Power quality evaluation method, device, equipment, storage medium and program product

Abstract

The application relates to a power quality evaluation method, device, equipment, storage medium and program product. The method comprises the following steps: in response to a power quality evaluation request for a target power grid, obtaining historical operation data of the target power grid, service quality data associated with the target power grid, and original power data of the target power grid in each sampling period; processing each original power data to obtain power analysis data; determining power evaluation parameters, power supply quality parameters and user evaluation parameters according to the original power data, the power analysis data, the historical operation data and the service quality data; and determining a power quality evaluation result of the target power grid according to the power evaluation parameters, the power supply quality parameters and the user evaluation parameters. The method can improve the comprehensiveness and reliability of power quality evaluation.

Description

Technical Field

[0001] This application relates to the field of power distribution network technology, and in particular to a power quality assessment method, apparatus, equipment, storage medium, and program product. Background Technology

[0002] The large-scale integration of intermittent and random distributed renewable energy sources under the new energy structure has led to increasingly complex distribution network structures and more prominent power quality issues. To more accurately assess power quality in distribution networks, power quality assessment methods have emerged. Current power quality assessment methods generally determine power quality based on voltage fluctuations within the distribution network.

[0003] However, the above method ignores the social and economic impact of power quality, thus reducing the comprehensiveness and reliability of power quality assessment. Summary of the Invention

[0004] Therefore, it is necessary to provide a power quality assessment method, apparatus, equipment, storage medium, and program product that can improve the comprehensiveness and reliability of power quality assessment in response to the above-mentioned technical problems.

[0005] In a first aspect, this application provides a power quality assessment method, including:

[0006] In response to a power quality assessment request for the target power grid, historical operating data of the target power grid, service quality data associated with the target power grid, and raw power data of the target power grid for each sampling period are obtained.

[0007] The raw electrical energy data are processed to obtain electrical energy analysis data;

[0008] Based on the original power data, power analysis data, historical operation data and service quality data, determine the power assessment parameters, power supply quality parameters and user evaluation parameters;

[0009] Based on power assessment parameters, power supply quality parameters, and user evaluation parameters, the power quality assessment results of the target power grid are determined.

[0010] In one embodiment, the power analysis data includes local analysis data for each sampling period; the raw power data is processed to obtain the power analysis data, including:

[0011] For the raw power data in each sampling period, at least two sets of spectrum data in different sampling intervals are determined based on the raw power data; based on the spectrum data in each sampling interval, local analysis data for the sampling period is determined; wherein, the local analysis data includes at least one of the minimum, maximum and average values ​​of harmonic components.

[0012] In one embodiment, based on the raw power data, at least two sets of spectral data within different sampling intervals are determined, including:

[0013] Extract target power data within the target frequency band from the original power data; extract at least two sampling intervals at equal intervals from the sampling period; for each sampling interval, obtain discrete power data from the target power data located within the sampling interval according to the preset sampling rate, and perform discrete Fourier transform processing on the discrete power data to obtain the spectrum data within the sampling interval.

[0014] In one embodiment, based on raw power data, power analysis data, historical operating data, and service quality data, power assessment parameters, power supply quality parameters, and user evaluation parameters are determined, including:

[0015] Based on standard power information, various raw power data, and power analysis data, power assessment parameters are determined; among them, power assessment parameters include at least one of voltage qualification rate, harmonic qualification rate, three-phase imbalance qualification rate, and frequency quality; based on historical operating data, power supply quality parameters are determined; among them, power supply quality parameters include at least one of power supply safety parameters, power supply reliability parameters, and voltage quality parameters; based on service quality data, user evaluation parameters are determined.

[0016] In one embodiment, the power quality assessment result of the target power grid is determined based on power assessment parameters, power supply quality parameters, and user evaluation parameters, including:

[0017] Based on the evaluation information of each user for different evaluation dimensions, a subjective judgment matrix relating each evaluation dimension is constructed, and the subjective judgment matrix is ​​calculated to obtain the subjective weight values ​​corresponding to each evaluation dimension. The evaluation dimensions include power evaluation parameter dimensions, power supply quality parameter dimensions, and user evaluation parameter dimensions. Based on the original power data, historical operation data, and service quality data, the dynamic weight values ​​corresponding to each evaluation dimension are determined. For each evaluation dimension, the subjective weight values ​​corresponding to the evaluation dimension are processed using the dynamic weight values ​​corresponding to the evaluation dimension to obtain the comprehensive weight value corresponding to the evaluation dimension. Finally, the power evaluation parameters, power supply quality parameters, and user evaluation parameters are weighted and summed using the comprehensive weight values ​​corresponding to each evaluation dimension to obtain the power quality evaluation result of the target power grid.

