A method and system for harmonic distortion type recognition based on symmetry features
By employing a harmonic distortion type identification method based on symmetry characteristics, and utilizing sliding window sampling and dual-criteria collaborative identification technology, the limitations of existing harmonic analysis methods are overcome, enabling rapid and accurate identification and fault analysis of power system harmonics.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- SHANDONG UNIV
- Filing Date
- 2026-05-06
- Publication Date
- 2026-06-02
AI Technical Summary
Existing harmonic analysis methods for power systems are unable to effectively distinguish between interharmonics and integer harmonics, resulting in a lack of specificity in harmonic mitigation solutions. Furthermore, these methods are computationally complex or rely on a large number of labeled samples, leading to insufficient generalization ability.
A harmonic distortion type identification method based on symmetry characteristics is adopted. By using sliding window sampling, single-window harmonic energy variance index and dual-criteria collaborative identification technology, the rapid identification of interharmonics, even harmonics and odd harmonics is realized, simplifying the calculation process.
It enables accurate and lightweight identification of harmonic distortion types in power signals, improves data acquisition quality and efficiency, simplifies spectrum leakage issues, and provides a basis for rapid fault analysis.
Smart Images
Figure CN122131017A_ABST
Abstract
Description
Technical Field
[0001] This invention belongs to the field of power quality measurement technology, specifically relating to a method and system for identifying harmonic distortion types based on symmetry characteristics. Background Technology
[0002] The statements in this section are merely background information related to the present invention and do not necessarily constitute prior art.
[0003] With the grid integration of new energy sources, such as photovoltaic and wind power, whose large-scale integration rate has exceeded 35%, and the widespread application of power electronic devices, power system harmonics have exhibited new characteristics of multi-source coupling, wide-band distribution, and dynamic changes. Not only are traditional integer and odd harmonics still the main sources of interference, easily causing wide-band resonance phenomena, but the harmonic frequency band has also widened further compared to traditional distribution networks, resulting in more severe hazards. Interharmonics frequently occur due to the slip effect of new energy converters and the charging and discharging switching of energy storage PCS, further aggravating voltage flicker and causing zero-crossing monitoring errors and overload. Even harmonics also exceed the original suppression threshold due to scenarios such as three-phase load imbalance and LED cluster power supply, triggering electromagnetic transient response processes and causing serious resonant overvoltage problems. Therefore, accurate measurement and identification of power system harmonic components have become a key link in ensuring the reliable operation of the power system and improving power quality.
[0004] Current methods for harmonic analysis in power systems mainly include Fourier Transform (FFT) and its improved algorithms (STFT, wavelet transform), as well as machine learning-based identification methods, but all have certain limitations. FFT and its improved algorithms are constrained by the trade-off between time and frequency resolution; STFT struggles to capture both steady-state and transient harmonics; wavelet transform has high computational complexity and cannot effectively distinguish between interharmonics and integer harmonics. Machine learning-based methods rely on a large number of labeled samples, have insufficient generalization ability, and require significant hardware computing power. Furthermore, most existing methods can only identify a single type of harmonic, resulting in a lack of specificity in harmonic mitigation solutions and low mitigation efficiency. Summary of the Invention
[0005] To address the aforementioned problems, this invention proposes a harmonic distortion type identification method and system based on symmetry characteristics. This invention constructs a harmonic distortion type identification model based on time-domain statistics and symmetry characteristics, and proposes a dual-criteria collaborative signal harmonic distortion type identification scheme, achieving accurate and lightweight identification of power signal harmonic distortion types.
[0006] According to some embodiments, the present invention adopts the following technical solution: A method for identifying harmonic distortion types based on symmetry features includes the following steps: Power signal data is acquired by sliding the preset sampling window; The data from each sampling window are processed to extract harmonic components and calculate the single-window harmonic energy variance index. The calculated single-window harmonic energy variance index is grouped and compared with the preset centrally symmetric normalized mean square error threshold to determine whether each group of data conforms to the centrally symmetric distribution. Then, it is compared with the interharmonic characteristic error threshold to obtain the interharmonic criterion variable. The time-domain data of any complete single cycle is processed, calculated and analyzed. By comparing the consistency between the shifted first half-cycle data and the inverted second half-cycle data, the even harmonic criterion variable is obtained. Based on the interharmonic and even harmonic criterion variables, the type of signal harmonic distortion is identified.
