A method and system for monitoring the load of an electric meter based on a power quality analyzer

Abstract

The present invention relates to the field of electrical detection technology, and specifically to a method and system for monitoring the load of an electric meter based on a power quality analyzer, including the following steps: extracting the three-phase voltage phase offset characteristics within the disturbance period, combining the frequency band change trend with the fundamental frequency power ratio, identifying the interference coupling section and analyzing the jump frequency points, and outputting the monitoring result of the electric meter load. In the present invention, by extracting the phase offset angles of the three-phase voltage within the voltage disturbance period and constructing a cross-phase synchronization index, the synchronous response characteristics of different loads to the disturbance can be accurately revealed, and the accuracy of load response attribution judgment can be enhanced. On the basis of cross-phase synchronization analysis, the corresponding relationship between the frequency band characteristics and the response attribution is constructed, so that the load response dissection has the ability to identify phase differences and the ability to calibrate frequency behavior, thereby improving the discrimination robustness under the background of multi-source interference.

Description

Technical Field

[0001] The present invention relates to the technical field of electrical detection, and particularly to a method and system for monitoring the load of an electric meter based on a power quality analyzer. Background Art

[0002] The technical field of electrical detection includes the monitoring of the operating states of various electrical devices and systems, performance evaluation, and safety diagnosis. The core content of this technical field is to identify abnormal device operation, system load changes, and potential power fault hazards through the measurement and analysis of key electrical parameters such as voltage, current, frequency, power factor, and power quality.

[0003] Among them, the method for monitoring the load of an electric meter based on a power quality analyzer refers to using a power quality analyzer to collect various electrical parameter data such as voltage, current, harmonics, and voltage deviation of the corresponding circuit of the measured electric meter, and combining the time-periodic characteristics and power characteristics of the electrical load to classify and identify the actual load conditions connected to the electric meter and monitor the changes.

[0004] The load monitoring methods based on traditional power quality analyzers mostly rely on the direct sampling of static electrical parameters such as voltage, current, and harmonics, lacking in-depth characterization of the dynamic response behavior within the disturbance period, and it is difficult to accurately judge the phase response differences of different loads under the action of disturbances. The lack of a quantitative analysis mechanism for the three-phase phase shift path leads to the problem of response attribution confusion in the case of mixed access of multiple loads or superposition of non-linear interferences, making it difficult to clearly analyze different load behaviors. During the load identification process, a single-band or full-band unified analysis strategy is mostly adopted, without differential processing in combination with the frequency-band behavior characteristics, which is easily masked by frequency disturbances and reduces the identification accuracy. The lack of an interference identification mechanism based on the linkage characteristics of periodic power and frequency-band amplitude results in problems such as inaccurate frequency point positioning and high response misjudgment rate in a strong interference background. For example, although the amplitude of some high-frequency harmonic disturbances is small, they are highly synchronized with the fundamental frequency power change. Without matching analysis, they are easily ignored, leading to blind spots in the monitoring of abnormal load behaviors. In addition, in the existing technology, clustering analysis is not carried out on the jump frequency points, making it difficult to identify representative frequency characteristic points and unable to be effectively used for subsequent frequency point governance and load model optimization. Summary of the Invention

[0005] The object of the present invention is to solve the drawbacks existing in the prior art, and to propose a method and system for monitoring the load of an electric meter based on a power quality analyzer.

[0006] To achieve the above object, the present invention adopts the following technical solution: A method for monitoring the load of an electric meter based on a power quality analyzer, comprising the following steps:

[0007] S1: Obtain the three-phase voltages of the electric meter through a power quality analyzer, extract the phase shift angles of the three-phase voltages during the disturbance period to judge the synchronization characteristics between phases, and form a cross-phase synchronization evaluation result;

[0008] S2: Based on the cross-phase synchronization evaluation result, identify the load response attribution types of the phase shift angles of the three-phase voltages, establish a load response dissection sample set, and obtain the frequency band characteristics corresponding to the three-phase voltages in the load response dissection sample set to establish a frequency band correspondence relationship;

[0009] S3: Refer to the frequency band correspondence relationship of the frequency band characteristics corresponding to the three-phase voltages in the load response dissection sample set, judge the change trend of the harmonic amplitude within the frequency band, and obtain the frequency band amplitude change data;

[0010] S4: Obtain the change trend of the fundamental frequency total power of the electric meter load monitored by the power quality analyzer during the disturbance period, compare the change trend of the harmonic amplitude at the corresponding moment in the frequency band amplitude change data with the change trend of the fundamental frequency total power, and identify the interference coupling fluctuation section;

[0011] S5: Extract the frequency points with prominent jump amplitudes and jump frequencies in the interference coupling fluctuation section, perform active frequency point analysis on the frequency points, and output the electric meter load monitoring result.

[0012] As a further solution of the present invention, the cross-phase synchronization evaluation result includes a phase shift direction feature, an inter-phase response consistency level, and a synchronous stability change condition. The load response dissection sample set includes a load combination structure label, a response mode classification result, and a record of phase synchronization performance. The frequency band amplitude change data is specifically a jump amplitude trajectory within the frequency band, a periodic stability characteristic index, and a frequency fluctuation distribution state. The interference coupling fluctuation section specifically refers to a harmonic disturbance superposition interval, a power change synchronization region, and a frequency band behavior resonance node. The electric meter load monitoring result includes a load type identification result, an interference frequency identification label, and load structure response information.

[0013] As a further solution of the present invention, the specific steps for obtaining the cross-phase synchronization evaluation result are as follows:

[0014] S111: Obtain the A, B, and C three-phase voltage waveform data recorded by the power quality analyzer within a specified time period, and extract the voltage phase shift angle change sequences in three cycles before, during, and after the disturbance;

[0015] S112: Based on the voltage phase shift angle change sequence, use the cosine similarity algorithm to calculate the cosine values of the angles between the three pairs of angle change vectors A-B, B-C, and A-C in the sequence respectively, and judge the inter-phase synchronization matching degree at each sampling point in the corresponding cycle according to the cosine value of the angle, and obtain the phase shift path similarity analysis result;

[0016] S113: Determine the average similarity change trend of the three pairs of angle change vectors, namely A - B, B - C, and A - C, in three consecutive cycle segments of the phase shift path similarity analysis result to form a cross-phase synchrony evaluation result.

