A method and system for testing a power quality monitoring device

Abstract

The present application relates to the technical field of power quality monitoring, and more particularly to a test method and system for a power quality monitoring device. The content includes: according to a test task, collecting response data of the power quality monitoring device, and preprocessing the response data to obtain preprocessed response data; performing preliminary feature extraction on the preprocessed response data to obtain preliminary feature data; introducing a recursive frequency domain feature enhancement algorithm to perform feature enhancement processing on the preliminary feature data to obtain enhanced feature data; introducing a multi-segment spectral residual evaluation algorithm to divide the enhanced feature data into frequency bands in the frequency domain, calculate the frequency band residuals, and obtain an overall residual index; performing grade determination based on the overall residual index and generating a standardized test report. The technical problems of low accuracy and poor adaptability of the traditional test method in the test of the power quality monitoring device are solved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of power quality monitoring, and in particular to a test method and system for a power quality monitoring device. BACKGROUND

[0002] With the continuous advancement of smart grid construction, power quality monitoring devices, as an important means to ensure the stable operation of the power grid and evaluate the quality of power transmission, have been widely deployed in substations, distribution terminals and industrial user sides. Such devices are used to monitor and record various power quality index data such as voltage sag, voltage fluctuation, harmonic content, flicker, frequency drift, etc. in real time, thereby providing decision-making basis for the dispatching system. However, before the device is put into operation, how to effectively verify its monitoring capability and technical performance has always been a technical problem faced by the industry. At present, there is a lack of a complete, systematic and repeatable test platform to functionally verify and performance evaluate the power quality monitoring device, and the test process mostly relies on manual debugging or static comparison based on partial voltage and current effective values, which cannot truly simulate the influence of various disturbance signals on the response capability of the device, especially in transient processes such as harmonic response, frequency offset tracking, and transient sag and transient rise, traditional debugging methods cannot cover all the functions of the monitoring device.

[0003] In summary, the traditional test method in the test of the power quality monitoring device has the technical problems of low accuracy and poor adaptability. SUMMARY

[0004] The present application provides a test method and system for a power quality monitoring device to solve the technical problems of low accuracy and poor adaptability of the traditional test method in the test of the power quality monitoring device.

[0005] The test method and system for a power quality monitoring device of the present application specifically include the following technical solutions:

[0006] A test method for a power quality monitoring device, comprising the following steps:

[0007] S1. According to the test task, the response data of the power quality monitoring device is collected, and the response data is preprocessed to obtain preprocessed response data; the preprocessed response data is preliminarily feature extracted to obtain preliminary feature data; a recursive frequency domain feature enhancement algorithm is introduced to perform feature enhancement processing on the preliminary feature data to obtain enhanced feature data;

[0008] S2. A multi-segment spectral residual evaluation algorithm is introduced to divide the enhanced feature data into frequency bands in the frequency domain, calculate the frequency band residual, and obtain the overall residual index; based on the overall residual index, the grade is determined, and a standardized test report is generated.

[0009] Preferably, the S1 specifically includes:

[0010] The recursive frequency domain feature enhancement algorithm combines recursive optimization and multi-scale frequency domain transformation to perform frequency domain coupling enhancement on the preliminary feature data.

[0011] Preferably, the S1 specifically includes:

[0012] In the implementation process of the recursive frequency domain feature enhancement algorithm, the preliminary feature data is converted from the time domain to the frequency domain to obtain an initial frequency spectrum vector; a parameterized orthogonal enhancement factor is introduced to perform dynamic weighting on the frequency spectrum data in the frequency spectrum vector and then perform inverse transformation back to the feature domain to obtain iteratively updated feature data; and all the iteratively updated feature data is averaged and fused to obtain enhanced feature data.

[0013] Preferably, the S1 specifically includes:

[0014] Based on the frequency spectrum data in the frequency spectrum vector, a frequency band average gradient is calculated, and a periodic disturbance enhancement factor is introduced to obtain the parameterized orthogonal enhancement factor.