[0007] As an alternative implementation method, the process of acquiring power signal data by sliding according to a preset sampling window includes: real-time data acquisition of power signals monitored by the measuring equipment, using a sliding window sampling strategy, taking a complete cycle as the sampling window and sliding half a cycle each time to achieve continuous acquisition, and acquiring power signal data containing harmonic components.
[0008] As an alternative implementation, the process of processing the data of each acquired sampling window, extracting harmonic components, and calculating the single-window harmonic energy variance index includes: expressing the data of each acquired sampling window as a time-domain waveform signal, and calculating the single-window harmonic energy variance index according to the definition of variance in higher-order statistics. ; In the formula, This is a method for calculating the single-window harmonic energy variance index. The moment when the waveform signal is generated. For the first The start time of each sampling window; This indicates that the length of each sampling window is... , It is a time-domain waveform signal.
[0009] As an alternative implementation method, the calculated single-window harmonic energy variance index is grouped and compared with a preset centrally symmetric normalized mean square error threshold to determine whether each group of data conforms to a centrally symmetric distribution. This process includes: grouping the calculated single-window harmonic energy variance index according to the required frequency resolution requirements. Calculate the center-symmetric normalized mean square error for each group of data and compare it with the preset center-symmetric normalized mean square error threshold. If the center-symmetric normalized mean square error of a certain group of data is less than the preset center-symmetric normalized mean square error threshold, then the group of data is centrally symmetric.
[0010] As an alternative implementation method, the process of obtaining the interharmonic criterion variable by comparing it with the interharmonic characteristics and setting an error threshold includes: if a set of data satisfies a centrally symmetric distribution, the average, maximum and minimum values of the set of data are calculated; if the ratio of the difference between the maximum and minimum values of the corresponding set to the average value is greater than the preset error threshold, then the interharmonic criterion variable is zero; otherwise, the interharmonic criterion variable is one.
[0011] As an alternative implementation method, the process of processing and analyzing the time-domain data of any complete single cycle, and obtaining the even harmonic criterion variable by comparing the consistency between the shifted first half-cycle data and the inverted second half-cycle data, includes: calculating the shifted first half-cycle data and the inverted second half-cycle data based on the time-domain data of any complete single cycle; calculating the half-wave symmetry based on the shifted first half-cycle data and the inverted second half-cycle data; comparing the half-wave symmetry with the set symmetry threshold; if the half-wave symmetry is less than the set symmetry threshold, the even harmonic criterion variable is one; otherwise, it is zero.
[0012] As an alternative implementation, the process of identifying the type of signal harmonic distortion based on the interharmonic criterion variable and the even harmonic criterion variable includes: if the interharmonic criterion variable is one, then an interharmonic exists; if the interharmonic criterion variable is zero and the even harmonic criterion variable is one, then both odd and even harmonics exist; otherwise, only odd harmonics exist.
[0013] A harmonic distortion type identification system based on symmetry features, comprising: The data acquisition module is configured to acquire power signal data by sliding along a preset sampling window; The single-window index calculation module is configured to process the data acquired in each sampling window, extract harmonic components, and calculate the single-window harmonic energy variance index. The interharmonic criterion calculation module is configured to group the calculated single-window harmonic energy variance index, and compare it with the preset central symmetric normalized mean square error threshold to determine whether each group of data conforms to the central symmetric distribution. Then, it is compared with the interharmonic characteristic error threshold to obtain the interharmonic criterion variable. The even harmonic criterion calculation module is configured to process and analyze the time-domain data of any complete single cycle. By comparing the consistency between the shifted first half-cycle data and the inverted second half-cycle data, the even harmonic criterion variables are obtained. The distortion type comprehensive identification module is configured to identify the signal harmonic distortion type by comprehensively considering the interharmonic criterion variable and the even harmonic criterion variable.
