A high-efficiency new energy power quality monitoring method and system adaptive to multiple scenes

Abstract

The application belongs to the technical field of power quality measurement, and particularly relates to a high-efficiency new energy power quality monitoring method and system suitable for multiple scenes, wherein the method comprises the following steps: synchronously sampling voltage and current signals in a frequency tracking mode, dividing sampling data obtained through power interval labels, performing jump detection, dynamic data segmentation and data statistical processing according to application scenes, and obtaining wind power, solar energy and conventional power quality detection wind power and power quality monitoring; and performing new energy power quality monitoring according to application scenes from A-level power quality analysis results and data categories, so that the new energy power quality monitoring efficiency, range and accuracy are improved through the technical scheme of the application.

Description

Technical Field

[0001] This invention relates to the field of power quality measurement technology, and in particular to a highly efficient new energy power quality monitoring method and system adaptable to multiple scenarios. Background Technology

[0002] The power quality interference levels generated by wind and solar power vary under different operating conditions. Therefore, power quality monitoring and evaluation for wind and solar power must be conducted separately for each power range. Current power quality monitoring products cannot automatically perform power quality monitoring and generate power quality evaluation reports for each power range. Manual screening and statistical analysis of monitoring data for each power range, as well as report writing, are required. Furthermore, because current wind and solar power quality monitoring cannot automatically monitor power quality for each power range, it is impossible to know whether the data for each power range meets the required number of evaluation criteria. The current practice is to extend the monitoring period to a very long time (usually more than one month) to determine if enough power range data has been collected. In reality, power range data monitoring may be completed in just one week, wasting a significant amount of time. Summary of the Invention

[0003] Aimed at solving at least one of the technical problems existing in the prior art, the present invention provides an efficient new energy power quality monitoring method and system that is adaptable to multiple scenarios.

[0004] One aspect of the present invention provides an efficient new energy power quality monitoring method adaptable to multiple scenarios, comprising: The voltage and current signals are synchronously sampled using a frequency tracking method to obtain and buffer the sampled data. Based on the application scenario, the sampled data is subjected to Class A power quality analysis to obtain Class A power quality analysis results. The application scenarios include wind power power quality monitoring, solar power quality monitoring and conventional power quality monitoring. The data categories of the Class A power quality analysis results include set values, frequency, short-time flicker and long-time flicker. Based on the application scenario, data categories are selected from the Class A power quality analysis results. Then, based on the data category selection results, the new energy power quality monitoring is performed as follows: If the application scenario is wind power quality monitoring, the wind power range is divided according to the installed capacity of the wind farm, and power range labels are added to the data category selection results according to the wind power range. The A-level power quality analysis results are statistically analyzed by wind power range and power range labels, and the wind power quality assessment report is determined based on the statistical results. If the application scenario is solar power quality monitoring, the A-level power quality analysis results are divided according to the solar power range and power range labels are added. The A-level power quality analysis results are statistically analyzed using jump detection and dynamic data segmentation methods based on the solar power range and power range labels. The solar power quality assessment report is determined based on the statistical results. If the application scenario is routine power quality monitoring, the analysis results of Class A power quality are statistically analyzed to obtain a routine power quality assessment report.

[0005] According to the efficient new energy power quality monitoring method adapted to multiple scenarios, the sampling interval for synchronous sampling is obtained by the following formula:

[0006] in, The sampling interval is... This represents the number of sampling points per wave. This refers to the system frequency.

[0007] According to the efficient new energy power quality monitoring method adapted to multiple scenarios, the sampled data is subjected to Class A power quality analysis based on the application scenario to obtain Class A power quality analysis results, including: The voltage analog signal is acquired and frequency measurement is performed synchronously at 10-second intervals to obtain the system frequency over 10 seconds. According to the Class A requirements of IEC 61000-4-30, absolute 10-minute synchronization is performed on the sampling value windows of 10-cycle, 150-cycle, 10-minute and 2-hour ensemble values ​​to obtain an absolute 10-minute synchronization signal; The absolute 10-minute synchronization sampling data is obtained based on the absolute 10-minute synchronization signal. The 10-cycle, 150-cycle, 10-minute and 2-hour aggregate values ​​are calculated based on the absolute 10-minute synchronization sampling data according to the IEC 61000-4-30 Class A requirements. The aggregate values ​​include voltage, power, harmonics, interharmonics, unbalance and sequence components. The instantaneous flicker visual sensitivity is calculated and cached based on the sampled data, and the solar power range transition signal is acquired. If the application scenario is wind power quality measurement and conventional power quality measurement, the cached instantaneous flicker visual sensitivity is synchronized based on the absolute 10-minute synchronization signal, the application scenario, and the solar power range transition signal. If the application scenario is solar power quality monitoring, the solar power range transition signal is synchronized with the cached instantaneous flicker visual sensitivity. When the application scenario is wind power quality monitoring or conventional power quality monitoring, the instantaneous flicker visual sensitivity buffer is cleared and re-buried based on the absolute 10-minute synchronization signal; after receiving the next 10-minute synchronization signal, the instantaneous flicker visual sensitivity of all buffered buffers is statistically evaluated for flicker; when monitoring solar power quality, when receiving instantaneous flicker visual sensitivity, it is determined whether the number of buffered instantaneous flicker visual sensitivity is greater than or equal to the number of instantaneous flicker visual sensitivity in 10 minutes. If so, the short-term flicker and long-term flicker evaluations of all buffered instantaneous flicker visual sensitivity are performed and the buffered instantaneous flicker visual sensitivity is cleared. When the application scenario is wind power quality monitoring, the 10-minute ensemble value, short-time flicker, and long-time flicker are automatically identified based on the wind power range. When the application scenario is solar power quality monitoring, the 10-cycle ensemble value, short-time flicker, and long-time flicker are automatically identified. When the application scenario is conventional power quality monitoring, the 10-second frequency, 10-minute ensemble value, short-time flicker, and long-time flicker are automatically identified.

[0008] According to the efficient new energy power quality monitoring method adapted to multiple scenarios, when the application scenario is wind power power quality monitoring, the wind power power quality assessment report is obtained through the following steps: Obtain the 10-minute power quality set values ​​with power range labels and send them to the wind power range cache for counting. The wind power range cache includes the power range label, the number of 10-minute set values, and the 10-minute set value cache. If the count of the wind power range meets the requirements of the wind power quality assessment standard, the 10-minute aggregate value of all power ranges is assessed to obtain a wind power quality assessment report, and then the 10-minute aggregate value and count of all power ranges are cleared.