[0018] In one embodiment, dynamic weight values ​​corresponding to each evaluation dimension are determined based on the original power data, historical operation data, and service quality data, including:

[0019] Based on the time-series power data corresponding to the original power data, determine the power information entropy; based on the time-series operation data corresponding to the historical operation data, determine the operation information entropy; based on the time-series quality data corresponding to the service quality data, determine the quality information entropy; based on the power information entropy, operation information entropy, and quality information entropy, determine the dynamic weight values ​​corresponding to each evaluation dimension.

[0020] Secondly, this application also provides a power quality assessment device, comprising:

[0021] The data acquisition module is used to respond to the power quality assessment request for the target power grid by acquiring the historical operating data of the target power grid, the service quality data associated with the target power grid, and the raw power data of the target power grid for each sampling period.

[0022] The data analysis module is used to process the raw electrical energy data to obtain electrical energy analysis data;

[0023] The parameter determination module is used to determine power assessment parameters, power supply quality parameters, and user evaluation parameters based on various raw power data, power analysis data, historical operation data, and service quality data.

[0024] The result determination module is used to determine the power quality assessment result of the target power grid based on power assessment parameters, power supply quality parameters, and user evaluation parameters.

[0025] Thirdly, this application also provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to perform the following steps:

[0026] In response to a power quality assessment request for the target power grid, historical operating data of the target power grid, service quality data associated with the target power grid, and raw power data of the target power grid for each sampling period are obtained.

[0027] The raw electrical energy data are processed to obtain electrical energy analysis data;

[0028] Based on the original power data, power analysis data, historical operation data and service quality data, determine the power assessment parameters, power supply quality parameters and user evaluation parameters;

[0029] Based on power assessment parameters, power supply quality parameters, and user evaluation parameters, the power quality assessment results of the target power grid are determined.

[0030] Fourthly, this application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, performs the following steps:

[0031] In response to a power quality assessment request for the target power grid, historical operating data of the target power grid, service quality data associated with the target power grid, and raw power data of the target power grid for each sampling period are obtained.

[0032] The raw electrical energy data are processed to obtain electrical energy analysis data;

[0033] Based on the original power data, power analysis data, historical operation data and service quality data, determine the power assessment parameters, power supply quality parameters and user evaluation parameters;

[0034] Based on power assessment parameters, power supply quality parameters, and user evaluation parameters, the power quality assessment results of the target power grid are determined.

[0035] Fifthly, this application also provides a computer program product, including a computer program that, when executed by a processor, performs the following steps:

[0036] In response to a power quality assessment request for the target power grid, historical operating data of the target power grid, service quality data associated with the target power grid, and raw power data of the target power grid for each sampling period are obtained.

[0037] The raw electrical energy data are processed to obtain electrical energy analysis data;

[0038] Based on the original power data, power analysis data, historical operation data and service quality data, determine the power assessment parameters, power supply quality parameters and user evaluation parameters;

[0039] Based on power assessment parameters, power supply quality parameters, and user evaluation parameters, the power quality assessment results of the target power grid are determined.

[0040] The aforementioned power quality assessment methods, apparatus, equipment, storage media, and program products, in response to a power quality assessment request for a target power grid, acquire historical operating data of the target power grid, associated service quality data of the target power grid, and raw power data of the target power grid for each sampling period. They then process the raw power data to obtain power analysis data. Subsequently, based on the raw power data, power analysis data, historical operating data, and service quality data, they determine power assessment parameters, power supply quality parameters, and user evaluation parameters. Finally, based on these parameters, the power quality assessment result for the target power grid is determined. Compared to related technologies that only analyze power data, the above method analyzes data from multiple dimensions, including power data, historical operating data, and service quality data, to obtain power quality assessment results. This allows the power quality assessment results to include the social and economic impact of power quality, thereby improving the comprehensiveness and reliability of the power quality assessment. Attached Figure Description

[0041] To more clearly illustrate the technical solutions in the embodiments of this application or related technologies, the drawings used in the description of the embodiments of this application or related technologies will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0042] Figure 1 This is a flowchart illustrating a power quality assessment method in one embodiment;

[0043] Figure 2 This is a schematic diagram of the process for determining power analysis data in one embodiment;

