignored or not executed.

[0128] Furthermore, unless otherwise specified, the functional units / modules in the various embodiments of the present invention can be integrated into one unit / module, or each unit / module can exist physically separately, or two or more units / modules can be integrated together. The integrated units / modules described above can be implemented in hardware or as software program modules.

[0129] If the integrated unit / module is implemented in hardware, the hardware can be digital circuits, analog circuits, etc. The physical implementation of the hardware structure includes, but is not limited to, transistors, memristors, etc. Unless otherwise specified, the processor or chip can be any suitable hardware processor, such as a CPU, GPU, FPGA, DSP, and ASIC, etc. Unless otherwise specified, the on-chip cache, off-chip memory, and storage can be any suitable magnetic or magneto-optical storage medium, such as resistive random access memory (RRAM), dynamic random access memory (DRAM), static random access memory (SRAM), enhanced dynamic random access memory (EDRAM), high-bandwidth memory (HBM), hybrid memory cube (HMC), etc.

[0130] If the integrated unit / module is implemented as a software program module and sold or used as an independent product, it can be stored in a computer-readable storage device (CMD). Based on this understanding, the technical solution of this invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a memory and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this disclosure. The aforementioned memory includes various media capable of storing program code, such as USB flash drives, read-only memory (ROM), random access memory (RAM), portable hard drives, magnetic disks, or optical disks.

[0131] This application embodiment also provides a non-transitory computer storage medium storing a computer program, which, when executed by multiple processors, causes the processors to perform actions such as... Figure 1 and / or Figure 2 The method and its detailed scheme are shown.

[0132] According to an example embodiment, this application provides a waveform distortion identification method based on a moving short data window algorithm. The short data window algorithm overcomes the problem of long overall calculation time caused by long data window algorithms in the prior art. By judging the dispersion of the calculation results of the short data window algorithm, the regularity of the sampled waveform is judged, thus overcoming the problem of large error in the calculation of harmonic content by the short data window algorithm in the prior art.

[0133] It should be clearly understood that this application describes how specific examples are formed and used, but this application is not limited to any details of these examples. Rather, based on the teachings of the disclosure of this application, these principles can be applied to many other embodiments.

[0134] Furthermore, it should be noted that the above figures are merely illustrative representations of the processes included in the method according to exemplary embodiments of this application, and are not intended to be limiting. It is readily understood that the processes shown in the above figures do not indicate or limit the temporal order of these processes. Additionally, it is readily understood that these processes may be executed synchronously or asynchronously, for example, in multiple modules.

[0135] Exemplary embodiments of this application have been specifically shown and described above. It should be understood that this application is not limited to the detailed structures, arrangements, or implementation methods described herein; rather, this application is intended to cover various modifications and equivalent arrangements contained within the spirit and scope of the appended claims.

Claims

1. A waveform distortion recognition method based on a moving short data window algorithm, characterized in that, include: Select the fundamental frequency of the signal waveform to be analyzed; The peak value or effective value of the signal waveform to be analyzed is calculated at multiple sampling times using the short data window method, resulting in a sequence of the peak value or effective value at the multiple sampling times, including: Calculating the peak value or effective value of any one of the plurality of sampling times of the waveform to be analyzed requires three sampling points. , , Voltage sampling value at time, , , Sampling points , , The voltage sample values ​​at that time, the sampling interval of these three sample values ​​is , n Indicates the first n One sampling point, n The value is an integer greater than or equal to 1, and the sampling interval between adjacent sampling points is the same; Adopting the sampling value integration algorithm irrelevant to frequency, the peak value of the sampling waveform Um The expression is: ； Calculate the judgment value based on the sequence; Determining whether waveform distortion exists at any of the plurality of sampling times based on the judgment value includes: The judgment value is the average value of the sequence; If the difference between the peak value or the effective value and the average value is less than a first threshold multiple of the average value, it is determined that the waveform at the sampling time is not distorted. The first threshold value ranges from (0, 1).

2. The identification method of claim 1, wherein, The calculation of the peak or effective value of the signal waveform under analysis at multiple sampling times using the short data window method includes: Use a frequency-independent short data window algorithm, or A frequency-dependent short data window algorithm is used, wherein the frequency is the fundamental frequency of the selected signal waveform to be analyzed.

3. The identification method of claim 1, wherein, The step of using the short data window method to calculate the peak value or effective value of the waveform of the signal to be analyzed at multiple sampling times, and obtaining the sequence of the peak value or effective value at the multiple sampling times, includes: In the sequence, two adjacent sampling times among the plurality of sampling times include at least one identical sampling point.

4. The identification method of claim 3, wherein, The step of calculating the judgment value based on the sequence includes: Find a subset of the given sequence. A subset of the sequence includes at least three consecutive peaks or valid values ​​in the sequence; Calculate the variance or standard deviation of a subset of the sequence.

5. The identification method of claim 4, wherein, The step of determining whether waveform distortion exists at any of the plurality of sampling times based on the judgment value includes: Based on the variance or standard deviation, determine whether waveform distortion exists at any of the multiple sampling times.

6. The identification method of claim 5, wherein, The step of determining whether waveform distortion exists at any of the plurality of sampling times based on the variance or standard deviation includes: If the variance of the subset is not greater than a second threshold multiple of any value in the subset, then in the sequence, the waveform at the sampling time of the subset is determined to be undistorted; wherein the second threshold ranges from (0, 1).

7. The identification method of claim 5, wherein, The step of determining whether waveform distortion exists at any of the plurality of sampling times based on the variance or standard deviation includes: If the standard deviation of the subset is not greater than a third threshold multiple of any value in the subset, the waveform at the sampling time in the subset is not distorted in the sequence; wherein the value range of the third threshold is (0, 1).

8. The identification method of claim 3, wherein, The step of calculating the judgment value based on the sequence includes: Calculate the average value of the sequence; A subset of the sequence is obtained, wherein the subset of the sequence includes at least three consecutive peaks or valid values ​​in the sequence; Calculate the variance or standard deviation of a subset of the sequence.

9. The identification method of claim 8, wherein, The step of determining whether waveform distortion exists at any of the plurality of sampling times based on the judgment value includes: Based on the average value of the sequence, the variance or standard deviation of a subset of the sequence, determine whether the waveform of the signal to be analyzed has waveform distortion.

10. The identification method of claim 8, wherein, The step of determining whether the waveform of the signal to be analyzed has waveform distortion based on the average value of the sequence, the variance or standard deviation of a subset of the sequence includes: The difference between the peak value or effective value in the sequence and the average value is less than a fourth threshold multiple of the average value; and The variance of the subset is no greater than a fifth threshold multiple of any value in the subset; or the standard deviation of the subset is no greater than a sixth threshold multiple of any value in the subset. The waveform of the signal to be analyzed is not distorted; The values ​​of the fourth threshold, the fifth threshold, and the sixth threshold are in the range of (0, 1).

11. An electronic device, comprising: include: processor; as well as A memory storing computer instructions that, when executed by the processor, cause the processor to perform the method according to any one of claims 1-10.

12. A non-transitory computer storage medium storing a computer program that, when executed by a plurality of processors, causes the processors to perform the method of any one of claims 1-10.

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