[0009] According to the efficient new energy power quality monitoring method adapted to multiple scenarios, when the application scenario is solar power quality monitoring, the solar power quality assessment report is obtained through the following steps: The 10-cycle power quality set value is obtained from the Class A power quality analysis by solar power range, and the latest 10-cycle power quality set value greater than 10 minutes is cached. Wavelet analysis is used to identify jumps in the solar power range. If a jump exists in the solar power range, the 10-cycle power quality set value for the corresponding solar power range is cleared. If there is no jump in the solar power range, the 10-minute set value is calculated and the 10-cycle power quality set value cache is cleared when the number of 10-cycle set values ​​meets the requirement of counting 10-minute set values. Add a power range label to the 10-cycle set value based on the solar power range to which it belongs; The 10-cycle set values ​​with the added power range label are cached in the solar power range cache and the count is updated. When the number of 10-minute set values ​​in all power ranges in the solar power range cache meets the requirements of the solar power quality assessment standard, the 10-minute set values ​​of all power ranges are assessed for quality and all 10-minute set values ​​and counts are cleared to obtain the solar power quality assessment report.

[0010] According to the efficient new energy power quality monitoring method adapted to multiple scenarios, wavelet analysis is used to identify jumps in the solar power range, including: A circular cache is used to store 10-cycle values ​​for a period of more than 10 minutes. Every 10-cycle interval, the latest multiple 10-cycle power sets are retrieved from the cache. :

[0011] in, This represents the total number of power ensemble values ​​for 10 cycles. For power sequences Discrete wavelet transform based on a 4th-order Daubechies wavelet basis is performed, with wavelet decomposition parameters set to three decomposition levels, as follows:

[0012] in For wavelet basis functions, For scale parameters, These are translation parameters; The second level detail coefficients obtained after wavelet decomposition To monitor the modulus maxima, the i-th point is considered an extreme point when it simultaneously satisfies the following conditions:

[0013]

[0014]

[0015] The adaptive threshold is calculated as follows:

[0016] in, The median of the absolute values ​​of the detail coefficients. is the median of the absolute deviations of the detail coefficients from the median, and 0.6745 is the Gaussian distribution correction coefficient. To locate the power abrupt change point, the extreme points of the wavelet transform detail coefficients are mapped to the power sequence positions, as follows:

[0017] in The levels of wavelet decomposition, Location of the power abrupt change point. This indicates the location of the extreme points of the wavelet transform detail coefficients; Based on the mapping results, if no power range transition occurs, determine whether the count of the current 10-cycle set value is greater than 1. If the value is greater than 10, a 10-minute set value calculation is performed, and the 10-cycle set value count is reset to zero. The calculation formula is as follows:

[0018] in, Counting the set values ​​for 10 cycles, Indicates the first A set of 10-cycle values.

[0019] According to the efficient new energy power quality monitoring method adapted to multiple scenarios, when the application scenario is conventional power quality monitoring, the conventional power quality assessment report is obtained through the following steps: It receives and caches the 10-minute set value, frequency, long-term flicker, and short-term flicker from the Class A power quality analysis results. During the evaluation period, it performs statistics on the 10-minute set value, frequency, long-term flicker, and short-term flicker according to the conventional power quality evaluation standards and generates a conventional power quality evaluation report.

[0020] Another aspect of the present invention discloses a highly efficient new energy power quality monitoring system adaptable to multiple scenarios, comprising: The synchronous sampling module is used to synchronously sample voltage and current signals using frequency tracking, and to obtain and buffer the sampled data. The Class A power quality analysis module is used to perform Class A power quality analysis on the sampled data according to the application scenario, and obtain Class A power quality analysis results. The application scenarios include wind power power quality monitoring, solar power quality monitoring and conventional power quality monitoring. The data categories of the Class A power quality analysis results include set values, frequency, short-time flicker and long-time flicker. The new energy power quality monitoring module is used to select data categories from the Class A power quality analysis results according to the application scenario, and to perform corresponding new energy power quality monitoring based on the data category selection results. It includes: The wind power quality assessment module is used when the application scenario is wind power quality monitoring. It divides the wind power range according to the installed capacity of the wind farm, adds power range labels to the data category selection results according to the wind power range, statistically analyzes the Class A power quality results through the wind power range and power range labels, and determines the wind power quality assessment report based on the statistical results. The solar power quality assessment module is used when the application scenario is solar power quality monitoring. It divides the Class A power quality analysis results according to the solar power range and adds power range labels. It uses jump detection and dynamic data segmentation methods to statistically analyze the Class A power quality analysis results through the solar power range and power range labels, and determines the solar power quality assessment report based on the statistical results. The conventional power quality assessment module is used to statistically analyze the results of Class A power quality when the application scenario is conventional power quality monitoring, and to obtain a conventional power quality assessment report.

[0021] The beneficial effects of this invention are as follows: by intelligently identifying power ranges and tracking sample progress in real time, the monitoring cycle is significantly shortened; by using sliding window caching and dynamic data segmentation methods during power range transitions, data utilization and monitoring efficiency are greatly improved; the entire process from sampling, range judgment, sample validity confirmation to report generation is automated, supporting remote monitoring and email notifications, and achieving unmanned testing; at the same time, it meets the testing and evaluation requirements for solar, wind, and conventional power quality, greatly improving the application value of the system. Attached Figure Description

[0022] Figure 1 This is a flowchart of an efficient new energy power quality monitoring method adapted to multiple scenarios, according to an embodiment of the present invention.

[0023] Figure 2 This is a flowchart of the Class A power quality analysis according to an embodiment of the present invention.

[0024] Figure 3 This is a schematic diagram of the wind power quality monitoring process according to an embodiment of the present invention.

[0025] Figure 4 This is a schematic diagram of the solar power quality monitoring process according to an embodiment of the present invention.

[0026] Figure 5 This is a schematic diagram of the transition recognition process according to an embodiment of the present invention.

[0027] Figure 6 This is a block diagram of a highly efficient new energy power quality monitoring system adapted to multiple scenarios, according to an embodiment of the present invention.