[0044] Figure 3 This is a flowchart illustrating the process of determining parameters for each dimension in one embodiment;

[0045] Figure 4 This is an architecture diagram of a power quality assessment system in one embodiment;

[0046] Figure 5 This is a schematic diagram of the process for determining power quality assessment results in one embodiment;

[0047] Figure 6 This is a schematic diagram of the evaluation model framework in one embodiment;

[0048] Figure 7 This is a hardware architecture block diagram of a voltage and current integrated sensor in one embodiment;

[0049] Figure 8 This is a software architecture block diagram of a voltage and current integrated sensor in one embodiment;

[0050] Figure 9 This is a flowchart illustrating the power quality assessment method in another embodiment;

[0051] Figure 10 This is a structural block diagram of a power quality assessment device in one embodiment;

[0052] Figure 11 This is an internal structural diagram of a computer device in one embodiment. Detailed Implementation

[0053] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0054] The large-scale integration of intermittent and random distributed renewable energy sources under the new energy structure has led to increasingly complex distribution network structures and more prominent power quality issues. To more accurately assess power quality in distribution networks, power quality assessment methods have emerged. Current power quality assessment methods generally determine power quality based on voltage fluctuations within the distribution network.

[0055] However, the above method ignores the social and economic impact of power quality, thus reducing the comprehensiveness and reliability of power quality assessment.

[0056] Based on this, in an exemplary embodiment, a power quality assessment method is provided. This embodiment illustrates the application of this method to a server, but it is understood that the method can also be applied to a terminal, and further to a system including both a terminal and a server, and implemented through the interaction between the terminal and the server. Figure 1 As shown, the specific steps include:

[0057] S101, in response to a power quality assessment request for the target power grid, acquires historical operating data of the target power grid, service quality data associated with the target power grid, and raw power data of the target power grid for each sampling period.

[0058] The target power grid refers to the distribution network that requires power quality assessment. A power quality assessment request triggers an assessment of the power quality of the target power grid. Historical operational data can be understood as relevant data generated by the target power grid during historical periods, including but not limited to fault-related data and outage information for various power equipment. Service quality data can be understood as data related to the power supply services of companies connected to the target power grid, including but not limited to user complaints and survey data.

[0059] The sampling period is the period during which electrical energy data is collected. Each sampling period has the same duration; for example, each sampling period can be 10 cycles (200ms) or 12 cycles (240ms). Raw electrical energy data can be understood as electrical energy data directly collected from the target power grid, such as voltage and current signals.

[0060] Optionally, when the operations and maintenance team needs to assess the power quality of the target power grid, it can send a power quality assessment request to the server. In response to the power quality assessment request, the server can retrieve historical operational data and associated service quality data of the target power grid from the database based on the grid identifier, and acquire raw power data at each sampling time period using a sensor architecture deployed in the target power grid, according to preset time intervals.

[0061] S102 processes the raw electrical energy data to obtain electrical energy analysis data.

[0062] Among them, power analysis data can be understood as data that can characterize the results of power analysis.

[0063] Optionally, the raw power data from each sampling period can be analyzed to obtain power analysis data. For example, for current signals, parameters such as the fundamental RMS value, waveform distortion rate, and transient peak value can be calculated as the corresponding power analysis data. Alternatively, for voltage signals, parameters such as the RMS value, peak value, frequency, and phase angle can be calculated as the corresponding power analysis data. Or, a Fast Fourier Transform (FFT) can be performed on the current and voltage signals to obtain parameters such as the 2nd to 50th harmonic content and total harmonic distortion (THD), which can then be used as the corresponding power analysis data.

[0064] S103. Based on the original power data, power analysis data, historical operation data, and service quality data, determine the power assessment parameters, power supply quality parameters, and user evaluation parameters.

[0065] Among these, power assessment parameters can be understood as numerical values ​​characterizing power quality after evaluating power data. Power supply quality parameters can be understood as numerical values ​​characterizing power quality after evaluating historical operating data. User evaluation parameters can be understood as numerical values ​​characterizing power quality after evaluating service quality data.

[0066] Optionally, power assessment parameters can be determined by combining various raw power data and power analysis data; power supply quality parameters can be determined based on historical operating data; and user evaluation parameters can be determined based on service quality data.