[0015] Preferably, the S2 specifically includes:

[0016] In the implementation process of the multi-segment frequency spectrum residual error evaluation algorithm, the enhanced feature data is converted from the time domain to the frequency domain to obtain an enhanced frequency spectrum vector, and the enhanced frequency spectrum vector is divided into frequency bands in the frequency domain to construct a frequency band set.

[0017] Preferably, the S2 specifically includes:

[0018] In each frequency band, based on the enhanced frequency spectrum vector, a standard frequency spectrum reference vector is combined, and a logarithmic modulation factor and a periodic disturbance modulation factor are introduced to perform residual error calculation to obtain a frequency band residual error.

[0019] Preferably, the S2 specifically includes:

[0020] A function importance weight is introduced to weight and fuse all the frequency band residual errors to generate an overall residual error index; based on the overall residual error index, a preset grade threshold is combined to perform grade determination to obtain a grade determination result, and a standardized test report is generated.

[0021] A test system of a power quality monitoring device includes the following parts:

[0022] A configuration file analysis module, a channel matching module, a test task instantiation module, a data acquisition module, a test result evaluation module, and a report generation module;

[0023] A configuration file parsing module acquires a configuration file of the power quality monitoring device and performs parsing processing to obtain power quality index data; the power quality index data is transmitted to a channel matching module and a test task instantiation module;

[0024] The channel matching module selects a channel matched with the monitoring device based on the power quality index data by using a topological structure matching mechanism according to a channel mapping rule library, so as to realize the connection between the channel and the monitoring device.

[0025] The test task instantiation module selects and instantiates a test function module from a test module library according to a resource minimization strategy based on the power quality index data in combination with the current functional activation state of the power quality monitoring device, the signal channel resource situation and the test priority after completing channel matching, so as to obtain a test task; the test task is transmitted to a data acquisition module.

[0026] The data acquisition module acquires response data of the power quality monitoring device matched through the channel according to the test task obtained by the test task instantiation module, and pre-processes the response data to obtain pre-processed response data; the pre-processed response data is transmitted to a test result evaluation module.

[0027] The test result evaluation module performs preliminary feature extraction on the pre-processed response data to obtain preliminary feature data; a recursive frequency domain feature enhancement algorithm is introduced to perform feature enhancement processing on the preliminary feature data to obtain enhanced feature data; a multi-segment spectral residual evaluation algorithm is introduced to perform frequency band division on the enhanced feature data, calculate the frequency band residual, and obtain an overall residual index; grade determination is performed based on the overall residual index to obtain a grade determination result; the test result evaluation module is connected with a report generation module.

[0028] The report generation module generates a standardized test report based on the grade determination result in combination with the frequency band residual, the physical name of the corresponding frequency band, the abnormal frequency point and the overall residual index.

[0029] The technical scheme of the present application has the following advantages:

[0030] 1. A full-process closed-loop automatic test system is established from configuration analysis, channel matching, test module loading, signal injection, data acquisition, feature enhancement, intelligent evaluation to standardized report output, which effectively solves the problem that the existing power quality monitoring device lacks functional verification and quantitative evaluation means in the commissioning and debugging stage, and ensures the reliability and verifiability of the detection result of the power quality monitoring device.

[0031] 2. A recursive frequency domain feature enhancement algorithm is introduced to improve feature expressiveness. Through multiple rounds of spectrum reconstruction and orthogonal enhancement, the expressive power of features such as latent, small frequency perturbations, and non-stationary fluctuations in the frequency domain is effectively enhanced, providing more fine-grained and robust input features for subsequent evaluation.

[0032] 3. Construct a multi-segment spectrum residual evaluation algorithm to refine the error analysis dimensions. By dividing the spectrum data into multiple physically meaningful frequency bands (fundamental, low harmonic, high harmonic, etc.), residual evaluation is performed independently in each frequency band. This effectively identifies high-order disturbances, boundary frequency anomalies, intermittent response distortions, etc., achieving a more refined and sensitive evaluation effect than overall error calculation. Attached Figure Description

[0033] Figure 1 This is a structural diagram of a test system for a power quality monitoring device according to the present invention;

[0034] Figure 2 This is a flowchart of a testing method for a power quality monitoring device according to the present invention. Detailed Implementation

[0035] To further illustrate the technical means and effects adopted by the present invention to achieve its intended purpose, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0036] Unless otherwise defined, 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.