[0028] Figure 7This is a block diagram of another efficient new energy power quality monitoring system adapted to multiple scenarios according to an embodiment of the present invention.

[0029] Figure 8 This describes the module workflow of the monitoring system in this embodiment of the invention.

[0030] Figure 9 This is a schematic diagram of the grid connection point of a photovoltaic power station according to an embodiment of the present invention.

[0031] Figure 10 This describes the workflow of the Class A power quality analysis module in this embodiment of the invention.

[0032] Figure 11 This is a diagram illustrating the power jump effect based on wavelet transform in an embodiment of the present invention.

[0033] Figure 12 This is a diagram illustrating the power jump point detection effect based on wavelet transform according to an embodiment of the present invention.

[0034] Figure 13 This is a schematic diagram of the power range buffer module according to an embodiment of the present invention. Detailed Implementation

[0035] The embodiments of the present invention are described in detail below, examples of which are shown in the accompanying drawings. Throughout the description, the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions. In the following description, suffixes such as "module," "part," or "unit" used to denote elements are used only for the purpose of illustrative purposes and have no inherent meaning. Therefore, "module," "part," or "unit" can be used interchangeably. Terms such as "first," "second," etc., are used only to distinguish technical features and should not be construed as indicating or implying relative importance, or implicitly indicating the number of indicated technical features, or implicitly indicating the sequential relationship of the indicated technical features. In this subsequent description, the consecutive reference numerals for method steps are for ease of review and understanding. Adjusting the implementation order of steps, in conjunction with the overall technical solution of the present invention and the logical relationship between the various steps, will not affect the technical effect achieved by the technical solution of the present invention. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention, and should not be construed as limiting the present invention.

[0036] refer to Figure 1 , Figure 1 This is a flowchart of an efficient new energy power quality monitoring method adapted to multiple scenarios according to an embodiment of the present invention, which includes, but is not limited to, steps S100~S300: including: S100 synchronously samples voltage and current signals using frequency tracking, and then buffers the sampled data.

[0037] In some embodiments, the sampling interval for synchronous sampling is obtained by the following formula:

[0038] in, The sampling interval is... This represents the number of sampling points per wave. This refers to the system frequency.

[0039] S200 performs Class A power quality analysis on the sampled data according to the application scenario to obtain Class A power quality analysis results. The application scenarios include wind power quality monitoring, solar power quality monitoring and conventional power quality monitoring. The data categories of the Class A power quality analysis results include ensemble values, frequency, short-time flicker and long-time flicker.

[0040] In some embodiments, reference Figure 2 The flowchart for Class A power quality analysis shown includes, but is not limited to, steps S210 to S260: S210, acquires the voltage analog signal and synchronizes the frequency measurement at the whole 10-second interval to obtain the system frequency for 10 seconds; S220, according to the Class A requirements of IEC 61000-4-30, performs absolute 10-minute synchronization on the sampling value windows of 10-cycle, 150-cycle, 10-minute and 2-hour ensemble values ​​to obtain an absolute 10-minute synchronization signal; S230: Obtain absolute 10-minute synchronization sampling data based on the absolute 10-minute synchronization signal, and calculate the 10-cycle, 150-cycle, 10-minute and 2-hour aggregate values ​​for the absolute 10-minute synchronization sampling data according to the IEC 61000-4-30 Class A requirements. The aggregate values ​​include voltage, power, harmonics, interharmonics, unbalance and sequence components. S240 calculates and caches the instantaneous flicker visual sensitivity based on the sampled data, and acquires the solar power range transition signal. If the application scenario is wind power quality measurement and conventional power quality measurement, the cached instantaneous flicker visual sensitivity is synchronized based on the absolute 10-minute synchronization signal, the application scenario, and the solar power range transition signal. If the application scenario is solar power quality monitoring, the solar power range transition signal is synchronized with the cached instantaneous flicker visual sensitivity. S250, when the application scenario is wind power quality monitoring or conventional power quality monitoring, clears the buffer of transient flicker visual sensitivity based on the absolute 10-minute synchronization signal and re-buffers the transient flicker visual sensitivity; after receiving the next 10-minute synchronization signal, performs a statistical evaluation of the flicker of all buffered transient flicker visual sensitivity; when monitoring solar power quality, when receiving transient flicker visual sensitivity, it determines whether the number of buffered transient flicker visual sensitivity is greater than or equal to the number of 10-minute transient flicker visual sensitivity. If so, it performs short-term flicker and long-term flicker evaluation on all buffered transient flicker visual sensitivity and clears the buffered transient flicker visual sensitivity. S260, when the application scenario is wind power quality monitoring, automatically identifies the 10-minute ensemble value, short-time flicker, and long-time flicker based on the wind power range; when the application scenario is solar power quality monitoring, it automatically identifies the 10-cycle ensemble value, short-time flicker, and long-time flicker; when the application scenario is conventional power quality monitoring, it automatically identifies the 10-second frequency, 10-minute ensemble value, short-time flicker, and long-time flicker.

[0041] S300: Select data categories from the Class A power quality analysis results based on the application scenario; based on the selected data categories, execute steps S310~S330 for new energy power quality monitoring. S310, if the application scenario is wind power quality monitoring, divide the wind power range according to the installed capacity of the wind farm, add power range labels to the data category selection results according to the wind power range, statistically analyze the Class A power quality results through the wind power range and power range labels, and determine the wind power quality assessment report based on the statistical results.

[0042] In some embodiments, reference Figure 3 The schematic diagram of the wind power power quality monitoring process shown includes, but is not limited to, steps S311 to S312: S311, obtain the 10-minute power quality set value with power range label and send it to the wind power range cache and count it. The wind power range cache includes the power range label, the number of 10-minute set values ​​and the 10-minute set value cache. S312, if the count of the wind power range meets the requirements of the wind power quality assessment standard, perform a quality assessment on the 10-minute aggregate value of all power ranges to obtain a wind power quality assessment report, and then clear the 10-minute aggregate value and count of all power ranges.

[0043] The correspondence between power ranges and labels is shown in Table 1.

[0044] Table 1. Power Range and Label Comparison Table

[0045] The determination formula is as follows:

[0046] The correspondence between tags, technologies, and caches is shown in Table 2.