[0017] As a further solution of the present invention, the step of obtaining the frequency band correspondence relationship is specifically as follows:

[0018] S211: Determine the inter-phase response mode of the load to the disturbance according to the three pairs of angle change vectors, namely A - B, B - C, and A - C, in the cross-phase synchrony evaluation result. If it shows a similar coincidence state in three consecutive cycles, it is classified as a concentrated load response sample; if there are phase - stage or amplitude response differences between different phases, it is classified as a parallel load response sample.

[0019] S212: Integrate the concentrated load response samples and the parallel load response samples, and label the samples with structure and time indexes to form a load response dissection sample set.

[0020] S213: By calling the phase calibration interface and frequency component measurement function of the power quality analyzer, obtain the frequency band characteristics corresponding to the three - phase voltage in the load response dissection sample set, and establish the frequency band correspondence relationship between the three - phase voltage and the frequency band characteristics.

[0021] As a further solution of the present invention, the step of obtaining the frequency band amplitude change data is specifically as follows:

[0022] S311: Obtain the harmonic voltage amplitudes in the 2nd, 3rd, and 6th frequency bands recorded in the power quality analyzer, and extract the harmonic amplitude sequences whose frequency band information is consistent with that marked in the load response dissection sample set and the frequency band correspondence relationship.

[0023] S312: Extract the amplitude change paths of each cycle before, during, and after the disturbance in the harmonic amplitude sequences with consistent frequency band information, analyze the number of jumps and amplitude fluctuation characteristics, and statistically analyze the change trend under consecutive cycles to output the frequency band amplitude change data.

[0024] As a further solution of the present invention, the step of obtaining the interference coupling fluctuation section is specifically as follows:

[0025] S411: Obtain the change trend of the fundamental frequency total power of the meter load monitored by the power quality analyzer during the disturbance cycle, establish power sequence data according to the cycle to obtain the fundamental frequency total power change sequence.

[0026] S412: Extract the harmonic amplitude change trend at the corresponding moment according to the frequency band amplitude change data, and perform cycle - corresponding pairing with the fundamental frequency total power change sequence to generate an amplitude - power ratio sequence.

[0027] S413: Identify the period segments in the amplitude-power ratio sequence where the jump trends of the two types of trends are consistent within the disturbance period through the cumulative sum test algorithm, and determine the interference coupling fluctuation segments with synchronous fluctuations.

[0028] As a further solution of the present invention, the steps for obtaining the electricity meter load monitoring result are specifically as follows:

[0029] S511: Extract the frequency points with jumps in each period within the interference coupling fluctuation segment, count the corresponding jump amplitudes and jump frequencies, and obtain the jump frequency point index set;

[0030] S512: According to the jump frequency point index set, screen the set of frequency points with prominent jump amplitudes and frequencies, and construct a candidate active frequency point sequence;

[0031] S513: Use the density clustering algorithm to perform clustering analysis on the candidate active frequency point sequence, identify the activation frequency points exceeding the preset frequency threshold, and output the electricity meter load monitoring result for amplitude limiting processing of interference frequency points and load identification optimization.

[0032] An electricity meter load monitoring system based on a power quality analyzer, the electricity meter load monitoring system based on a power quality analyzer is used to execute the above-mentioned electricity meter load monitoring method based on a power quality analyzer, and the system includes:

[0033] The cross-phase synchronization identification module obtains the three-phase voltages of the electricity meter through the power quality analyzer, extracts the phase shift angles of the three-phase voltages within the disturbance period to judge the synchronization characteristics between phases, and forms a cross-phase synchronization evaluation result;

[0034] The load response attribution module, based on the cross-phase synchronization evaluation result, identifies the load response attribution types of the phase shift angles of the three-phase voltages, establishes a load response dissection sample set, and obtains the frequency band corresponding relationship of the three-phase voltages in the load response dissection sample set;

[0035] The frequency band amplitude analysis module refers to the frequency band corresponding relationship of the frequency band characteristics of the three-phase voltages in the load response dissection sample set, judges the change trend of the harmonic amplitudes within the frequency band, and obtains the frequency band amplitude change data;

[0036] The power trend comparison module obtains the change trend of the fundamental frequency total power of the electricity meter load monitored by the power quality analyzer within the disturbance period, compares the change trend of the harmonic amplitudes at the corresponding moments in the frequency band amplitude change data with the change trend of the fundamental frequency total power, and identifies the interference coupling fluctuation segment;

[0037] The active frequency point identification module extracts the frequency points with prominent jump amplitudes and jump frequencies in the interference coupling fluctuation segment, performs active frequency point analysis on the frequency points, and outputs the electricity meter load monitoring result.

[0038] Compared with the prior art, the advantages and positive effects of the present invention are:

[0039] In the present invention, by extracting the phase offset angle of the three-phase voltage within the voltage disturbance period and constructing a cross-phase synchronization index, the synchronous response characteristics of different loads to the disturbance can be accurately revealed, and the accuracy of the load response attribution judgment can be enhanced. On the basis of cross-phase synchronization analysis, the correspondence between the frequency band characteristics and the response attribution is constructed, so that the load response segmentation has the phase difference recognition ability and the frequency behavior calibration ability, thereby improving the robustness of the discrimination under the background of multi-source interference. Combining the microscopic disturbance characteristics such as the hopping frequency and amplitude with the periodic power change trend for multi-dimensional data comparison, it is possible to effectively explore the coupling relationship between the frequency band amplitude fluctuation and the load change, and improve the ability to identify key frequency points in complex disturbance events. Further mining the active feature points in the hopping frequency through density clustering helps to accurately distinguish the noise frequency points from the effective load frequency points, and provide a more targeted frequency basis for subsequent load identification and interference control. During the entire process, with the help of dynamic matching analysis of harmonic amplitude, phase offset and fundamental frequency power, not only can the monitoring sensitivity of abnormal load events be improved, but the load identification dimension can also be expanded to frequency band behavior and synchronization performance, achieving a deep perception of the load structure status and comprehensively enhancing the stability, accuracy and interpretability of the monitoring results. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] Figure 1 It is a schematic diagram of the workflow of the present invention;

[0041] Figure 2 This is a flow chart of step S1 of the present invention;

[0042] Figure 3 This is a flow chart of step S2 of the present invention;

[0043] Figure 4 This is a flow chart of step S3 of the present invention;

[0044] Figure 5 This is a flow chart of step S4 of the present invention;

[0045] Figure 6 This is a flow chart of step S5 of the present invention. DETAILED DESCRIPTION

[0046] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0047] In the description of the present invention, it should be understood that the orientation or positional relationship indicated by the terms "length", "width", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", etc. is based on the orientation or positional relationship shown in the drawings. It is only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be construed as a limitation on the present invention. In addition, in the description of the present invention, the meaning of "a plurality" is two or more, unless otherwise specifically defined.