[0067] For example, raw power data and power analysis data can be input into a trained power assessment model, which then outputs power assessment parameters based on the raw power data and power analysis data. Historical operating data can be input into a trained power supply quality assessment model, which then outputs power supply quality parameters based on the historical operating data. Service quality data can be input into a trained user evaluation model, which then outputs user evaluation parameters based on the service quality data.

[0068] S104. Based on the power assessment parameters, power supply quality parameters, and user evaluation parameters, determine the power quality assessment results of the target power grid.

[0069] Among them, the power quality assessment result can be understood as the overall assessment result obtained after multi-dimensional analysis of the target power grid.

[0070] In one optional implementation, power quality assessment parameters can be calculated based on power assessment parameters, power supply quality parameters, and user evaluation parameters; then, candidate levels matching the power quality assessment parameters are used as the power quality assessment results. These candidate levels include Excellent, Good, Moderate, Satisfactory, and Unsatisfactory.

[0071] For example, power quality assessment parameters can be compared with the parameter range associated with each candidate level, and then the candidate level corresponding to the parameter range containing the power quality assessment parameters can be used as the power quality assessment result.

[0072] In another alternative implementation, candidate levels corresponding to power assessment parameters, power supply quality parameters, and user evaluation parameters can be determined separately. Then, by combining the candidate levels corresponding to power assessment parameters, power supply quality parameters, and user evaluation parameters, a target level is determined as the power quality assessment result of the target power grid.

[0073] For example, the lowest candidate level among the candidate levels corresponding to each parameter can be used as the target level; or, the candidate levels corresponding to each parameter can be weighted based on the historical impact of each parameter on the evaluation results to obtain the target level.

[0074] In the aforementioned power quality assessment method, in response to a power quality assessment request for a target power grid, historical operational data of the target power grid, associated service quality data of the target power grid, and raw power data of the target power grid for each sampling period are acquired. The raw power data is then processed to obtain power analysis data. Subsequently, based on the raw power data, power analysis data, historical operational data, and service quality data, power assessment parameters, power supply quality parameters, and user evaluation parameters are determined. Finally, based on these parameters, the power quality assessment result for the target power grid is determined. Compared to related technologies that only analyze power data, the above method analyzes data from multiple dimensions, including power data, historical operational data, and service quality data, to obtain power quality assessment results. This allows the power quality assessment results to include the social and economic impact of power quality, thereby improving the comprehensiveness and reliability of the power quality assessment.

[0075] Based on the above embodiments, in this application embodiment, the power analysis data includes local analysis data for each sampling period; furthermore, an optional method for determining the power analysis data is provided, such as... Figure 2 As shown, the specific steps include:

[0076] S201, for the raw power data in each sampling period, determine at least two sets of spectrum data in different sampling intervals based on the raw power data.

[0077] In this system, the duration of different sampling intervals is the same. Spectral data can reflect the distribution of amplitude and phase of different frequency components in the signal.

[0078] Optionally, for the raw power data in each sampling period, multiple short-term sampling intervals can be selected evenly within that sampling period; then, the spectral data in each sampling interval can be determined based on the raw power data in each sampling interval.

[0079] For example, target power data within the target frequency band is extracted from the original power data; at least two sampling intervals are extracted at equal intervals from the sampling period; for each sampling interval, discrete power data is obtained from the target power data located within the sampling interval according to a preset sampling rate, and the discrete power data is processed by discrete Fourier transform to obtain the spectrum data within the sampling interval. Here, the target frequency band is the frequency band where the effective power data is located. Target power data can be understood as power data located within the target frequency band. The preset sampling rate is the sampling rate of the power data. Discrete power data is the discrete data constructed by combining the power data points extracted from the target power data with the preset sampling rate.

[0080] Optionally, for the raw power data in each sampling period, bandpass filtering can be applied to the raw power data to retain only the power data within the target frequency band, thus obtaining the target power data. The target frequency band can be the 1.5kHz-200kHz band.

[0081] Furthermore, the sampling period can be divided into equally spaced sampling intervals. For each sampling interval, a preset sampling rate can be used to collect discrete electrical energy data from the target electrical energy data within that interval. Then, a preset resolution is used to perform discrete Fourier transform processing on the discrete electrical energy data to obtain the spectral data within the sampling interval.

[0082] For example, each 10 / 12 cycles is divided into 32 sampling intervals. Then, for each sampling interval, 512 power data points are collected at a sampling rate of 1.024MHz as discrete power data. The discrete power data is then processed by Discrete Fourier Transform at a resolution of 2kHz to obtain 256 sets of spectral data within that sampling interval.