[0037] The following description, in conjunction with the accompanying drawings, details the specific scheme of the testing method and system for the power quality monitoring device provided by the present invention.

[0038] See attached document Figure 1 The diagram illustrates a test system structure of a power quality monitoring device according to an embodiment of the present invention. The system includes the following components:

[0039] The module includes a configuration file parsing module, a channel matching module, a test task instantiation module, a data acquisition module, a test result evaluation module, and a report generation module.

[0040] A configuration file parsing module acquires the configuration file of the power quality monitoring device and uses existing parsing techniques to perform parsing processing to obtain power quality index data; the power quality index data is transmitted to the channel matching module and the test task instantiation module;

[0041] The configuration file of the power quality monitoring device contains key parameters such as power quality item types supported by the device under test (i.e., the power quality monitoring device), channel definitions, parameter ranges, sampling accuracies, sampling frequencies, data resolutions, and measurement standards;

[0042] The parsing techniques are, for example, XML parsing engines based on DOM (Document Object Model) or SAX (Simple API for XML), or JSON parsing libraries based on key-value pair structures (such as RapidJSON or Jackson);

[0043] The power quality index data is, for example, harmonics, interharmonics, voltage fluctuations, flicker, frequency deviation, etc.

[0044] A channel matching module selects channels that match the power quality monitoring device based on the power quality index data using a topological structure matching mechanism according to an existing channel mapping rule library, i.e., the channel requirements of the power quality monitoring device are constructed into a requirement graph, and all available channels, signal sources, and interface connections in the test system are constructed into a physical connection graph, then the requirement graph and the physical connection graph are compared using a graph matching algorithm (such as maximum matching or subgraph isomorphism) to find a set of channel mapping relationships that meet the requirements of the power quality index data, have the shortest signal path, and have the smallest resource conflicts, which are used as the matching result to realize the connection between the channels and the monitoring device; the channel matching module is connected to the test task instantiation module;

[0045] The channel mapping rule library includes channel type definitions, physical interface parameters, and device function channel mapping tables, etc. The channel type definitions include signal type classifications such as analog quantities (voltage, current), digital quantities, and control quantities. The physical interface parameters are, for example, wiring terminal numbers, channel rated voltages / currents, impedances, channel directions (input / output), shielding levels, etc. The device function channel mapping table specifies the signal input channel requirements corresponding to each power quality index data (such as harmonics and frequency deviation).

[0046] A test task instantiation module selects and instantiates corresponding test function modules from an existing test module library according to a resource minimization strategy based on the power quality index data (or specific test requirements), in combination with the current functional activation state of the power quality monitoring device, the signal channel resource situation, and the test priority, to obtain a test task after completing channel matching; the test task is transmitted to the data acquisition module;

[0047] The instantiation behavior includes loading module control logic, configuring injection signal parameters, establishing a module execution queue, and ensuring that the dependency relationship and resources among modules do not conflict, such as when the device supports 2~13 harmonic detection and the frequency offset monitoring function is in an active state, i.e., the harmonic signal generation unit and the frequency offset control unit are instantiated, and unnecessary units such as the sag swell unit are skipped to reduce the test system load and test time length;

[0048] The data acquisition module acquires the response data of the power quality monitoring device matched through the channel according to the test task instantiated by the module, and pre-processes the response data, such as denoising, sampling alignment, standardization, and normalization, to obtain pre-processed response data; and transmits the pre-processed response data to the test result evaluation module;

[0049] The test result evaluation module performs preliminary feature extraction on the pre-processed response data to obtain preliminary feature data; introduces a recursive frequency domain feature enhancement algorithm to perform feature enhancement processing on the preliminary feature data to obtain enhanced feature data; introduces a multi-segment spectral residual evaluation algorithm to divide the enhanced feature data into frequency bands, calculate the frequency band residual, and obtain an overall residual index; performs grade determination based on the overall residual index to obtain a grade determination result; and connects the test result evaluation module with the report generation module;

[0050] The report generation module generates a standardized test report based on the grade determination result, in combination with the frequency band residual, the physical name of the corresponding frequency band (such as low-order harmonic), the abnormal frequency point, the overall residual index, and the like, to realize the testing of the power quality monitoring device.