[0048] Please refer to Figure 1 , the present invention provides a technical solution: a method for monitoring the load of an electric meter based on a power quality analyzer, including the following steps:

[0049] S1: Obtain the three-phase voltage of the electric meter through the power quality analyzer, extract the phase shift angles of the three-phase voltage during the disturbance period to judge the synchronization characteristics between phases, and form a cross-phase synchronization evaluation result;

[0050] S2: Based on the cross-phase synchronization evaluation result, identify the load response attribution types of the phase shift angles of the three-phase voltage, establish a load response dissection sample set, and obtain the frequency band characteristics corresponding to the three-phase voltage in the load response dissection sample set to establish a frequency band correspondence relationship;

[0051] S3: Refer to the frequency band correspondence relationship of the frequency band characteristics corresponding to the three-phase voltage in the load response dissection sample set, judge the change trend of the harmonic amplitude within the frequency band, and obtain the frequency band amplitude change data;

[0052] S4: Obtain the change trend of the fundamental frequency total power of the electric meter load monitored by the power quality analyzer during the disturbance period, compare the change trend of the harmonic amplitude at the corresponding moment in the frequency band amplitude change data with the change trend of the fundamental frequency total power, and identify the interference coupling fluctuation section;

[0053] S5: Extract the frequency points with prominent jump amplitudes and jump frequencies in the interference coupling fluctuation section, perform active frequency point analysis on the frequency points, and output the electric meter load monitoring result;

[0054] The cross-phase synchronization evaluation result includes the phase shift direction characteristics, the inter-phase response consistency level, and the change of synchronization stability. The load response dissection sample set includes the load combination structure label, the response mode classification result, and the record of phase synchronization performance. The frequency band amplitude change data is specifically the jump amplitude trajectory within the frequency band, the periodic stability characteristic index, and the frequency fluctuation distribution state. The interference coupling fluctuation section specifically refers to the harmonic disturbance superposition interval, the power change synchronization region, and the frequency band behavior resonance node. The electric meter load monitoring result includes the load type identification result, the interference frequency identification label, and the load structure response information.

[0055] Please refer to Figure 2 , and the steps for obtaining the cross-phase synchronization evaluation result are specifically as follows:

[0056] S111: Obtain the voltage waveform data of phases A, B, and C recorded by the power quality analyzer within a specified time period, and extract the voltage phase offset angle change sequence within three cycles before, during, and after the disturbance;

[0057] Obtain the voltage waveform data of phases A, B, and C recorded by the power quality analyzer within a specified time period. First, set the data sampling rate to 10 kHz, perform continuous data acquisition during the period from 09:32:00 to 09:32:02 on July 5, 2024. Independently measure the voltage channels of phases A, B, and C, record the sampled data and save it as a time series array. After obtaining the voltage data of each phase, extract the steady-state waveform data within 5 cycles before the disturbance through the whole-cycle truncation method, identify the first abnormal offset point as the disturbance starting point, and extract a waveform sequence with a period length before and after this point as the data before and after the disturbance. When extracting the data during the disturbance, locate it at the center of the disturbance occurrence time point, symmetrically extract a voltage waveform with a period length, perform a fast Fourier transform (FFT) on the data sequence within each cycle, convert the time-domain waveform to the frequency-domain complex form, and then extract the main frequency component phase angle of each phase voltage at the fundamental frequency in the complex form. Using the phase angle offset calculation method, assume the initial phase angle of phase A is , during the disturbance is , after the disturbance is , then the three-phase angle offset sequences are , phases B and C are processed in the same way to form three groups of time-angle sequence data. After obtaining the angle change sequences of each phase, they are organized into the following array representation. Phase A: , phase B: , phase C: .

[0058] S112: Based on the voltage phase offset angle change sequence, use the cosine similarity algorithm to calculate the cosine values of the included angles between the three pairs of angle change vectors of A-B, B-C, and A-C in the sequence respectively, and judge the inter-phase synchronization matching degree at each sampling point within the corresponding cycle according to the cosine values of the included angles to obtain the phase offset path similarity analysis result;

[0059] Based on the extracted phase offset angle change sequence, respectively construct the phase angle change vector differences between each pair of phases, defined as: A-B vector difference: , B-C vector difference: , A-C vector difference: , for the above three groups of angle difference vectors, calculate the cosine similarity pairwise, and the steps are as follows:

[0060] Take each pair of difference vectors as two three-dimensional vectors , , then the formula for calculating the cosine value of the included angle is:

[0061] ;

[0062] Among them, : represents the cosine value of the included angle between vectors u and v, reflecting the similarity degree of two angle change vectors; : represents the included angle between two three-dimensional vectors u and v, with the unit of radian (but in actual applications, the cosine value is directly analyzed); : represents the first set of angle change difference vectors, and the elements , , are respectively the A - B, B - C or A - C angle difference components in three sampling periods; : represents another set of angle change difference vectors corresponding to u, and the elements , , are another set of angle difference components in the same position period; : is the dot product of vectors u and v, which is essentially the sum of term-by-term products, representing the coincidence degree of the direction projections of the two vectors; : represents the Euclidean norm of vector u, reflecting the overall "strength" of the vector; : Similarly, it is the norm of vector v; The overall fractional structure: is the standard expression for calculating the cosine of the included angle between two vectors, and the result range is , where 1 means the directions are completely the same, 0 means perpendicular, and -1 means the directions are completely opposite.

[0063] For example, calculate the cosine value of the included angle between A - B and A - C: Let v = [0, 2, 1] Substitute into the calculation: Dot product: , Norm: , , Cosine value: . Similarly, calculate the cosine value of the included angle between B - C and A - B: Let ;

[0064] Dot product: ;

[0065] Norm: ;

[0066] Cosine value: .