[0083] S202, Based on the spectral data within each sampling interval, determine the local analysis data for the sampling period.

[0084] The local analysis data includes at least one of the minimum, maximum, and average values ​​of the harmonic components.

[0085] For each sampling interval, the effective harmonic values ​​of the ultra-high harmonic components within that interval can be obtained based on the spectral data. For example, to ensure the reliability of the data analysis, the first four sets of data and the data after the 181st set from the 256 sets of spectral data can be discarded, thus obtaining the effective values ​​of 71 sets of ultra-high harmonic components covering the frequency range of 8-150kHz.

[0086] Furthermore, based on the effective values ​​of the ultra-high harmonic components within the sampling interval, the minimum, maximum, and average values ​​of the harmonic components are calculated, and these values ​​are used as local analysis data for that sampling interval. Then, the local analysis data from each sampling interval are stitched together to obtain the local analysis data for the sampling period.

[0087] In another embodiment, within the frequency range of 9~150 kHz, the voltage and current signals within the sampling period (200ms measurement window) can be filtered to remove harmonics with frequencies less than 1.5kHz and greater than 200kHz. Then, 32 sampling intervals are selected at equal intervals within the sampling period, each sampling interval being 0.5ms. Spectral analysis is performed on each of the 32 sampling intervals, and the average value is taken as the local analysis data of that sampling interval.

[0088] In this embodiment, by breaking down the overall sampling period into multiple independent sampling intervals, the original power data within a single period is subjected to multi-interval spectrum splitting and extraction. This allows for the precise capture of harmonic spectrum features at different time positions, avoiding the loss of local fluctuation features caused by single-interval analysis and improving the precision of power quality data analysis.

[0089] Based on the above embodiments, this application provides an optional method for determining parameters of each dimension, such as... Figure 3 As shown, the specific steps include:

[0090] S301, determine the power assessment parameters based on standard power information, various raw power data and power analysis data.

[0091] Standard electrical energy information can be understood as electrical energy information during normal grid operation, which may include the permissible voltage deviation range, harmonic content limits for each frequency, three-phase voltage / current imbalance limits, and the grid's rated power frequency and permissible frequency fluctuation thresholds. Electrical energy assessment parameters include at least one of the following: voltage compliance rate, harmonic compliance rate, three-phase imbalance compliance rate, and frequency quality.

[0092] Optionally, standard electrical energy information can be used as the evaluation benchmark to verify the raw electrical energy data and electrical energy analysis data from dimensions such as voltage fluctuations and flicker, voltage deviation, voltage sags and short-term interruptions, grid harmonics, three-phase imbalance, and frequency deviation, thereby obtaining the voltage compliance rate, harmonic compliance rate, three-phase imbalance compliance rate, and frequency quality. Subsequently, the voltage compliance rate, harmonic compliance rate, three-phase imbalance compliance rate, and frequency quality can be used as electrical energy assessment parameters.

[0093] For example, the instantaneous values ​​of the three-phase real-time operating voltage can be extracted from the raw power data, and the measured voltage values ​​can be compared one by one with the nominal voltage and voltage upper and lower deviation limits specified in the standard power information. Then, based on the duration of the measured voltage within the qualified limit range and the number of samplings, the qualified duration / total monitoring duration and the qualified sampling points / total sampling points are used as the calculation basis to calculate the voltage qualification rate.

[0094] The measured amplitude data of ultra-high harmonics in the original power data are retrieved. Combined with the average, maximum and minimum harmonic values ​​in the power analysis data, and compared with the harmonic content rate and effective value limit indicators of different frequency bands in the standard power information, it is determined whether the single sampled harmonic value, the time period average harmonic value and the harmonic peak value exceed the standard limit. Then, the number of samples that meet the harmonic index within the monitoring period is counted. Combined with the frequency and magnitude of harmonic exceedance, the harmonic compliance rate is calculated.

[0095] The three-phase voltage amplitude, three-phase current amplitude, and three-phase phase difference data are extracted from the raw power data. Then, the three-phase voltage unbalance and three-phase current unbalance are calculated in real time according to the three-phase unbalance calculation formula. Furthermore, the maximum allowable limit of three-phase unbalance in the standard power information can be matched, and the running time and number of valid data sets within the unbalance range can be counted to obtain the three-phase unbalance qualification rate.