[0051] Referring to the accompanying drawings Figure 2 which shows a flow chart of a power quality monitoring device testing method provided by an embodiment of the present application, the method comprising the following steps:

[0052] S1. According to the test task, the response data of the power quality monitoring device is acquired, and the response data is pre-processed to obtain pre-processed response data; the pre-processed response data is preliminarily feature-extracted to obtain preliminary feature data; and a recursive frequency domain feature enhancement algorithm is introduced to perform feature enhancement processing on the preliminary feature data to obtain enhanced feature data;

[0053] According to the test task, response data from the power quality monitoring device is collected through the configured channels. The response data may include three-phase voltage, instantaneous current value, harmonic content, total harmonic distortion (THD), flicker (short-term flicker severity Pst, long-term flicker severity Plt), frequency offset curve, sag time-domain waveform, etc. The collected response data is preprocessed to obtain preprocessed response data. The preprocessing process, such as noise reduction, sampling alignment, standardization, and normalization, all employ techniques well-known to those skilled in the art and will not be elaborated here.

[0054] Further utilize existing feature engineering techniques such as statistical analysis to perform preliminary feature extraction on the preprocessed response data to obtain preliminary feature data, such as peak value, voltage slope, frequency drift rate, RMS change rate, phase angle change, power spectral density, etc.

[0055] Furthermore, a recursive frequency domain feature enhancement algorithm is introduced to enhance the initial feature data. By combining recursive optimization and multi-scale frequency domain transformation, frequency domain coupling enhancement is performed on the initial feature data to improve the ability to detect latent anomalies. The recursive frequency domain feature enhancement algorithm introduces a parameterized orthogonal enhancement factor and performs adaptive spectrum reconstruction on the initial feature data through a multi-level recursive form to obtain the enhanced feature data. The specific implementation process is as follows:

[0056] Preliminary feature data Perform a Discrete Fourier Transform (DFT) to obtain the initial spectrum vector. ,in, These are the feature dimensions of the preliminary feature data. This is the preliminary feature data. The value of each feature, It is the total number of frequency points. ,in It is the sampling frequency (e.g., 3200Hz, 6400Hz, etc., determined according to the configuration of the power quality monitoring device under test); It is the frequency domain resolution (e.g., 5Hz, 10Hz, determined based on standards such as IEC61000-4-7 requirements). It is the first in the initial spectrum vector The spectral data at each frequency point is used as the initial data for iteration. The parameterized orthogonal enhancement factor is dynamically adjusted during recursive iteration to perform enhancement transformation on the spectral data. Then, the data is inversely transformed back to the time domain using the inverse discrete Fourier transform to reconstruct the feature data, obtaining the iteratively updated feature data. Finally, after completing a total of [number] recursive enhancement rounds... Then, the feature data after all iterations and updates are averaged and fused to obtain the enhanced feature data; the discrete Fourier transform and its inverse transform are techniques well known to those skilled in the art and will not be described in detail here; the total number of recursive enhancement rounds... The value is obtained by methods such as gradient descent or energy focusing, and the range is 10-20. The gradient descent and energy focusing methods are well known to those skilled in the art and will not be described in detail here.