[0067] The judgment principle is set as follows: If the cosine value , it is judged as "highly synchronous"; if the cosine value is between 0.7 and 0.95, it is judged as "moderately synchronous"; if the cosine value is lower than 0.7, it is judged as "asynchronous". This interval is set according to the voltage deviation tolerance of the symmetric structure. The relevant reference standard is that the critical angle of the phase angle synchronization deviation specified in IEC61000-4-30 is approximately equal to 18°, and the cosine value critical point after conversion is . The benefit of the formula is that by calculating the cosine angle between the angle difference vectors before and after the three-phase voltage disturbance, the quantitative judgment of the instantaneous phase synchronization degree between the three phases is realized.

[0068] S113: Judge the average similarity change trend of the three pairs of angle change vectors in the three consecutive cycle sections of A-B, B-C, and A-C in the analysis result of the phase shift path similarity, and form the cross-phase synchronization evaluation result;

[0069] Based on the cosine values of the angles between the three pairs of angle change vectors, taking three consecutive cycles as an analysis window, extract the cosine values within each cycle in sequence, and statistically calculate the average similarity between each pair. Suppose the cosine values of the three cycles of A-B are 0.988, 0.993, and 0.979, those of B-C are -0.951, -0.980, and -0.925, and those of A-C are 0.961, 0.974, and 0.953. The following steps are executed: First, calculate the average value of the three cycles of each pair: The average value of A-B: , the average value of B-C: (-0.951 - 0.980 - 0.925) / , the average value of A-C: . Perform trend judgment on the above results. Using the sliding window method, perform differential processing on the average value of each group within three cycles to judge whether it is in an increasing, decreasing, or stable state. The judgment criteria are as follows: If the difference between the latter value and the former value , it is judged as "stable"; if the latter value increases compared with the former value , it is judged as "increasing"; if the latter value decreases compared with the former value , it is judged as "decreasing". Record the differential results in sequence, and set that if "decreasing" appears twice within the three cycles of the judgment window, it is judged as "synchronization deterioration", if "increasing" appears twice, it is "synchronization recovery", and if "stable" appears three times, it is "synchronization maintenance". In this embodiment, A-B is "synchronization maintenance", B-C is "synchronization deterioration", and A-C is "synchronization maintenance". This result constitutes the cross-phase synchronization evaluation result.

[0070] Please refer to Figure 3 , and the specific steps for obtaining the frequency band correspondence relationship are as follows:

[0071] S211: Determine the inter-phase response mode of the load to the disturbance based on the angle change vectors of the three pairs of A-B, B-C, and A-C in the cross-phase synchrony evaluation result. If it shows an approximately coincident state for three consecutive cycles, it is classified as a concentrated load response sample. If there are phased or amplitude response differences between different phases, it is classified as a parallel load response sample;

[0072] To determine the inter-phase response mode of the load to the disturbance based on the angle change vectors of the three pairs of A-B, B-C, and A-C in the cross-phase synchrony evaluation result, it is first necessary to extract the cosine value sequences of the angles between each pair and perform classification processing. The values of the three groups of cosine values of A-B, B-C, and A-C in three consecutive cycles are respectively set as , and where A-B, B-C, and A-C respectively represent the phase angle change difference vector combinations between phase A and phase B, phase B and phase C, and phase A and phase C. Each group of cosine values is the inter-phase similarity index obtained by calculating the previous vector angle, denoted as , , , where , , represents the angle between each group of vectors, in radians. The range of the cosine value of the angle is . Perform a difference judgment on the three-cycle cosine values of each group. Set the difference less than 0.01 as the approximate coincidence standard. According to the three-phase symmetry condition proposed in the IEC61000-4-30 standard, the phase angle offset of voltage fluctuation should not exceed 18°. The corresponding cosine critical value is 0.951. Therefore, set as the approximate state. Judge whether each pair of phases all meet this standard. If the three groups of angle differences all remain within this fluctuation range, the three-phase voltage fluctuation trends in this cycle section are consistent, denoted as "approximately coincident state". The corresponding sample is classified as a concentrated load response sample. In the above example values, the cycle changes of the three groups of data are all lower than 0.004, meeting the approximate coincidence standard. If it is replaced with another group of samples, where the three-cycle cosine value of A-B is , and its fluctuation amplitude reaches 0.088, far exceeding the coincidence threshold, indicating that there are amplitude response differences of the load between different phases. This type of sample is denoted as a parallel load response sample. The difference threshold is based on the polyphase response identification specification shown in IEEE Std1159. It is necessary to have at least one pair of cosine fluctuations exceeding 0.03 to be defined as a parallel type. The judgment step requires calculating the difference between the maximum and minimum cosine values of any one group of the three groups of angle differences in three cycles. If it is greater than the threshold, it enters the parallel response sample set, marks the sample, and saves the corresponding response category.

[0073] S212: Integrate the centralized load response samples and the shunt load response samples, and label the samples with structure and time indexes to form a load response dissection sample set;

[0074] After integrating the centralized load response samples and the shunt load response samples, merge all the sample sets to generate a unified sample index structure. Each group of sample data contains three information fields: phase shift timestamp, sample type identifier (0 represents centralized type, 1 represents shunt type), and sample number (uniquely represented by YYYYMMDDhhmmss + number sequence). During the execution process, fix the sample number format to a 17 - digit decimal number. The first 14 digits are the acquisition timestamp, and the last 3 digits are the in - day sequence number. For example, the sample number "202407050932001" means that this sample was collected at 09:32:00 on July 5, 2024, and is the first sampling segment of the day. Subsequently, save the sample information in a structured manner, and at the same time add a "time index field" and a "response category field" to each sample node. The file structure is defined in JSON format, and the field contents are as follows: "timestamp" corresponds to the start time of the sample, "response_type" is 0 or 1, "sequence_id" is a string - form number. When loading data, quickly filter the response category through the index field. The sample structure also needs to record the three - group angle vector values corresponding to the sample. The data format is represented in an array - nested manner as {"vectors":{"A - B":[0.985,0.989,0.987],"B - C":[0.981,0.982,0.983],"A - C":[0.986,0.987,0.985]}}, where "A - B", "B - C", and "A - C" are still the three - phase combination relationships. The three numerical values in each vector are the cosine values between the corresponding phases in each cycle, corresponding to , , , where i = 1, 2, 3 represents three consecutive cycles. This number is used to implement a reproducible multi - cycle tracking logic in the sample structure. The sample structure is finally written into the sampling record document in dictionary format, and "response_type" is set as the main retrieval field at the database end.