[0096] Based on the real-time frequency data of the power grid in the original power data, and using the rated power frequency and the allowable frequency fluctuation range set in the standard power information as the evaluation benchmark, the real-time frequency deviation can be calculated; combined with the frequency average and frequency fluctuation extreme values ​​within the time period, the stability of the power grid frequency can be determined; then, based on the real-time frequency deviation and the stability of the power grid frequency, the frequency quality can be determined.

[0097] S302 determines power quality parameters based on historical operating data.

[0098] The power supply quality parameters include at least one of the following: power supply safety parameters, power supply reliability parameters, and voltage quality parameters.

[0099] Optionally, power supply safety parameters, power supply reliability parameters, and voltage quality parameters can be determined based on equipment maintenance logs, line fault records, power outage event statistics, and load operation records from historical operating data.

[0100] For example, based on historical operational data such as equipment maintenance logs, one can statistically analyze the number of potential equipment hazards, defect rectification completion rate, equipment periodic maintenance execution rate, and the proportion of old and faulty equipment. Based on line fault records, one can statistically analyze the frequency of safety-related faults such as short-circuit faults, grounding faults, insulation abnormalities, and external damage. Based on load operation records, one can statistically analyze the duration of operating conditions prone to safety hazards, such as prolonged overload operation, load imbalance, and excessive reactive power deficit. Then, by comprehensively considering equipment health status, line safety fault frequency, and load safety operation conditions, one can quantify equipment safety operation parameters, line safety operation levels, and load safety control indicators, thereby obtaining power supply safety parameters.

[0101] Based on power outage statistics, the number of outages, average outage duration, and planned outage impact duration can be calculated. The average fault-free operating time and average power restoration time can be calculated based on line fault records. The impact of load fluctuations on power supply continuity can be analyzed in conjunction with load operation records. Then, according to industry standards for power supply reliability, the number of outages, average outage duration, planned outage impact duration, average fault-free operating time, average power restoration time, and the impact of load fluctuations on power supply continuity can be analyzed to determine power supply reliability parameters.

[0102] From historical load operation records, abnormal periods of concentrated peak load, off-peak load, and impulsive load are obtained; based on equipment operation and maintenance ledgers, the commissioning rate, operation frequency, and regulation response efficiency of power equipment such as voltage regulating equipment and reactive power compensation equipment are statistically analyzed; abnormal period data and regional data of long-term high voltage, low voltage, and frequent voltage fluctuations are collected within historical periods; then, based on the commissioning rate, operation frequency, and regulation response efficiency of power equipment, as well as the abnormal period data and regional data, the overall voltage regulation capability and steady-state voltage operation level of the regional power grid are analyzed from a long-term operation perspective, thereby obtaining voltage quality parameters.

[0103] S303, Determine user evaluation parameters based on service quality data.

[0104] Optionally, user evaluation parameters can be obtained by analyzing service quality-related data such as user electricity feedback, power quality complaint data, electricity experience survey results, and grid energy conservation governance effectiveness from dimensions such as electricity service experience, public electricity security, and low-carbon electricity benefits.

[0105] For example, refer to Figure 4The diagram shows the architecture of the power quality assessment system, which is divided into three levels: the target layer, the criteria layer, and the indicator layer. The criteria layer comprises three core evaluation dimensions, forming the system's framework, and is categorized into technical indicators, economic indicators, and social indicators. Specifically, technical indicators include: Three-phase imbalance: the corresponding indicator is the three-phase imbalance degree, which refers to the degree of imbalance in the amplitude and phase of the three-phase voltage / current, reflecting the balance of the three-phase load. Grid harmonics: the corresponding indicator is the total harmonic distortion rate, which refers to the ratio of the effective value of each harmonic voltage to the effective value of the fundamental voltage, reflecting the level of harmonic pollution. Voltage fluctuations and flicker: the corresponding indicators are voltage amplitude variation and voltage fluctuation frequency, which refer to the amplitude and frequency of rapid voltage fluctuations, reflecting the impact on sensitive equipment (such as lighting and precision instruments). Voltage deviation: the corresponding indicator is voltage deviation, which refers to the percentage deviation between the actual operating voltage of the power grid and the rated voltage, reflecting steady-state voltage quality. Voltage sag / short-term interruption: The specific indicators are voltage sag amplitude and voltage sag duration, which refer to the magnitude and duration of a short-term voltage drop, used to reflect the impact of power grid transient disturbances on the load. Frequency deviation: The specific indicator is frequency deviation.