[0047] Table 2. Tag, Count, and Cache Relationship Table

[0048] S320, if the application scenario is solar power quality monitoring, the A-level power quality analysis results are divided according to the solar power range and power range labels are added. The A-level power quality analysis results are statistically analyzed using jump detection and dynamic data segmentation methods based on the solar power range and power range labels. The solar power quality assessment report is determined based on the statistical results.

[0049] In some embodiments, reference Figure 4 The schematic diagram of the solar power quality monitoring process shown includes, but is not limited to, steps S321 to S324: S321 obtains 10-cycle power quality set values ​​from Class A power quality analysis through solar power range and caches the latest 10-cycle power quality set values ​​that are longer than 10 minutes. S322: Wavelet analysis is used to identify jumps in the solar power range. If there is a jump in the solar power range, the 10-cycle power quality set value of the corresponding solar power range is cleared. If there is no jump in the solar power range, the 10-minute set value is calculated and the 10-cycle power quality set value cache is cleared when the number of 10-cycle set values ​​meets the requirement of counting 10-minute set values. In some embodiments, reference Figure 5 The diagram shown illustrates the transition recognition process, and its flow is as follows: A circular cache is used to store 10-cycle values ​​for a period of more than 10 minutes. Every 10-cycle interval, the latest multiple 10-cycle power sets are retrieved from the cache. :

[0050] in, The total number of power ensemble values ​​over 10 cycles is, for example, designed with 21 sampling points (including the current detection point and 10 points before and after it), and the total number of ensemble values. The value is 3000; read the power test data sequence within the test period. The power sequence is subjected to discrete wavelet transform based on a 4th-order Daubechies wavelet basis, with wavelet decomposition parameters set to 3 decomposition levels, as follows:

[0051] in For wavelet basis functions, For scale parameters, For the translation parameters, the embodiments of the present invention select the Daubechies wavelet series as the basis function, preferably the fourth-order Daubechies wavelet (db4). This wavelet basis has four vanishing moments, which have excellent detection sensitivity for step-type singularities. At the same time, its tight support characteristics ensure good time-domain localization ability, while orthogonality ensures energy conservation and facilitates multi-scale analysis.

[0052] In some embodiments, the wavelet decomposition parameters are set to 3 decomposition levels, the analysis window size is 21 sampling points (including the current detection point and 10 points before and after it), and the boundary processing adopts a symmetrical extension method to reduce boundary effects.

[0053] In some embodiments, the Mallat algorithm for multi-resolution analysis is used in the calculation to iteratively filter and downsample the power sequence through a set of low-pass and high-pass filters to obtain wavelet coefficients at each scale.

[0054] The second level detail coefficients obtained after wavelet decomposition To monitor the modulus maxima, when the first A point is considered an extreme point when it simultaneously satisfies the following conditions:

[0055]

[0056]

[0057] The adaptive threshold is calculated as follows:

[0058] in, The median of the absolute values ​​of the detail coefficients. The median is the absolute deviation between the detail coefficients and the median. 0.6745 is the Gaussian distribution correction coefficient. It should be noted that the coefficient set obtained after wavelet decomposition includes approximation coefficients and detail coefficients. Approximation coefficients represent the low-frequency components of the signal, while detail coefficients represent the high-frequency components. In this embodiment of the invention, the second-level detail coefficients are preferred as the main analysis object (achieving the optimal balance between noise suppression and positioning accuracy).

[0059] The method described in this embodiment of the invention can dynamically adjust the detection threshold based on the local statistical characteristics of the power sequence, adapting to the power fluctuation characteristics of photovoltaic power plants of different capacities.

[0060] To locate the power abrupt change point, the extreme points of the wavelet transform detail coefficients are mapped to the power sequence positions, as follows:

[0061] in The levels of wavelet decomposition, Location of the power abrupt change point. The method uses the extreme points of wavelet transform detail coefficients to dynamically adjust the detection threshold based on the local statistical characteristics of the power sequence, adapting to the power fluctuation characteristics of photovoltaic power plants of different capacities.

[0062] Based on the mapping results, if no power range transition occurs, determine whether the count of the current 10-cycle set value is greater than 1. If the value is greater than 10, a 10-minute set value calculation is performed, and the 10-cycle set value count is reset to zero. The calculation formula is as follows:

[0063] in, Counting the set values ​​for 10 cycles, Indicates the first A set of 10-cycle values.

[0064] S323, add a power range label to the 10-cycle set value according to the solar power range to which the 10-cycle set value belongs; S324, cache the 10-cycle set value with the added power range label to the solar power range cache and update the count. When the number of 10-minute set values ​​in all power ranges in the solar power range cache meets the requirements of the solar power quality assessment standard, perform quality assessment on the 10-minute set values ​​of all power ranges and clear all 10-minute set values ​​and counts to obtain the solar power quality assessment report.

[0065] S330, if the application scenario is conventional power quality monitoring, statistical analysis results of Class A power quality are performed to obtain a conventional power quality assessment report.

[0066] In some embodiments, the 10-minute set value, frequency, long-term flicker, and short-term flicker from the Class A power quality analysis results are received and cached. When the evaluation period is received, the 10-minute set value, frequency, long-term flicker, and short-term flicker are statistically analyzed according to the conventional power quality evaluation standards and a conventional power quality evaluation report is generated.

[0067] refer to Figure 6 ,in Figure 6This is a block diagram of a highly efficient new energy power quality monitoring system adapted to multiple scenarios according to an embodiment of the present invention. It includes: a synchronous sampling module, used to synchronously sample voltage and current signals using a frequency tracking method, and obtain and cache the sampled data; and an A-level power quality analysis module, used to perform A-level power quality analysis on the sampled data according to the application scenario, and obtain A-level power quality analysis results. The application scenarios include wind power quality monitoring, solar power quality monitoring, and conventional power quality monitoring. The data categories of the A-level power quality analysis results include set values, frequency, short-time flicker, and long-time flicker.

[0068] The new energy power quality monitoring module is used to select data categories from the Class A power quality analysis results based on the application scenario, and perform corresponding new energy power quality monitoring based on the data category selection results. It includes: a wind power quality assessment module, used when the application scenario is wind power quality monitoring, which divides the wind power output range based on the installed capacity of the wind farm, adds power range labels to the data category selection results based on the wind power range, statistically analyzes the Class A power quality analysis results using the wind power range and power range labels, and determines a wind power power quality assessment report based on the statistical results; a solar power quality assessment module, used when the application scenario is solar power quality monitoring, which divides the Class A power quality analysis results based on the solar power range and adds power range labels, statistically analyzes the Class A power quality analysis results using jump detection and dynamic data segmentation methods based on the solar power range and power range labels, and determines a solar power quality assessment report based on the statistical results; and a conventional power quality assessment module, used when the application scenario is conventional power quality monitoring, which statistically analyzes the Class A power quality analysis results to obtain a conventional power quality assessment report.