[0057] The specific calculation formula for the parameterized orthogonal enhancement factor is constructed based on the spectral adaptive enhancement theory and combined with the self-recursive feature enhancement technique. The specific formula is expressed as follows:

[0058] ,

[0059] in, It is the first In the nth iteration, the 1st Parameterized orthogonal enhancement factor for each spectral data; This is the main gain coefficient, used to control the baseline proportion of the parameterized orthogonal enhancement factor amplitude. It is set using a normalized model based on the spectral amplitude variance, and the reference value range is [range missing]. These are technical means well known to those skilled in the art, and will not be elaborated upon here; It is the first In the nth iteration, the 1st One spectrum data; It is a nonlinear enhancement power exponent used to stretch or compress the relative weights of large and small frequency amplitudes. The value is determined based on the skewness of the spectral distribution, with a reference range of values. ; This is a frequency stability adjustment coefficient used to constrain the denominator in the parameterized orthogonal enhancement factor. It is determined by the ratio of the average slope to the maximum slope of the statistical spectrum, with a reference range of values. ; It is the first The average gradient of the frequency band under each iteration is used to reflect the slope of the current frequency domain change. , It is the first In the nth iteration, the 1st One spectrum data; It is a periodic disturbance enhancement factor used to introduce a periodic fluctuation disturbance response sensitivity. The value is determined based on the specific application scenario, with a reference range of values. ; It is the total number of recursive enhancement rounds (iterations); Used to enhance the weights of periodic features during iteration, and to simulate sensitivity to medium-frequency perturbations; To achieve the current spectral amplitude nonlinear stretching enhancement, and adaptive suppression of the enhancement amplitude according to the overall smoothness of the spectrum data;

[0060] In the first iteration, the spectrum data obtained in the first iteration is enhanced and transformed to obtain the spectrum data of the second iteration ; , and an inverse discrete Fourier transform is performed to obtain the result of the second iteration, i.e., the updated feature data of the second iteration;

[0061] Finally, the updated feature data of all iterations is averaged and fused to obtain the enhanced feature data .

[0062] S2. A multi-segment spectrum residual evaluation algorithm is introduced to divide the enhanced feature data into frequency segments in the frequency domain, calculate the frequency segment residuals, and obtain an overall residual index. Based on the overall residual index, a level is determined, and a standardized test report is generated.

[0063] In order to realize accurate analysis of the power quality monitoring device, a multi-segment spectrum residual evaluation algorithm is introduced based on the enhanced feature data . The core technical idea of the multi-segment spectrum residual evaluation algorithm is to divide the enhanced feature data into multiple segments in the frequency domain, extract the residual information and spectrum pattern difference in each frequency segment, and form an overall residual index through weighted fusion, which is used to represent the overall response quality of the power quality monitoring device. Compared with the traditional overall spectrum error calculation method, the multi-segment spectrum residual evaluation algorithm has higher abnormal recognition ability and evaluation accuracy in the edge interval of the spectrum (such as the high-order harmonic segment, the frequency drift boundary, and the fluctuation transition segment). The specific implementation process is as follows:

[0064] Discrete Fourier transform is performed on the enhanced feature data to convert the enhanced feature data from the time domain to the frequency domain to obtain the enhanced spectrum vector , where is the total number of frequency points;

[0065] Further, the enhanced spectrum vector is divided into segments with clear physical meaning according to the functional configuration of the measured object and industry standards (such as GB / T 14549 and IEC 61000-4-7), such as: fundamental wave segment (0-50 Hz), low-order harmonic segment (50-250 Hz), middle-order harmonic segment (250-1000 Hz), high-frequency harmonic segment (1000-3000 Hz), and inter-harmonic segment / fluctuation disturbance segment (non-integer frequency segment);

[0066] After division, a set of frequency bands is obtained. :

[0067] ,

[0068] in, , is the A subset of frequency bands, representing a frequency range Spectrum data within, They represent the first The frequency index corresponding to the start and end frequencies of each frequency band indicates the boundary position of a certain frequency band in the enhanced spectrum vector;

[0069] Within each frequency band, based on the frequency band division mechanism, nonlinear enhancement function, and periodic perturbation modulation mechanism, the enhanced spectrum vector is... With standard spectral reference vector Residual calculations are performed; the standard spectrum reference vector is extracted from the national power quality standard database; the national power quality standard database contains power quality technical specifications covered and specified by multiple current national standards, which will not be elaborated here;