[0106] Economic indicators include: Power supply safety: Specific indicators include overload and voltage exceedance, referring to the frequency and duration of long-term overload operation and voltage exceedance of equipment, reflecting potential safety hazards in equipment operation. Power supply reliability: Specific indicators include power supply reliability rate, average annual power outage frequency, and average annual power outage duration, referring to the number and duration of power outages, reflecting the grid's continuous power supply capacity. Voltage quality (economic dimension): Specific indicators include additional energy loss, equipment tolerance, and equipment health index, referring to the additional losses caused by voltage anomalies, the rate of equipment aging, and health status, reflecting the impact of operation and maintenance costs on equipment lifespan.

[0107] Social indicators include: Corporate Image: Specific indicators include corporate responsibility, participation in public welfare, and user engagement, referring to public evaluation of power grid companies' performance in power quality management and fulfillment of social responsibility. User Experience: Specific indicators include service attitude, business competence and convenience, and user loyalty, referring to user satisfaction with power supply services and electricity usage experience, reflecting the impact of power quality on users' production and lives. Environmental Impact: Specific indicators include energy absorption rate and land and material conservation effects, referring to the energy conservation, emission reduction, and resource conservation results brought about by power quality optimization, aligning with the "dual carbon" target requirements.

[0108] In this embodiment, by strictly separating the sources and dimensions of power quality evaluation data, corresponding technical power assessment parameters, operation and maintenance power supply quality parameters, and public service evaluation parameters are generated based on power data, historical operation data, and service quality data, respectively. This achieves a systematic and hierarchical construction of evaluation dimensions, avoids confusion of indicators and misuse of data, and ensures that the overall evaluation logic is rigorous and standardized.

[0109] Based on the above embodiments, this application provides an optional method for determining power quality assessment results, such as... Figure 5 As shown, the specific steps include:

[0110] S501: Based on the evaluation information of each user on different evaluation dimensions, construct a subjective judgment matrix associated with each evaluation dimension, and perform calculations on the subjective judgment matrix to obtain the subjective weight values ​​corresponding to each evaluation dimension.

[0111] The assessment information can be understood as each user's evaluation of the importance of different assessment dimensions. These users can be experts in the field or electricity users of the target power grid. Assessment dimensions include power assessment parameters, power quality parameters, and user evaluation parameters. The subjective judgment matrix contains each user's subjective opinion on the importance of each assessment dimension. The subjective weight values ​​represent the degree of importance each user subjectively perceives for each assessment dimension.

[0112] Optionally, for the three categories of criteria—technical, economic, and social—authoritative experts in their respective fields (such as power systems, grid operation and maintenance, and user service experts) can be invited. The opinions of each expert can be assigned different weights based on their professional title, years of experience, and project experience. Afterward, questionnaires can be distributed to electricity users of the target power grid to collect their actual feelings about indicators such as power supply reliability and service experience, and user opinions can be integrated with expert opinions.

[0113] Furthermore, the integrated opinions can be used to compare the importance of each evaluation dimension pairwise, forming a subjective judgment matrix. Then, the subjective judgment matrix is ​​calculated to obtain the subjective weight values ​​corresponding to each evaluation dimension. For example, the integrated opinions can be used to compare the importance of different indicators under each evaluation dimension pairwise, forming a subjective judgment matrix. Then, the analytic hierarchy process (AHP) can be used to convert the subjective judgment matrix into the importance coefficients of the indicators, i.e., the subjective weight values.

[0114] S502 determines the dynamic weight values ​​corresponding to each evaluation dimension based on the original power data, historical operation data, and service quality data.

[0115] The dynamic weight values ​​represent the fluctuations in the importance of each evaluation dimension.

[0116] Optionally, dynamic weight values ​​for each evaluation dimension can be determined based on the fluctuations in the original power data, historical operation data, and service quality data.

[0117] For example, raw power data, historical operational data, and service quality data can be divided into multiple continuous subsequences by time series using a sliding window to capture the dynamic changes of the indicators. Then, range standardization is performed on the time series data within each sliding window to eliminate the influence of different units and orders of magnitude. Finally, by combining the data fluctuations between the processed continuous subsequences, the dynamic weight values ​​corresponding to each evaluation dimension are determined.

[0118] S503: For each evaluation dimension, the dynamic weight value corresponding to the evaluation dimension is used to process the subjective weight value corresponding to the evaluation dimension, so as to obtain the comprehensive weight value corresponding to the evaluation dimension.

[0119] The comprehensive weight value is the weight value that combines dynamic weight values ​​and subjective weight values.