[0069] refer to Figure 7 and Figure 8 , Figure 7 This is a block diagram of another efficient new energy power quality monitoring system adapted to multiple scenarios according to an embodiment of the present invention. Figure 8 The module workflow of the monitoring system according to this embodiment of the invention is as follows: The monitoring system includes a synchronous sampling module, a sampling buffer module, a Class A power quality analysis module, an application scenario selection module, a solar power range automatic identification module, a solar power range buffer module, a solar power quality assessment module, a wind power range automatic identification module, a wind power range buffer module, a wind power quality assessment module, and a conventional power quality assessment module. The synchronous sampling module performs synchronous sampling of voltage and current with frequency tracking and transmits the data to the sampling buffer module for buffering. The Class A power quality analysis module, based on the application scenario, uses different methods to measure frequency, ensemble values, long-term flicker, and short-term flicker, and transmits different calculation results to the corresponding modules according to different application scenarios. When the application scenario is wind power quality monitoring, the solar power range automatic identification module receives the 10-minute ensemble values ​​from the Class A power quality analysis module, determines the power range based on the power of the 10-minute ensemble values, and labels the 10-minute ensemble values ​​with the corresponding power range. The wind power range caching module receives 10-minute aggregate values ​​from wind power ranges and stores them in the corresponding cache according to the power range label. When all caches are full, all 10-minute aggregate values ​​are transmitted to the wind power quality assessment module for statistical analysis and report generation. The wind power quality assessment module receives the 10-minute aggregate values ​​from the wind power range caching module, performs statistical analysis on the 10-minute aggregate values ​​of each power range, compares them with standard limits, and generates a wind power quality assessment report. When the application scenario is solar power quality monitoring, the solar power range automatic identification module receives and caches 10-cycle aggregate values ​​from the Class A power quality analysis module. Upon receiving a 10-cycle aggregate value, a wavelet-based power range transition detection method is used to detect power range transitions. When a transition is detected, the cache and the number of 10-cycle aggregate values ​​are cleared. When the number of 10-cycle aggregate values ​​is greater than or equal to the number of 10-minute aggregate values, the root mean square value method is used to calculate the 10-minute aggregate value, and the corresponding power range label is added according to the power. The solar power range caching module receives 10-minute aggregate values ​​from the solar power range automatic identification module, stores them in the corresponding cache according to the power range label, and counts them. When the number of 10-minute aggregate values ​​for all power ranges meets the evaluation standard requirements, all 10-minute aggregate values ​​are transmitted to the solar power quality assessment module to generate a statistical evaluation report. The solar power quality assessment module receives 10-minute aggregate values ​​from the solar power range caching module, performs statistics on the 10-minute aggregate values ​​for all power ranges, and compares them with standard limits to generate a solar power quality assessment report for each power range. When the application scenario is conventional power quality assessment, the module receives 10-minute aggregate values, frequency, short-term flicker, and long-term flicker from the Class A power quality analysis module, and performs statistics on all power range values ​​and compares them with standard limits at the end of the evaluation period to generate a conventional power quality assessment report.

[0070] For example, data acquisition and Class A analysis include: after system startup, the synchronous sampling module synchronously samples the analog signal at a sampling rate of N points per cycle (e.g., 2048 points) and stores the data in the sampling buffer module. The Class A power quality analysis module reads the buffered data and performs calculations. Its internal process is as follows: The frequency measurement module receives voltage signals and synchronously measures the system frequency at 10-second intervals.

[0071] The set value calculation module calculates set values ​​such as 10-cycle, 150-cycle, 10-minute, and 2-hour values ​​based on the synchronization signal provided by the absolute 10-minute synchronization module. These values ​​include voltage, current, power, harmonics (up to 100th order), interharmonics, and unbalance.

[0072] For flicker processing, since the application scenario is solar energy, the instantaneous flicker visual sensitivity synchronization signal selection module receives power jump signals that may be emitted by the solar power range automatic identification module. When a power jump occurs, the instantaneous flicker visual sensitivity caching module clears the cached instantaneous flicker visual sensitivity data and restarts caching, ensuring the purity of the power range in the flicker statistics. When the cached instantaneous flicker visual sensitivity is sufficient to calculate a complete 10-minute short-term flicker (Pst) value, it is passed to the flicker statistics evaluation module.

[0073] The data output and power quality output selection module transmits the 10-cycle set value (including active power) and the calculated short-time flicker and long-time flicker values ​​to the solar power range automatic identification module according to the solar scenario.

[0074] In some embodiments, such as Figure 9 The diagram shows the grid connection point of a photovoltaic power station. In this embodiment of the invention, the monitoring system first connects to the grid connection point of the photovoltaic power station via a synchronous sampling module to collect voltage and current signals. The user selects the "Solar Power Quality Monitoring" mode in the application scenario selection module and sets the rated power of the photovoltaic power station, the power range division (e.g., 10% of the rated power as one range), and the minimum number of valid samples required for each range (according to relevant standards).

[0075] refer to Figure 10 ,in Figure 10The workflow of the Class A power quality analysis module in this embodiment of the invention is as follows: The Class A power quality analysis module analyzes and calculates aggregate values ​​(including 10-cycle, 150-cycle, 10-minute, and 2-hour aggregate values ​​of voltage, current, power, harmonics, interharmonics, unbalance, and sequence components), frequency, short-time flicker, and long-time flicker according to the Class A measurement method of IEC 61000-4-30. The Class A power quality analysis module receives the application scenario from the application scenario module. When the application scenario is conventional or wind power quality monitoring, flicker is measured in whole 10-minute intervals of UTC according to the Class A power quality requirements of IEC 61000-4-15. When the application scenario is solar power quality monitoring, after receiving a power jump signal, the Class A power quality module clears the instantaneous flicker sensitivity buffer and count, and restarts the instantaneous flicker sensitivity buffer until the number of buffered instantaneous flicker sensitivity values ​​meets the 10-minute requirement, after which short-time flicker and long-time flicker statistics are performed. The Class A power quality analysis module transmits different data from the application scenario selected by the application scenario selection module, including 10-cycle set value, 10-minute set value, short-time flicker, long-time flicker, and frequency, to the solar power range automatic identification module, the wind power range automatic identification module, or the conventional power quality assessment module.