[0070] The specific formula for calculating the residual for each frequency band is as follows:

[0071] ,

[0072] in, It is the first The residual of the i-th frequency band represents the i-th frequency band. The weighted average of the overall response error within each frequency band; It is the first The spectral length of a frequency band, that is, the number of discrete frequency points contained in the frequency band interval; Indicates the first The first standard spectral reference vector of the i-th frequency band One element; Indicates the first The first frequency band One spectrum data; This is the residual amplification power factor, used to control the sensitivity of error amplification. A larger residual amplification power factor indicates greater sensitivity to large errors. It is determined using methods such as least squares generalization, and a reference range is [range to be specified]. These are technical means well known to those skilled in the art, and will not be elaborated upon here; Indicates the first The first frequency band The fluctuation tolerance at each frequency point is used to suppress error explosion and is derived from the national power quality standard tolerance. The reference value range is as follows: ; It is the residual stability constant, used to prevent division-to-zero anomalies; is a logarithmic modulation factor, used to buffer high amplitude errors and emphasize the importance of small and medium amplitude errors; is a periodic disturbance modulation factor, used to simulate the response deviation caused by periodic disturbance to enhance sensitivity;

[0073] Residual error of each frequency band represents the average normalized nonlinear error under the th frequency band, and the residual errors of all frequency bands are weighted and fused according to the functional importance weight to generate an overall residual index for representing the overall response quality of the device; the functional importance weight is obtained by statistical learning based on historical test data, which is a well-known technical means for those skilled in the art and will not be described here; the specific calculation formula of the overall residual index is:

[0074]

[0075] is the functional importance weight of the th frequency band, and the value range is and the sum is 1, such as: represents the fundamental band, which changes little; represents the low-order harmonic band, which is important; represents the medium-order band, which is prone to abnormalities; represents the high-frequency harmonic band, which is used for device distortion detection; represents the inter-harmonic band, which is used for monitoring fine interference;

[0076] The overall residual index is a comprehensive residual score integrated with the error information of all frequency bands, reflecting the comprehensive performance of the power quality monitoring device under multiple power quality indicators. According to the overall residual index, the automatic grade determination is performed according to the grade threshold and set according to the existing clustering division method based on statistical analysis, and the grade determination result is obtained:

[0077]

[0078] Further, the residual value of each frequency band, the physical name of the corresponding frequency band (such as low-order harmonic), abnormal frequency point, overall residual index and grade determination result are integrated into the standardized test report, and the test of the power quality monitoring device is realized.

[0079] In summary, a test method and system for a power quality monitoring device are completed.

[0080] ​​​The progressive nature of the specification and examples does not constitute a requirement that all embodiments include all of the described elements nor that the disclosed steps be performed in the order presented. In some embodiments, a step can be performed in a different order or omitted.

[0081] Each of the embodiments in the specification is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other. Each embodiment focuses on the difference from other embodiments.

[0082] The above embodiments are only used to illustrate the technical solutions of the present application, but not limit the present application. Although the present application has been described in detail with reference to the foregoing embodiments, it should be understood by those skilled in the art that the technical solutions recorded in the foregoing embodiments can be modified, or some technical features can be replaced by equivalent ones. The modification or replacement does not change the essence of the corresponding technical solutions, and should be included in the protection scope of the present application.