[0075] S213: By invoking the phase calibration interface and frequency component measurement function of the power quality analyzer, obtain the frequency band characteristics corresponding to the three - phase voltages in the load response dissection sample set, and establish the frequency band correspondence relationship between the three - phase voltages and the frequency band characteristics;

[0076] When calling the phase calibration interface and frequency component measurement function of the power quality analyzer, it is necessary to first set the start and end time points of the sampled data, perform FFT processing on the three-phase voltage signals to extract the frequency components. During the processing, the analysis bandwidth is set to 0–2 kHz, the sampling frequency is set to 10 kHz, each group of voltage signals is processed with a window function in units of 1000 points, the corresponding window width is 0.1 second, a Hanning window is applied to each phase voltage to improve the frequency domain resolution. After performing the spectrum calculation, 20 frequency band windows are divided within the 0–2 kHz frequency band for each phase voltage, each segment width is 100 Hz. The square of the amplitude within each frequency band is integrated to obtain its energy value and normalized to represent the relative frequency band energy density. Let the frequency band number be j, then the relative energy density of the j-th frequency band is defined as , , , where , , are the energy integration values of the A, B, and C phase voltages in the j-th frequency band, respectively, with the unit of volt squared second (V²·s), and the vector forms are respectively , , , where each element , , represents the normalized frequency band energy density of this phase in the K-th frequency band, which is used to characterize the frequency domain response characteristics. If for any frequency band there is or , then this frequency band is recorded as the "phase difference frequency band", and with the frequency band number K as the primary key, a frequency band difference record table is established in the sample index structure to associate the three-phase frequency domain mapping characteristics.

[0077] Please refer to Figure 4 , the specific steps for obtaining the frequency band amplitude change data are as follows:

[0078] S3,1,1: Obtain the harmonic voltage amplitudes within the 2nd, 3rd, and 6th frequency bands recorded in the power quality analyzer, and extract the harmonic amplitude sequence whose frequency band information is consistent with that marked in the load response dissection sample set and the frequency band correspondence;

[0079] To obtain the harmonic voltage amplitudes within the 2nd, 3rd, and 6th frequency bands recorded in the power quality analyzer, first call the spectrum analysis module of the analyzer to extract the harmonic component amplitude data it records. Assuming the fundamental frequency of the system is 50 Hz, then the 2nd, 3rd, and 6th harmonics correspond to frequencies of 100 Hz, 150 Hz, and 300 Hz respectively. During the data reading process, assume the sampling period is 0.02 second and the sampling rate is 10 kHz. For each frequency band, the fast Fourier transform (FFT) processing method is used to extract the voltage amplitude at the specified frequency point, which are respectively denoted as 、 、 ,in Indicates the 2nd harmonic voltage amplitude (unit V), which corresponds to the 100Hz point modulus in the frequency domain. Indicates the amplitude of the third harmonic (unit: V), corresponding to the modulus value at 150Hz. Indicates the 6th harmonic amplitude (unit: V), corresponding to the 300Hz point modulus. Then, according to the frequency band correspondence of the load response split sample set record, filter out the sample records with frequency band numbers 2, 3, and 6 from the field "frequency_band" and establish a mapping relationship with the voltage harmonic amplitude data. For example, if sample A is marked with "frequency_band:3" in the record file, then extract the voltage harmonic amplitude data of all measurement points under this sample. Amplitude sequence, forming the corresponding harmonic amplitude sequence , where the subscript Indicates the harmonic order (i.e., 2, 3, 6), and the superscripts (1), (2), and (3) respectively represent the harmonic amplitude data corresponding to the three complete cycles before, during, and after the disturbance, all in volts (V). For example, if the amplitudes of the third harmonics in a certain sampling record are 1.8V, 3.2V, and 2.6V, respectively, then the third harmonic amplitude sequence of the sample is After all samples in the sampling record are matched one by one with the corresponding frequency band number and harmonic number, all the extracted , , The data is stored as a structured vector sequence for the next step of change analysis.

[0080] S312: Extract the amplitude change path of each period before, during, and after the disturbance in the harmonic amplitude sequence with consistent frequency band information, analyze the number of jumps and amplitude fluctuation characteristics, calculate the change trend under continuous cycles, and output frequency band amplitude change data;

[0081] Extract the amplitude change path of each period before, during and after the disturbance in the harmonic amplitude sequence with consistent frequency band information. Assume that the current processing frequency band is the third harmonic and the amplitude sequence is ,in represents the first period before the disturbance Subharmonic amplitude, in volts (V), represents the amplitude of the corresponding period during the disturbance process, Represents the amplitude of the corresponding period after the disturbance. Taking sample A as an example, it is recorded as , calculate the amplitude variation of two adjacent cycles respectively, let The amplitude change difference of the subharmonic frequency band during the next week is , In this example, , , the jump recognition criterion sets the jump determination threshold as . This value is obtained by referring to the harmonic stability requirements in the GB / T 24337-2009 standard. For any period then it is recognized as one jump event. Therefore, in this example, the 3rd harmonic has two jump events, denoted as the number of jumps . Subsequently, the maximum change amplitude of the amplitude in this frequency band over three periods is statistically calculated and denoted as the maximum amplitude fluctuation , which is used to reflect the disturbance intensity. Finally, a trend label is generated according to the amplitude change direction in three periods. If then mark "↑", if then mark "↓", and if they are equal, mark "=". Finally, it is combined into a trend string . In this example, the trend is "↑↓". Therefore, the statistical result of the change in this sample frequency band is output as: harmonic number amplitude sequence , number of jumps , maximum fluctuation amplitude , trend direction label , where: represents the harmonic serial number; represents the th harmonic amplitude array composed of amplitudes in three consecutive periods; represents the number of jump events identified in this array; represents the difference between the maximum value and the minimum value among the three values; represents the trend symbol combination formed by two adjacent amplitude changes during three periods. This sample data is added to the frequency band amplitude change dataset. If further statistics are performed on multiple samples, in , = [0.6, 0.62, 0.58] cases, the following are calculated respectively: , , (only the first group change exceeds 0.5), , the trend is "↑↓"; while for the 6th harmonic, each segment change 、 , are all less than the threshold, then = 0, 0.04V, the trend is "↑↓". Finally, the analysis result structure of each frequency band change is complete, and the values of each data item are clear, meeting the requirements of sample statistical output.