[0014] An electronic device includes a memory and a processor, as well as computer instructions stored in the memory and running on the processor, wherein the computer instructions, when executed by the processor, perform the steps in the method described above.
[0015] Compared with the prior art, the beneficial effects of the present invention are as follows: (1) The present invention adopts a sliding window sampling strategy, which not only ensures the complete acquisition of the fundamental wave and integer harmonics in the power signal, but also realizes the continuous acquisition of non-integer harmonics, and obtains richer harmonic information. Compared with the traditional acquisition method, it improves the quality and efficiency of data acquisition. (2) This invention innovatively modifies the variance definition to construct a single-window harmonic energy variance index, which does not require frequency domain transformation and complex preprocessing. While achieving lightweight calculation, it avoids the problem of spectrum leakage and solves the interference of harmonic order and phase on the calculation results. (3) This invention proposes a dual-threshold verification interharmonic identification logic. It filters effective data by using a centrally symmetric normalized mean square error threshold and combines the interharmonic characteristics to design an error threshold to generate criterion variables, thereby realizing the identification of interharmonics and providing effective data support for the subsequent management of interharmonics.
[0016] (4) This invention utilizes the difference that odd harmonics are half-wave symmetrical while even harmonics do not have this characteristic to propose a method for constructing even harmonic criteria. Even harmonics can be quickly identified without complex spectrum analysis, breaking through the traditional analysis mode that relies on frequency domain transformation and simplifying the calculation process.
[0017] (5) This invention achieves the identification of harmonic distortion types through a dual-criteria collaborative system, and realizes the rapid identification of interharmonics, even harmonics and odd harmonics in the time domain, providing a basis for rapid judgment of power system fault analysis.
[0018] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, preferred embodiments are described below in detail with reference to the accompanying drawings. Attached Figure Description
[0019] The accompanying drawings, which form part of this invention, are used to provide a further understanding of the invention. The illustrative embodiments of the invention and their descriptions are used to explain the invention and do not constitute an improper limitation of the invention.
[0020] Figure 1 A flowchart of the harmonic distortion type identification method provided in an embodiment of the present invention; Figure 2 This is a schematic diagram of the sliding window sampling principle provided in an embodiment of the present invention; Figure 3 This is a waveform diagram of a synthesized signal containing interharmonics provided in an embodiment of the present invention; Figure 4This is a schematic diagram of the logic flow for interharmonic criterion provided in an embodiment of the present invention. Detailed Implementation
[0021] The present invention will be further described below with reference to the accompanying drawings and embodiments.
[0022] It should be noted that the following detailed description is illustrative and intended to provide further explanation of the invention. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.
[0023] It should be noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the scope of exemplary embodiments according to the invention. As used herein, the singular form is intended to include the plural form as well, unless the context clearly indicates otherwise. Furthermore, it should be understood that when the terms "comprising" and / or "including" are used in this specification, they indicate the presence of features, steps, operations, devices, components, and / or combinations thereof.
[0024] Where there is no conflict, the embodiments and features described in this application may be combined with each other.
[0025] Example 1 like Figure 1 As shown, a method for identifying harmonic distortion types with symmetrical characteristics includes the following steps: S100: Real-time data acquisition of power signals monitored by the measuring equipment. A sliding window sampling strategy is employed, using a full cycle as the sampling window and sliding half a cycle at a time to achieve continuous acquisition. Figure 2 As shown, power signal data containing rich harmonic information is obtained.
[0026] S101: Based on the fundamental frequency of the power system Define sampling parameters, including the sampling window length. Sliding step size Sampling frequency and the number of window sampling points .
[0027] 1) Define the sampling window length parameter as the complete fundamental period, i.e.: (1) In the formula: The fundamental frequency of the power system; The fundamental frequency period of the power system; This is the sampling window length parameter.
[0028] 2) Define the sliding step size parameter as half the length of the sampling window, that is: (2) In the formula: This is the sliding step size parameter.