[0076] Specifically, the workflow of the Class A power quality analysis module is further shown below: The frequency measurement module receives the voltage analog signal and performs synchronous frequency measurement at 10-second intervals to obtain the system frequency over 10 seconds.

[0077] The absolute 10-minute synchronization module performs absolute 10-minute synchronization of the sampled value windows of 10-cycle, 150-cycle, 10-minute, and 2-hour ensemble values ​​according to the Class A requirements of IEC 61000-4-30, and transmits the absolute 10-minute synchronization signal to the instantaneous flicker visual perception synchronization signal selection module.

[0078] The ensemble value calculation module receives the absolute 10-minute synchronization sampling data output by the absolute 10-minute synchronization module and calculates the 10-cycle, 150-cycle, 10-minute, and 2-hour ensemble values ​​according to IEC 61000-4-30 Class A requirements. The ensemble values ​​include voltage, power, harmonics, interharmonics, unbalance, and sequence components.

[0079] The instantaneous flicker visual sensitivity synchronization signal selection module receives the absolute 10-minute synchronization signal from the absolute 10-minute synchronization module, the application scenario signal from the application scenario selection module, and the solar power range jump signal from the solar power range automatic identification module. When the application scenario is wind power quality measurement or conventional power quality measurement, the instantaneous flicker visual sensitivity synchronization signal selection module inputs the absolute 10-minute synchronization signal into the instantaneous flicker visual sensitivity buffer module for synchronization. When the application scenario is solar power quality monitoring, the instantaneous flicker visual sensitivity synchronization signal selection module transmits the solar power range jump signal to the instantaneous flicker visual sensitivity buffer module for synchronization.

[0080] The instantaneous flicker visual perception calculation module receives the synchronization sampling value output by the absolute 10-minute synchronization module and calculates the instantaneous flicker visual perception according to the Class A requirements of IEC61000-4-15.

[0081] When the application scenario is wind power quality monitoring or conventional power quality monitoring, the transient flicker sensitivity caching module clears its cache and restarts caching transient flicker sensitivity after receiving an absolute 10-minute synchronization signal. Upon receiving the next 10-minute synchronization signal, it transmits all cached transient flicker sensitivity to the flicker statistics and evaluation module for statistical evaluation of flicker. When the application scenario is solar power quality monitoring, the transient flicker sensitivity caching module clears its cached transient flicker sensitivity upon receiving a solar power range jump signal. When the transient flicker sensitivity caching module receives transient flicker sensitivity, it determines whether the number of cached transient flicker sensitivity values ​​is greater than or equal to the number of transient flicker sensitivity values ​​within 10 minutes. If it is greater than or equal to the number of transient flicker sensitivity values ​​within 10 minutes, it transmits all cached transient flicker sensitivity values ​​to the flicker evaluation module for statistical evaluation of short-term and long-term flicker and then clears the transient flicker sensitivity values.

[0082] In some embodiments, the transition processing flow of this invention is as follows: based on the above wavelet decomposition processing flow, after finding candidate points, for each candidate point; (1) Construct an analysis window with 21 sampling points centered on the mapping location, and calculate the first difference of the power sequence within the window: ΔP[i]=P[i+1]-P[i], i=start,...,end-1 (2) Identification of the maximum jump point: Within the analysis window, find the position with the largest absolute value of the first-order difference sequence. This position is the location of the singular point after precise localization. i_max=argmax|ΔP[i]| (3) Verify the significance of the jump, eliminate spurious jumps caused by noise, and verify the significance of the detected singularities; calculate the average power of the 5 sampling points before and after the jump point: P_before=mean(P[i-5:i]) P_after=mean(P[i+1:i+6]) The jump amplitude is: A = |P_after - P_before| Set a dynamic threshold based on local standard deviation: T_magnitude=k×σ_local Where σ_local is the standard deviation of the power sequence within the analysis window, and k is an empirical coefficient (usually taken as 2.0). When A > T_magnitude, it is considered a significant jump.

[0083] The flicker statistical evaluation module receives the instantaneous flicker visual sensitivity from the instantaneous flicker visual sensitivity buffer module and performs statistical evaluation of short-term and long-term flicker, thus obtaining the following... Figure 11 Power jump effect based on wavelet transform and Figure 12 The effect diagram of power jump point detection based on wavelet transform.

[0084] When the application scenario is wind power quality monitoring, the power quality output selection module obtains the 10-minute ensemble value from the ensemble value calculation module and the short-time flicker and long-time flicker from the flicker statistics evaluation module, and then transmits them to the wind power range automatic identification module. When the application scenario is solar power quality monitoring, the power quality output selection module obtains the 10-cycle ensemble value from the ensemble value calculation module and the short-time flicker and long-time flicker from the flicker statistics evaluation module, and then transmits them to the solar power range automatic identification module. When the application scenario is conventional power quality monitoring, the power quality output selection module obtains the 10-second frequency from the frequency measurement module, the 10-minute ensemble value from the ensemble value calculation module, and the short-time flicker and long-time flicker from the flicker statistics evaluation module, and then transmits them to the conventional power quality evaluation module.

[0085] In some embodiments, power range buffering and progress tracking include: The solar power range buffer module receives a 10-minute aggregate of values ​​(including all power quality indicators such as voltage, harmonics, and flicker) tagged with power ranges from the previous step. Internally, the module maintains an independent FIFO buffer and a counter for each power range. It stores the received data into the buffer corresponding to the power range and increments the counter by 1.

[0086] In this embodiment of the invention, the system can display the sample count of each power range in real time via an LCD screen or a remote web page. When the sample count of all preset power ranges meets the standard requirements (Table 3), the solar power range caching module triggers the next step.