Claims

1. A method of testing a power quality monitoring device, characterized by, The method comprises the following steps: S1. According to the test task, collect the response data of the power quality monitoring device, and preprocess the response data to obtain preprocessed response data; Preliminary feature extraction is performed on the preprocessed response data to obtain preliminary feature data; a recursive frequency domain feature enhancement algorithm is introduced to perform feature enhancement processing on the preliminary feature data, and the specific implementation process is as follows: the preliminary feature data is converted from the time domain to the frequency domain to obtain an initial frequency spectrum vector; based on the frequency spectrum data in the frequency spectrum vector, the frequency band average gradient is calculated, and a periodic disturbance enhancement factor is introduced to obtain a parameterized orthogonal enhancement factor; based on the parameterized orthogonal enhancement factor, the frequency spectrum data in the frequency spectrum vector is dynamically weighted and inverse transformed back to the feature domain to obtain iteratively updated feature data; all iteratively updated feature data is averaged and fused to obtain enhanced feature data; and the specific calculation formula of the parameterized orthogonal enhancement factor is: ， in, It is the first In the nth iteration, the 1st Parameterized orthogonal enhancement factor for each spectral data; It is the main gain coefficient; It is the first In the nth iteration, the 1st One spectrum data; It is a non-linear enhanced power exponent; It is the frequency stability adjustment coefficient; It is the first Average gradient of frequency band under the next iteration; It is a periodic disturbance enhancement factor; It is the total number of recursive enhancement rounds; S2. A multi-segment frequency spectrum residual evaluation algorithm is introduced to divide the enhanced feature data into frequency bands in the frequency domain, calculate the frequency band residuals, and obtain an overall residual index; based on the overall residual index, a level is determined, and a standardized test report is generated.

2. The method of claim 1, wherein, The S1 specifically comprises: The recursive frequency domain feature enhancement algorithm combines recursive optimization and multi-scale frequency domain transformation to perform frequency domain coupling enhancement on the preliminary feature data.

3. The method of claim 1, wherein the power quality monitoring device is a power quality monitor. The S2 specifically comprises: In the implementation process of the multi-segment frequency spectrum residual evaluation algorithm, the enhanced feature data is converted from the time domain to the frequency domain to obtain an enhanced frequency spectrum vector, and the enhanced frequency spectrum vector is divided into frequency bands in the frequency domain to construct a frequency band set.

4. The method of testing a power quality monitoring device of claim 3, wherein, The S2 specifically comprises: In each frequency band, based on the enhanced frequency spectrum vector, a standard frequency spectrum reference vector is combined, and a logarithmic modulation factor and a periodic disturbance modulation factor are introduced to calculate the residual to obtain the frequency band residual.

5. A method of testing a power quality monitoring device according to claim 4, wherein, The S2 specifically comprises: A functional importance weight is introduced to weight and fuse all frequency band residuals to generate an overall residual index; based on the overall residual index, a level threshold is determined based on the overall residual index, a level determination result is obtained, and a standardized test report is generated.

6. A test system of power quality monitoring device, applied to the test method of the power quality monitoring device according to claim 1, characterized in that, The method comprises the following parts: A configuration file analysis module, a channel matching module, a test task instantiation module, a data acquisition module, a test result evaluation module, and a report generation module; The configuration file analysis module obtains the configuration file of the power quality monitoring device and performs analysis processing to obtain power quality index data; the power quality index data is transmitted to the channel matching module and the test task instantiation module; The channel matching module selects a channel matched with the monitoring device based on the power quality index data by using a topological structure matching mechanism according to a channel mapping rule library, so as to realize the connection between the channel and the monitoring device; The test task instantiation module selects and instantiates a test function module from a test module library according to a resource minimization strategy based on the power quality index data in combination with the current functional activation state of the power quality monitoring device, the signal channel resource situation, and the test priority after completing channel matching, to obtain a test task; The test task is transmitted to the data acquisition module; A data acquisition module acquires response data of the power quality monitoring device matched through the channel according to the test task obtained by the test task instantiation module, and pre-processes the response data to obtain pre-processed response data; The pre-processed response data is transmitted to a test result evaluation module; The test result evaluation module performs preliminary feature extraction on the pre-processed response data to obtain preliminary feature data, introduces a recursive frequency domain feature enhancement algorithm to perform feature enhancement processing on the preliminary feature data to obtain enhanced feature data, introduces a multi-segment spectral residual evaluation algorithm to divide the enhanced feature data into frequency bands, calculate the frequency band residual, and obtain an overall residual index; grade determination is performed based on the overall residual index to obtain a grade determination result; The test result evaluation module is connected to a report generation module; The report generation module generates a standardized test report based on the grade determination result, combined with the frequency band residual, the physical name of the corresponding frequency band, the abnormal frequency point and the overall residual index.

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