[0029] 3) Define the sampling frequency parameter to satisfy the Nyquist sampling theorem, that is: (3) In the formula: This refers to the sampling frequency parameter; This refers to the highest harmonic frequency to be analyzed in the power system.
[0030] 4) Define the number of sampling points in the window as: (4) In the formula: The number of sampling points in the window is a parameter determined by the sampling frequency. Decide.
[0031] S102: Using the parameters defined above, a sliding window discretized data acquisition process is obtained to continuously capture the time-domain waveform of power signals containing harmonic components.
[0032] Each sampling window has a length of one period, the first... The start time and time range of each sampling window are as follows: (5) Range of the i-th sampling window: (6) In the formula: For the first The start time of each sampling window; This indicates that the length of each sampling window is... .
[0033] S200: Processes and calculates the data collected in each sampling window, extracts harmonic components through a modified higher-order statistical method, and then calculates the single-window harmonic energy variance index to obtain the index results reflecting the harmonic changes within the period, providing a basis for harmonic analysis.
[0034] S201: By acquiring data in real time using the measurement equipment from the previous step, the time-domain waveform signal of each sampling window is obtained, so as to facilitate the calculation of higher-order statistics on the acquired raw data.
[0035] Time-domain waveform signal: (7) in, (8) In the formula: This represents the moment the waveform signal was generated. The first in the waveform signal One harmonic; This represents the total number of harmonics contained in the waveform signal; For the first The amplitude of each harmonic; For the first The number of harmonics; For the first The phase of each harmonic relative to the fundamental wave; It is a time-domain waveform signal; The waveform signal under ideal conditions; This is a noise signal.
[0036] S202: Based on the definition of variance in higher-order statistics, the following modifications were made to the original formula to obtain the single-window harmonic energy variance index, so as to ensure that the final calculation result is not affected by the harmonic order and phase while using lightweight data calculation.
[0037] (9) In the formula: This is a method for calculating the variance index of harmonic energy in a single window.
[0038] Discretize it to fit the data collected in the computation window: (10) In the formula: To The form after discretization; These are the sampled data points; The time-domain waveform data value obtained from sampling.
[0039] S300: The calculated single-window harmonic energy variance index is grouped and processed. A matching analysis is performed on the data using harmonic problem criteria. A centrally symmetric normalized mean square error threshold is set to determine if the requirements are met. The waveform signal containing interharmonics is as follows: Figure 3 As shown, by setting an error threshold based on the characteristics of interharmonic waves, the interharmonic wave criterion variable is obtained, thus achieving data lightweighting.
[0040] S301: According to the required frequency resolution, the calculated single-window harmonic energy variance index is grouped as follows: (11) In the formula: M The number of cycles selected to meet the required frequency resolution. The calculated single-window harmonic energy variance index data is given for each 2... M They are grouped into sets of 1 for processing.
[0041] S302: Let the threshold for the centrally symmetric normalized mean square error be δ1. Calculate and determine whether the data set is centrally symmetric. The determination method is as follows: Calculate the Normalized Mean Squared Error (NMSE). If it is less than the centrally symmetric NMSE threshold δ1 (in this embodiment, the threshold is set based on the accuracy of the collected data and practical experience), then the data set exhibits a centrally symmetric distribution, i.e.: (12) If the data set follows a centrally symmetric distribution, the mean, maximum, and minimum values of the data set are calculated using the following formula: (13) (14) (15) The error threshold is set to δ2. In this embodiment, the threshold value is set based on the accuracy of the collected data and practical experience. The value is further calculated and compared with the set error threshold to obtain the interharmonic criterion variable. A The specific judgment process is as follows: (16) S400: Processes and analyzes the time-domain data of any complete single cycle. Based on the half-wave symmetry characteristic of odd harmonics, which even harmonics lack, the even harmonic criterion variable is obtained by comparing the consistency between the shifted first half-cycle data and the inverted second half-cycle data. Figure 4 As shown.