[0082] Please refer to Figure 5, the steps for obtaining the interference coupling fluctuation section are specifically as follows:

[0083] S411: Obtain the change trend of the fundamental frequency total power of the meter load monitored by the power quality analyzer within the disturbance period, establish power sequence data according to the period, and obtain the fundamental frequency total power change sequence;

[0084] To obtain the change trend of the fundamental frequency total power of the meter load monitored by the power quality analyzer within the disturbance period, first set the voltage and current signals corresponding to the monitoring time period, extract the fundamental wave frequency (50 Hz) from the three-phase waveform data, then calculate the instantaneous active power of each period respectively and perform period average operation to obtain the period average power value, denoted as , where the subscript j represents the period number, with the unit of watt (W), and each represents the average total active power value within the j-th complete power frequency period. Assuming that the disturbance duration covers 3 complete periods, then construct the power sequence , where P is the fundamental frequency power change sequence vector, and the element , , are the active powers of the 1st, 2nd, and 3rd periods respectively, with the unit of watt (W). Taking the sample as an example, if the three-phase voltage effective values are , = 219 V, = 221 V, and the current effective value is 10 A, , = 10.1 A, where , , represents the three-phase voltage effective value (unit: V), , , represents the three-phase current effective value (unit: A), and the power factor is , substitute into the calculation , where and are the average values of the three-phase voltage and current respectively, with the units of V and A. If the current in the 2nd period increases to , = 11.8 A, , similarly, , the current in the 3rd period drops to , , , then 6930 W, and finally obtain the fundamental frequency active power change sequence , as the total power change record within the disturbance period.

[0085] S412: Extract the harmonic amplitude change trend at the corresponding moment according to the frequency band amplitude change data, perform periodic corresponding pairing with the fundamental frequency total power change sequence, and generate an amplitude-power ratio sequence;

[0086] Extract the harmonic amplitude change trend at the corresponding moment according to the frequency band amplitude change data. First, call the amplitude sequence corresponding to the th harmonic that has been obtained , where represents the 3rd harmonic frequency band, represents the amplitude (unit: V) of the th harmonic in the Kth cycle, , with the unit of volt (V). Construct an amplitude-power pairing structure, pair each cycle's harmonic amplitude with the corresponding cycle's fundamental frequency power to form paired elements and form a paired sequence , where M is the "amplitude-power" ratio vector group, and the elements are binary tuples. The former is the harmonic amplitude of this cycle, and the latter is the fundamental frequency active power of this cycle. If in sample A and , then there is

[0087] , and organize the pairing results into a key-value structure {"t1":[1.8,6270],"t2":[3.2,7524],"t3":[2.6,6930]} according to the time tags, where "t1", "t2", and "t3" respectively correspond to the time sequence tags of the periods before, during, and after the disturbance, and complete the amplitude and power trend alignment pairing structure. [[ID=,29]]

[0088] S413: Identify the period segments where the jump trends of the two types of trends in the amplitude-power ratio sequence are consistent during the disturbance period through the cumulative sum test algorithm, and judge the interference coupling fluctuation segments with synchronous fluctuations;

[0089] Identify the period segments with consistent trends in the amplitude-power ratio sequence M through the cumulative sum test algorithm. First, define the trend change method. Let the amplitude change amount of the th harmonic between two periods be , and the fundamental frequency power change amount be , where [[ID=,41]]represents the period index, and judge whether the signs of the two are the same. Let the sign consistency marking value be , where is the sign function. If the product is greater than 0, then represents consistent trends, and if the product is less than 0, then represents inconsistent trends. For example, in sample A , the change in the first segment is , , the product +1.4×+1245 = , so = +1;

[0090] The second paragraph changes to , , the product , so , add all accumulatively to obtain the total number of period segments with consistent trends , where represents the period segment with consistent trends, and the unit is the number of segments (dimensionless). Set the threshold for determining consistent trends to . According to the sampling segment number n = 3, the threshold is taken as n - 1, that is, 2. If , it is determined that there is an interference coupling trend section. This value comes from the principle that the power and amplitude synchronous jitter time in the interference section should cover at least two period segments. Finally, it is determined that there is an interference coupling fluctuation section with trend synchronization in the disturbance period in sample A, and the coupling characteristic is established.

[0091] Please refer to Figure 6 , and the steps for obtaining the electricity meter load monitoring results are specifically as follows:

[0092] S511: Extract the frequency points with jumps in each period in the interference coupling fluctuation section, count the corresponding jump amplitudes and jump frequencies, and obtain the jump frequency point index set;

[0093] Extract the frequency points with jumps in each period in the interference coupling fluctuation section. First, divide the frequency data collected by the electricity meter into multiple time periods according to the period (with 50Hz as the reference), each period is 20ms, and the amount of data in each period is 20 data points according to the 1kHz sampling rate. Read the frequency change sequence in each period in turn. By calculating the frequency difference between adjacent data points, if the frequency difference is greater than the set jump threshold Δf (set to 0.3Hz), then record the current frequency point as a jump point. For example, if the frequency of the 9th data point is 49.8Hz and the 10th is 50.2Hz, then the difference is 0.4Hz, which meets the jump condition, and it is recorded as the jump frequency point 50.2Hz. Record its jump amplitude as 0.4Hz, and the jump frequency is accumulated once in one period. By analogy, traverse all periods, count the number of times each jump frequency point accumulates in the observation period and the corresponding average jump amplitude, and obtain the jump frequency point index set, where each frequency point is recorded as follows: frequency value, number of jumps, average jump amplitude. If 20 periods are sampled, at most 200 data points can be recorded, and the selected frequency points such as 50.2Hz, 49.6Hz, etc. each have independent statistical entries and are stored in the index set.

[0094] S512: According to the jump frequency point index set, screen the set of frequency points with prominent jump amplitudes and frequencies, and construct a candidate active frequency point sequence;

[0095] According to the jump frequency point index set, traverse the jump frequency and average jump amplitude of each frequency point. First, calculate the mean value of the jump frequencies of all frequency points and the standard deviation , as well as the mean value of the jump amplitudes and the standard deviation . On this basis, if the jump frequency of a certain frequency point is greater than +1.5 and its average jump amplitude is greater than +1.5 , then it is judged as a frequency point with prominent jump characteristics and classified into the candidate active frequency point set. For example, for 50 frequency points, if the mean frequency is 4 times, the standard deviation is 1.2 times, the mean amplitude is 0.25 Hz, and the standard deviation is 0.05 Hz, then the frequency point needs to satisfy the frequency > 5.8 times and the amplitude > 0.325 Hz. For example, the jump frequency of 49.6 Hz is 6 times and the amplitude is 0.38 Hz, which meets the conditions and is selected into the candidate set. This set is used as the input sequence for subsequent clustering analysis, including the frequency point value and its statistical characteristics. In this process, all screened frequency points need to meet the dual conditions of frequency and amplitude to avoid introducing errors due to occasional jumps.