[0087] Table 3 Standard requirements table for power intervals

[0088] In some embodiments, automatic assessment and report generation are performed as follows: The solar power quality assessment module receives complete data sets of all power intervals, and for each power interval, it performs the following statistical calculations: calculating the maximum value, minimum value, average value and 95% probability value (95th percentile value) of each power quality index (such as total harmonic distortion THD, voltage unbalance, etc.); comparing the statistical results with the Grid-connection Agreement built in the system or relevant national standard limit values, and clearly identifying which indexes in which power intervals exceed the standards; finally, automatically generating a structured power quality assessment report divided by power intervals, which includes data statistical statements, trend graphs and clear conformity conclusions.

[0089] In another embodiment, the power interval power quality test for wind farms is similar to the previous embodiment, but differs in the power interval identification method, because wind power fluctuations are relatively gentle.

[0090] (1) Scenario selection and data acquisition A user selects the "wind power quality monitoring" mode in the application scenario selection module. The working process of the Class A power quality analysis module is similar to that of the conventional mode, and the absolute 10-minute synchronization module directly provides synchronization signals for flicker measurement and aggregate value calculation. The power quality output selection module transmits 10-minute aggregate values (including average active power) to the wind power interval automatic identification module.

[0091] (2) Power interval identification The wind power interval automatic identification module receives 10-minute aggregate values from the Class A power quality analysis module. The module is pre-set with a power interval threshold table based on the installed capacity PN of the wind farm, for example: interval 1: [0%, 10%), threshold 10%; interval 2: [10%, 20%), threshold 20%; ..., up to interval 8: [70%, 80%), threshold 80%. When a 10-minute aggregate value is received, the module directly compares its 10-minute average power P_10min with each interval threshold. If P_{i-min}≤P_10min<P_{i-max} is satisfied, the 10-minute data is determined to belong to power interval i, and marked with a corresponding label.

[0092] (3) Caching, statistics and report generation The wind power interval caching module (its structure is shown in Figure 13The schematic diagram of the power range caching module shown indicates that it receives 10 minutes of tagged data and stores it in the corresponding cache area according to the tag, while updating the counter. Using the power range vs. threshold lookup table (Table 4), once the sample counts for all eight power ranges reach the minimum required number (e.g., five), the module transmits all data to the wind power quality assessment module. This module performs statistical analysis on the power quality data for each power range in a manner similar to that in Example 1, compares it with the limit values, and ultimately generates a wind farm power quality assessment report for each power range.

[0093] Table 4 Power Range and Threshold Comparison Table

[0094] The two embodiments described above clearly demonstrate how the system and method of the present invention solves the technical problems of low efficiency, long cycle, and high dependence on manual labor in traditional methods through intelligent power range identification, dynamic data management, and automated evaluation processes in two typical new energy scenarios: photovoltaic and wind power. This achieves efficient, intelligent, and multi-scenario adaptable new energy power quality monitoring.

[0095] It should be understood that various parts of the present invention can be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0096] In the description of this specification, references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0097] Although embodiments of the invention have been shown and described, those skilled in the art will understand that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the claims and their equivalents.

[0098] The above is a detailed description of the preferred embodiments of the present invention, but the present invention is not limited to the embodiments described. Those skilled in the art can make various equivalent modifications or substitutions without departing from the spirit of the present invention, and these equivalent modifications or substitutions are all included within the scope defined by the claims of this application.

Claims

1. A highly efficient new energy power quality monitoring method adaptable to multiple scenarios, characterized in that, include: The voltage and current signals are synchronously sampled using a frequency tracking method to obtain and buffer the sampled data. Based on the application scenario, the sampled data is subjected to Class A power quality analysis to obtain Class A power quality analysis results. The application scenarios include wind power power quality monitoring, solar power quality monitoring and conventional power quality monitoring. The data categories of the Class A power quality analysis results include set values, frequency, short-time flicker and long-time flicker. Based on the application scenario, data categories are selected from the Class A power quality analysis results. Then, based on the data category selection results, the new energy power quality monitoring is performed as follows: If the application scenario is wind power quality monitoring, the wind power range is divided according to the installed capacity of the wind farm, and power range labels are added to the data category selection results according to the wind power range. The A-level power quality analysis results are statistically analyzed by wind power range and power range labels, and the wind power quality assessment report is determined based on the statistical results. If the application scenario is solar power quality monitoring, the A-level power quality analysis results are divided according to the solar power range and power range labels are added. The A-level power quality analysis results are statistically analyzed using jump detection and dynamic data segmentation methods based on the solar power range and power range labels. The solar power quality assessment report is determined based on the statistical results. If the application scenario is routine power quality monitoring, the analysis results of Class A power quality are statistically analyzed to obtain a routine power quality assessment report.

2. The efficient new energy power quality monitoring method adaptable to multiple scenarios according to claim 1, characterized in that, The sampling interval for synchronous sampling is obtained by the following formula: ； in, The sampling interval is... This represents the number of sampling points per wave. This refers to the system frequency.

3. The efficient new energy power quality monitoring method adaptable to multiple scenarios according to claim 1, characterized in that, The step of performing a Class A power quality analysis on the sampled data according to the application scenario to obtain the Class A power quality analysis results includes: The voltage analog signal is acquired and frequency measurement is performed synchronously at 10-second intervals to obtain the system frequency over 10 seconds. According to the Class A requirements of IEC 61000-4-30, absolute 10-minute synchronization is performed on the sampling value windows of 10-cycle, 150-cycle, 10-minute and 2-hour ensemble values ​​to obtain an absolute 10-minute synchronization signal; The absolute 10-minute synchronization sampling data is obtained based on the absolute 10-minute synchronization signal. The 10-cycle, 150-cycle, 10-minute and 2-hour aggregate values ​​are calculated based on the absolute 10-minute synchronization sampling data according to the IEC 61000-4-30 Class A requirements. The aggregate values ​​include voltage, power, harmonics, interharmonics, unbalance and sequence components. The instantaneous flicker visual sensitivity is calculated and cached based on the sampled data, and the solar power range transition signal is acquired. If the application scenario is wind power quality measurement and conventional power quality measurement, the cached instantaneous flicker visual sensitivity is synchronized based on the absolute 10-minute synchronization signal, the application scenario, and the solar power range transition signal. If the application scenario is solar power quality monitoring, the solar power range transition signal is synchronized with the cached instantaneous flicker visual sensitivity. When the application scenario is wind power quality monitoring or conventional power quality monitoring, the instantaneous flicker visual sensitivity buffer is cleared and re-buried based on the absolute 10-minute synchronization signal; after receiving the next 10-minute synchronization signal, the instantaneous flicker visual sensitivity of all buffered buffers is statistically evaluated for flicker; when monitoring solar power quality, when receiving instantaneous flicker visual sensitivity, it is determined whether the number of buffered instantaneous flicker visual sensitivity is greater than or equal to the number of instantaneous flicker visual sensitivity in 10 minutes. If so, the short-term flicker and long-term flicker evaluations of all buffered instantaneous flicker visual sensitivity are performed and the buffered instantaneous flicker visual sensitivity is cleared. When the application scenario is wind power quality monitoring, the 10-minute ensemble value, short-time flicker, and long-time flicker are automatically identified based on the wind power range. When the application scenario is solar power quality monitoring, the 10-cycle ensemble value, short-time flicker, and long-time flicker are automatically identified. When the application scenario is conventional power quality monitoring, the 10-second frequency, 10-minute ensemble value, short-time flicker, and long-time flicker are automatically identified.