[0042] Let the time-domain signal of a complete single cycle be... d ( t The translated first half-cycle data d1 and the inverted second half-cycle data d2 are obtained as follows: (17) (18) Calculate half-wave symmetry S See the following formula: (19) Set the symmetry threshold as S th , half-wave symmetry S The even-order harmonic criterion variable is obtained by comparing it with the set symmetrical threshold. B .
[0043] (20) S500: The obtained interharmonic and even harmonic criteria variables are classified into types. Combining the typical characteristics of different harmonic types in half-wave symmetry and frequency composition, the signal harmonic distortion type is comprehensively identified through the correspondence of variable value combinations.
[0044] Interharmonic criterion variables A Even harmonic criterion variables B The numerical correspondence between the two can be used to identify the type of harmonic distortion of the signal, as shown below.
[0045] (twenty one) Example 2 A harmonic distortion type identification system based on symmetry features, comprising: The data acquisition module collects real-time data of the power signals monitored by the measuring equipment. It adopts a sliding window sampling strategy, which uses a complete cycle as the sampling window and slides half a cycle each time to achieve continuous acquisition and obtain power signal data containing rich harmonic information. The single-window index calculation module processes and calculates the data collected from each sampling window. By using a modified higher-order statistical method, it extracts harmonic components and then calculates the single-window harmonic energy variance index to obtain index results that reflect the harmonic changes within the period, providing a basis for harmonic analysis. The interharmonic criterion calculation module groups the calculated single-window harmonic energy variance index, performs matching analysis on the data through harmonic problem criteria, sets a centrally symmetric normalized mean square error threshold to determine whether it meets the requirements, and obtains the interharmonic criterion variables by setting the error threshold according to the interharmonic characteristics, thus achieving data lightweighting. The even harmonic criterion calculation module processes and analyzes the time-domain data of any complete single cycle that has been collected. Based on the half-wave symmetry characteristic of odd harmonics, which even harmonics do not have, the even harmonic criterion variable is obtained by comparing the consistency between the first half-cycle data after translation and the second half-cycle data after inversion. The distortion type comprehensive identification module classifies the obtained interharmonic and even harmonic criteria variables into types. Combining the typical performance characteristics of different harmonic types in half-wave symmetry and frequency composition, it comprehensively identifies the harmonic distortion type of the signal through the correspondence of variable value combinations.
[0046] Example 3 This embodiment provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, it implements... Figure 1 The steps shown are in the harmonic distortion type identification method based on time-domain statistics and symmetry characteristics.
[0047] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of one or more computer-usable storage media (including, but not limited to, disk storage, etc.) containing computer-usable program code. CD - ROM It takes the form of a computer program product implemented on (such as optical memory, etc.).
[0048] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.
[0049] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.
[0050] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.
[0051] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made by those skilled in the art without creative effort within the spirit and principles of the present invention should be included within the scope of protection of the present invention.
Claims
1. A method for identifying harmonic distortion types based on symmetry characteristics, characterized in that, Includes the following steps: Power signal data is acquired by sliding the preset sampling window; The data from each sampling window are processed to extract harmonic components and calculate the single-window harmonic energy variance index. The calculated single-window harmonic energy variance index is grouped and compared with the preset centrally symmetric normalized mean square error threshold to determine whether each group of data conforms to the centrally symmetric distribution. Then, it is compared with the interharmonic characteristic error threshold to obtain the interharmonic criterion variable. The time-domain data of any complete single cycle is processed, calculated and analyzed. By comparing the consistency between the shifted first half-cycle data and the inverted second half-cycle data, the even harmonic criterion variable is obtained. Based on the interharmonic and even harmonic criterion variables, the type of signal harmonic distortion is identified.
2. The harmonic distortion type identification method based on symmetry characteristics as described in claim 1, characterized in that, The process of acquiring power signal data by sliding the sampling window according to the preset sampling window includes: real-time data acquisition of the power signal monitored by the measuring equipment, and continuous acquisition by using a sliding window sampling strategy, with a complete cycle as the sampling window and sliding half a cycle each time, to acquire power signal data containing harmonic components.