[0096] S513: Use the density clustering algorithm to perform clustering analysis on the candidate active frequency point sequence, identify the activation frequency points exceeding the preset frequency threshold, and output the electricity meter load monitoring results for the amplitude limiting processing of interference frequency points and the optimization of load identification;

[0097] Use the density clustering algorithm to perform clustering analysis on the candidate active frequency point sequence. First, represent each candidate frequency point as a two-dimensional vector , where represents the actual frequency value of the frequency point, in Hz, represents the number of jumps that have occurred cumulatively at this frequency point in multiple cycles, in times. This vector sequence constitutes a point set , where n is the number of candidate frequency points. To improve the density perception accuracy, introduce the density weight evaluation parameter to measure the degree of aggregation of a certain frequency point in its local area. Its expression is: , where is the density value of frequency point i, represents all frequency points that satisfy , representing the neighborhood range of this point, is the smoothing coefficient, in Hz, with a set value of 0.2 Hz, The neighborhood radius is set to 0.5 Hz, which is used to determine whether points with close frequency values form a clustering relationship. MinPts is the threshold for clustering core points, set to 3, indicating that at least 2 neighbor frequency points are required. The judgment logic is as follows: Traverse all candidate frequency points, calculate the number of qualified points in their neighborhoods one by one. If the number of neighborhood points is greater than or equal to MinPts, the current frequency point is a core point, record all frequency points in its neighborhood as members of the same cluster, and then continue to expand other core points, merge clusters with overlapping neighborhoods to form several density frequency clusters, and calculate the average frequency value within each frequency cluster and the average frequency hopping times , set the activation frequency judgment threshold and the density threshold . Filter out the frequency point clusters whose average frequency value is greater than this threshold and density is greater than this value, and finally use them as active activation frequency points for subsequent processing. Suppose a set of candidate frequency points is as follows: , = 7 times, = 49.6 Hz, = 6 times, = 49.7 Hz, = 5 times. For the point Hz, its neighborhood is Hz, and the neighborhood contains the point , calculate the density value as follows:

[0098] , , since , and the number of neighborhood points is 2 ≥ MinPts - 1, it is determined as a core point, and its clustering cluster is {49.5, 49.6, 49.7} Hz. The average frequency within the cluster is: , and the average frequency hopping times is: times. Since , this frequency cluster does not meet the activation condition, so it is not output. However, if another frequency cluster contains the points: times), times) , calculate its: , times, , satisfying and , it is determined as an activation frequency cluster, and finally output the frequency points 50.1 Hz, 50.2 Hz, 50.3 Hz within this cluster, which form the activation frequency point sequence for monitoring output and participate in the subsequent interference frequency point amplitude limiting and electricity meter load identification steps. This step constructs a frequency point recognition mechanism with relatively high accuracy through the combination of frequency hopping times and local density

[0099] An electricity meter load monitoring system based on a power quality analyzer, the electricity meter load monitoring system based on a power quality analyzer is used to execute the above-mentioned electricity meter load monitoring method based on a power quality analyzer. The system includes:

[0100] The cross-phase synchronization identification module obtains the three-phase voltages of the electricity meter through the power quality analyzer, extracts the phase shift angles of the three-phase voltages within the disturbance period to judge the synchronization characteristics between phases, and forms a cross-phase synchronization evaluation result;

[0101] The load response attribution module, based on the cross-phase synchronization evaluation result, identifies the load response attribution types of the phase shift angles of the three-phase voltages, establishes a load response dissection sample set, and obtains the frequency band characteristics corresponding to the three-phase voltages in the load response dissection sample set to establish a frequency band correspondence relationship;

[0102] The frequency band amplitude analysis module, referring to the frequency band correspondence relationship of the frequency band characteristics corresponding to the three-phase voltages in the load response dissection sample set, judges the change trend of the harmonic amplitudes within the frequency band to obtain the frequency band amplitude change data;

[0103] The power trend comparison module obtains the change trend of the fundamental frequency total power of the electricity meter load monitored by the power quality analyzer within the disturbance period, compares the change trend of the harmonic amplitudes at the corresponding moments in the frequency band amplitude change data with the change trend of the fundamental frequency total power, and identifies the interference coupling fluctuation section;

[0104] The active frequency point identification module extracts the frequency points with prominent jump amplitudes and jump frequencies in the interference coupling fluctuation section, performs active frequency point analysis on the frequency points, and outputs the electricity meter load monitoring result.

[0105] The above is only a preferred embodiment of the present invention, and does not limit the present invention in other forms. Any person skilled in the relevant art may use the disclosed technical content to make changes or modifications into equivalent embodiments with equivalent changes and apply them to other fields. However, as long as it does not depart from the technical content of the technical solution of the present invention, any simple modification, equivalent change and modification made to the above embodiments based on the technical essence of the present invention still fall within the protection scope of the technical solution of the present invention.

Claims

1. A method for monitoring the load of an electric meter based on a power quality analyzer, characterized in that It includes the following steps: S1: Obtain the three-phase voltage of the electric meter through a power quality analyzer, extract the phase shift angles of the three-phase voltage during the disturbance period to judge the synchronization characteristics between phases, and form a cross-phase synchronization evaluation result; S2: Based on the cross-phase synchronization evaluation result, identify the load response attribution types of the phase shift angles of the three-phase voltage, establish a load response dissection sample set, and obtain the frequency band characteristics corresponding to the three-phase voltage in the load response dissection sample set to establish a frequency band correspondence relationship; S3: Refer to the frequency band correspondence relationship of the frequency band characteristics corresponding to the three-phase voltage in the load response dissection sample set, judge the change trend of the harmonic amplitude within the frequency band, and obtain the frequency band amplitude change data; S4: Obtain the change trend of the fundamental frequency total power of the electric meter load monitored by the power quality analyzer during the disturbance period, compare the change trend of the harmonic amplitude at the corresponding moment in the frequency band amplitude change data with the change trend of the fundamental frequency total power, and identify the interference coupling fluctuation section; S5: Extract the frequency points with prominent jump amplitudes and jump frequencies in the interference coupling fluctuation section, perform active frequency point analysis on the frequency points, and output the monitoring result of the electric meter load; The cross-phase synchronization evaluation result includes the phase shift direction feature, the inter-phase response consistency level, and the synchronous stability change condition. The load response dissection sample set includes the load combination structure label, the response mode classification result, and the phase synchronization performance record. The frequency band amplitude change data is specifically the jump amplitude trajectory within the frequency band, the cycle stability characteristic index, and the frequency fluctuation distribution state. The interference coupling fluctuation section specifically refers to the harmonic disturbance superposition interval, the power change synchronization region, and the frequency band behavior resonance node.