4. The efficient new energy power quality monitoring method adaptable to multiple scenarios according to claim 3, characterized in that, When the application scenario is wind power quality monitoring, the wind power quality assessment report is obtained through the following steps: Obtain the 10-minute power quality set values ​​with power range labels and send them to the wind power range cache for counting. The wind power range cache includes the power range label, the number of 10-minute set values, and the 10-minute set value cache. If the count of the wind power range meets the requirements of the wind power quality assessment standard, the 10-minute aggregate value of all power ranges is assessed to obtain a wind power quality assessment report, and then the 10-minute aggregate value and count of all power ranges are cleared.

5. The efficient new energy power quality monitoring method adaptable to multiple scenarios according to claim 3, characterized in that, When the application scenario is solar power quality monitoring, the solar power quality assessment report is obtained through the following steps: The 10-cycle power quality set value is obtained from the Class A power quality analysis by solar power range, and the latest 10-cycle power quality set value greater than 10 minutes is cached. Wavelet analysis is used to identify jumps in the solar power range. If a jump exists in the solar power range, the 10-cycle power quality set value for the corresponding solar power range is cleared. If there is no jump in the solar power range, the 10-minute set value is calculated and the 10-cycle power quality set value cache is cleared when the number of 10-cycle set values ​​meets the requirement of counting 10-minute set values. Add a power range label to the 10-cycle set value based on the solar power range to which it belongs; The 10-cycle set values ​​with the added power range label are cached in the solar power range cache and the count is updated. When the number of 10-minute set values ​​in all power ranges in the solar power range cache meets the requirements of the solar power quality assessment standard, the 10-minute set values ​​of all power ranges are assessed for quality and all 10-minute set values ​​and counts are cleared to obtain the solar power quality assessment report.

6. The efficient new energy power quality monitoring method adaptable to multiple scenarios according to claim 5, characterized in that, The method of using wavelet analysis to identify jumps in the solar power range includes: A circular cache is used to store 10-cycle values ​​for a period of more than 10 minutes. Every 10-cycle interval, the latest multiple 10-cycle power sets are retrieved from the cache. : ； in, This represents the total number of power ensemble values ​​for 10 cycles. For power sequences Discrete wavelet transform based on a 4th-order Daubechies wavelet basis is performed, with wavelet decomposition parameters set to three decomposition levels, as follows: ； in For wavelet basis functions, For scale parameters, These are translation parameters; The second level detail coefficients obtained after wavelet decomposition To monitor the modulus maxima, the i-th point is considered an extreme point when it simultaneously satisfies the following conditions: ； ； ； The adaptive threshold is calculated as follows: ； in, The median of the absolute values ​​of the detail coefficients. is the median of the absolute deviation between the detail coefficients and the median, and 0.6745 is the Gaussian distribution correction coefficient; To locate the power abrupt change point, the extreme points of the wavelet transform detail coefficients are mapped to the power sequence positions, as follows: ； in The levels of wavelet decomposition, Location of the power abrupt change point. This indicates the location of the extreme points of the wavelet transform detail coefficients; Based on the mapping results, if no power range jump occurs, it is determined whether the current 10-cycle set value count is greater than 3000. If it is, the 10-minute set value is calculated, and the 10-cycle set value count is reset to zero. The calculation formula is as follows: ； in, Counting the set values ​​for 10 cycles, Indicates the first A set of 10-cycle values.

7. The efficient new energy power quality monitoring method adaptable to multiple scenarios according to claim 6, characterized in that, When the application scenario is routine power quality monitoring, the routine power quality assessment report is obtained through the following steps: It receives and caches the 10-minute set value, frequency, long-term flicker, and short-term flicker from the Class A power quality analysis results. During the evaluation period, it performs statistics on the 10-minute set value, frequency, long-term flicker, and short-term flicker according to the conventional power quality evaluation standards and generates a conventional power quality evaluation report.

8. A highly efficient new energy power quality monitoring system adaptable to multiple scenarios, characterized in that, include: The synchronous sampling module is used to synchronously sample voltage and current signals using frequency tracking, and to obtain and buffer the sampled data. The Class A power quality analysis module is used to perform Class A power quality analysis on the sampled data according to the application scenario, and obtain Class A power quality analysis results. The application scenarios include wind power power quality monitoring, solar power quality monitoring and conventional power quality monitoring. The data categories of the Class A power quality analysis results include set values, frequency, short-time flicker and long-time flicker. The new energy power quality monitoring module is used to select data categories from the Class A power quality analysis results according to the application scenario, and to perform corresponding new energy power quality monitoring based on the data category selection results. It includes: The wind power quality assessment module is used when the application scenario is wind power quality monitoring. It divides the wind power range according to the installed capacity of the wind farm, adds power range labels to the data category selection results according to the wind power range, statistically analyzes the Class A power quality results through the wind power range and power range labels, and determines the wind power quality assessment report based on the statistical results. The solar power quality assessment module is used when the application scenario is solar power quality monitoring. It divides the Class A power quality analysis results according to the solar power range and adds power range labels. It uses jump detection and dynamic data segmentation methods to statistically analyze the Class A power quality analysis results through the solar power range and power range labels, and determines the solar power quality assessment report based on the statistical results. The conventional power quality assessment module is used to statistically analyze the results of Class A power quality when the application scenario is conventional power quality monitoring, and to obtain a conventional power quality assessment report.

Images