3. The harmonic distortion type identification method based on symmetry characteristics as described in claim 1, characterized in that, The process of processing the data of each sampling window, extracting harmonic components, and calculating the single-window harmonic energy variance index includes: expressing the data of each sampling window as a time-domain waveform signal, and calculating the single-window harmonic energy variance index according to the definition of variance in higher-order statistics.
4. The harmonic distortion type identification method based on symmetry characteristics as described in claim 3, characterized in that, The single-window harmonic energy variance index is: ； In the formula, This is a method for calculating the single-window harmonic energy variance index. The moment when the waveform signal is generated. For the first The start time of each sampling window; This indicates that the length of each sampling window is... , It is a time-domain waveform signal.
5. The harmonic distortion type identification method based on symmetry characteristics as described in claim 1, characterized in that, The process of grouping the calculated single-window harmonic energy variance index and comparing it with the preset central symmetric normalized mean square error threshold to determine whether each group of data conforms to the central symmetric distribution includes: grouping the calculated single-window harmonic energy variance index according to the required frequency resolution requirements. Calculate the center-symmetric normalized mean square error for each group of data and compare it with the preset center-symmetric normalized mean square error threshold. If the center-symmetric normalized mean square error of a certain group of data is less than the preset center-symmetric normalized mean square error threshold, then the group of data is centrally symmetric.
6. The harmonic distortion type identification method based on symmetry characteristics as described in claim 1, characterized in that, The process of obtaining the interharmonic criterion variable by comparing it with the error threshold set by the interharmonic characteristics includes: if a set of data satisfies a centrally symmetric distribution, the average, maximum and minimum values of the set of data are calculated; if the ratio of the difference between the maximum and minimum values of the corresponding set to the average value is greater than the preset error threshold, then the interharmonic criterion variable is zero; otherwise, the interharmonic criterion variable is one.
7. The harmonic distortion type identification method based on symmetry characteristics as described in claim 1, characterized in that, The process of processing and analyzing the time-domain data of any complete single cycle, and obtaining the even harmonic criterion variable by comparing the consistency between the shifted first half-cycle data and the inverted second half-cycle data, includes: calculating the shifted first half-cycle data and the inverted second half-cycle data based on the time-domain data of any complete single cycle; calculating the half-wave symmetry based on the shifted first half-cycle data and the inverted second half-cycle data; comparing the half-wave symmetry with the set symmetry threshold; if the half-wave symmetry is less than the set symmetry threshold, the even harmonic criterion variable is one; otherwise, it is zero.
8. The harmonic distortion type identification method based on symmetry characteristics as described in claim 1, characterized in that it comprehensively... The process of identifying the type of harmonic distortion of a signal based on the interharmonic criterion variable and the even harmonic criterion variable includes: if the interharmonic criterion variable is one, then there is an interharmonic; if the interharmonic criterion variable is zero and the even harmonic criterion variable is one, then there are both odd and even harmonics; otherwise, only odd harmonics exist.
9. A harmonic distortion type identification system based on symmetry characteristics, characterized in that, include: The data acquisition module is configured to acquire power signal data by sliding along a preset sampling window; The single-window index calculation module is configured to process the data acquired in each sampling window, extract harmonic components, and calculate the single-window harmonic energy variance index. The interharmonic criterion calculation module is configured to group the calculated single-window harmonic energy variance index, and compare it with the preset central symmetric normalized mean square error threshold to determine whether each group of data conforms to the central symmetric distribution. Then, it is compared with the interharmonic characteristic error threshold to obtain the interharmonic criterion variable. The even harmonic criterion calculation module is configured to process and analyze the time-domain data of any complete single cycle. By comparing the consistency between the shifted first half-cycle data and the inverted second half-cycle data, the even harmonic criterion variables are obtained. The distortion type comprehensive identification module is configured to identify the signal harmonic distortion type by comprehensively considering the interharmonic criterion variable and the even harmonic criterion variable.
10. An electronic device, characterized in that, It includes a memory and a processor, as well as computer instructions stored in the memory and running on the processor, which, when executed by the processor, perform the steps of the method according to any one of claims 1-8.