2. The method for monitoring the load of an electric meter based on a power quality analyzer according to claim 1, characterized in that The monitoring result of the electric meter load includes the load type identification result, the interference frequency identification label, and the load structure response information.

3. The method for monitoring the load of an electric meter based on a power quality analyzer according to claim 1, wherein The specific steps for obtaining the cross-phase synchronization evaluation result are as follows: S111: Obtain the A, B, and C three-phase voltage waveform data recorded by the power quality analyzer within a specified time period, and extract the voltage phase shift angle change sequences in the three cycles before, during, and after the disturbance; S112: Based on the voltage phase shift angle change sequence, use the cosine similarity algorithm to calculate the cosine values of the angles between the three pairs of angle change vectors of A - B, B - C, and A - C in the sequence respectively, and judge the inter-phase synchronization matching degree at each sampling point within the corresponding cycle according to the cosine value of the angle, and obtain the phase shift path similarity analysis result; S113: Judge the average similarity change trend of the three pairs of angle change vectors of A - B, B - C, and A - C in the three consecutive cycle sections of the phase shift path similarity analysis result to form a cross-phase synchronization evaluation result.

4. The method for monitoring the meter load based on the power quality analyzer according to claim 3, wherein The specific steps for obtaining the frequency band correspondence relationship are as follows: S211: Judge the inter-phase response mode of the load to the disturbance according to the three pairs of angle change vectors of A - B, B - C, and A - C in the cross-phase synchronization evaluation result. If it shows a similar coincidence state in three consecutive cycles, it is classified as a concentrated load response sample. If there are phased or amplitude response differences between different phases, it is classified as a parallel load response sample; S212: Integrate the centralized load response samples and the shunt load response samples, and perform structural and time index labeling on the samples to form a load response dissection sample set; S213: By invoking the phase calibration interface and frequency component measurement function of the power quality analyzer, obtain the frequency band characteristics corresponding to the three-phase voltage in the load response dissection sample set, and establish the frequency band correspondence between the three-phase voltage and the frequency band characteristics.

5. The method for monitoring the meter load based on the power quality analyzer according to claim 4, wherein The specific steps for obtaining the frequency band amplitude change data are as follows: S311: Obtain the harmonic voltage amplitudes in the 2nd, 3rd, and 6th frequency bands recorded in the power quality analyzer, and extract the harmonic amplitude sequence whose frequency band information is consistent with that marked in the load response dissection sample set and the frequency band correspondence; S312: Extract the amplitude change paths of each cycle before, during, and after the disturbance in the harmonic amplitude sequence with consistent frequency band information, analyze the number of jumps and amplitude fluctuation characteristics, and statistically analyze the change trend under continuous cycles to output the frequency band amplitude change data.

6. The method for monitoring the meter load based on a power quality analyzer according to claim 5, characterized in that, The specific steps for obtaining the interference coupling fluctuation section are as follows: S411: Obtain the change trend of the fundamental frequency total power of the meter load monitored by the power quality analyzer during the disturbance cycle, establish power sequence data according to the cycle to obtain the fundamental frequency total power change sequence; S412: Extract the harmonic amplitude change trend at the corresponding moment according to the frequency band amplitude change data, and perform cycle corresponding pairing with the fundamental frequency total power change sequence to generate an amplitude-power ratio sequence; S413: Identify the cycle sections where the jump trends of the two types of trends in the amplitude-power ratio sequence are consistent during the disturbance cycle through the cumulative sum test algorithm, and judge the interference coupling fluctuation section of synchronous fluctuation.

7. The method for monitoring the load of an electric meter based on a power quality analyzer according to claim 6, wherein The specific steps for obtaining the monitoring result of the meter load are as follows: S511: Extract the frequency points with jumps in each cycle in the interference coupling fluctuation section, and count the corresponding jump amplitudes and jump frequencies to obtain a jump frequency point index set; S512: According to the jump frequency point index set, screen the set of frequency points with prominent jump amplitudes and frequencies, and construct a candidate active frequency point sequence; S513: Use the density clustering algorithm to perform clustering analysis on the candidate active frequency point sequence, identify the activation frequency points exceeding the preset frequency threshold, and output the monitoring result of the meter load for the amplitude limiting processing of interference frequency points and the optimization of load identification.

8. An electricity meter load monitoring system based on a power quality analyzer, characterized in that, According to the method for monitoring the meter load based on a power quality analyzer according to any one of claims 1-7, the system includes: The cross-phase synchronization identification module obtains the three-phase voltage of the meter through the power quality analyzer, extracts the phase offset angles of the three-phase voltage during the disturbance cycle to judge the synchronization characteristics between phases, and forms a cross-phase synchronization evaluation result; The load response attribution module, based on the cross-phase synchronization evaluation result, identifies the load response attribution type of the phase offset angles of the three-phase voltage, establishes a load response dissection sample set, obtains the frequency band characteristics corresponding to the three-phase voltage in the load response dissection sample set, and establishes a frequency band correspondence; The frequency band amplitude analysis module, referring to the frequency band correspondence of the frequency band characteristics corresponding to the three-phase voltage in the load response dissection sample set, judges the change trend of the harmonic amplitude in the frequency band to obtain the frequency band amplitude change data; The power trend comparison module obtains the change trend of the fundamental frequency total power of the meter load monitored by the power quality analyzer within the disturbance period, compares the change trend of the harmonic amplitude at the corresponding moment in the frequency band amplitude change data with the change trend of the fundamental frequency total power, and identifies the interference coupling fluctuation section; The active frequency point identification module extracts the frequency points with prominent jump amplitudes and jump frequencies in the interference coupling fluctuation section, performs active frequency point analysis on the frequency points, and outputs the monitoring results of the meter load.

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