[0120] Optionally, for each evaluation dimension, the dynamic weight value and the subjective weight value of that evaluation dimension can be fused to obtain the comprehensive weight value corresponding to that evaluation dimension. For example, the average of the dynamic weight value and the subjective weight value of that evaluation dimension can be used as the comprehensive weight value. Alternatively, the dynamic weight value and the subjective weight value of that evaluation dimension can be fused according to a preset ratio to obtain the comprehensive weight value.

[0121] S504 uses the comprehensive weight values ​​corresponding to each evaluation dimension to perform a weighted summation of the power evaluation parameters, power supply quality parameters, and user evaluation parameters to obtain the power quality evaluation result of the target power grid.

[0122] Optionally, the power quality assessment parameters, power supply quality parameters, and user evaluation parameters can be weighted and summed using the comprehensive weight values ​​corresponding to each assessment dimension to obtain the power quality assessment parameters of the target power grid; then, the candidate level that matches the power quality assessment parameters is used as the power quality assessment result.

[0123] In this embodiment of the application, subjective weights are determined based on actual user evaluation opinions, fully incorporating the evaluation experience and preferences of actual application side, and dynamic objective weights are calculated based on raw power data, historical operation data, and service quality data. The actual distinguishing ability of the indicators is reflected by real operation data, which makes the comprehensive weight value more reasonable, thereby improving the reliability of power quality evaluation results.

[0124] Based on the above embodiments, this application provides an optional method for determining dynamic weight values. Specifically, the method involves determining the energy information entropy based on the time-series energy data corresponding to the original energy data; determining the operation information entropy based on the time-series operation data corresponding to the historical operation data; determining the quality information entropy based on the time-series quality data corresponding to the service quality data; and determining the dynamic weight values ​​corresponding to each evaluation dimension based on the energy information entropy, operation information entropy, and quality information entropy.

[0125] Among them, time-series electrical energy data refers to electrical energy data under a time series, and electrical energy information entropy is a quantitative indicator that measures the degree of disorder, uncertainty, and discrete fluctuation of the original electrical energy data. Time-series operational data refers to historical operational data under a time series, and operational information entropy is a quantitative indicator that measures the degree of disorder, uncertainty, and discrete fluctuation of historical operational data. Time-series quality data refers to service quality data under a time series, and quality information entropy is a quantitative indicator that measures the degree of disorder, uncertainty, and discrete fluctuation of service quality data.

[0126] Optionally, the information entropy formula can be used to calculate the energy information entropy of the time-series energy data corresponding to the original energy data; the operation information entropy of the time-series operation data corresponding to the historical operation data; and the quality information entropy of the time-series quality data corresponding to the service quality data.

[0127] Each evaluation dimension can be viewed as a participant in a cooperative game. By defining a payoff function using data and entropy values, the Shapley value of each evaluation dimension can be calculated as the initial dynamic weight value. The Shapley value can represent the average marginal contribution of each evaluation dimension in the overall index combination.

[0128] The Pearson correlation coefficient can be calculated between data points across different evaluation dimensions, and then converted into conflict entropy weights. The Pearson correlation coefficient measures the degree of linear correlation between two evaluation dimensions, determining whether data trends converge, and is used to eliminate information redundancy and adjust weights.

[0129] Furthermore, conflict entropy weights can be used to correct and adjust the initial dynamic weight values ​​for each evaluation dimension, thereby obtaining the corresponding dynamic weight values ​​for each evaluation dimension.

[0130] For example, refer to Figure 6 The evaluation model framework shown, for the game entropy weight module, can first calculate the initial constant weights based on the correlation and differences of each evaluation dimension using information entropy; then, adjust the constant weights using Shapley value and Pearson correlation coefficient, reduce the weights of highly correlated indicators, eliminate information redundancy, and obtain the dynamic weight values ​​of each evaluation dimension.

[0131] For the time-series variable weighting module and the grade matching mechanism module, each indicator can be graded (Excellent / Good / Medium / Quality / Unqualified) according to power quality standards. Then, data under each evaluation dimension is monitored in real time to determine its current indicator level and level status. Finally, combining the dynamic weight values ​​output by the game entropy weighting module, and the indicator levels and level statuses under each evaluation dimension output by the time-series variable weighting module and the grade matching mechanism module, the final dynamic weight values ​​are generated. These generated dynamic weight values ​​can be directly used to weight and sum the evaluation parameters under each evaluation dimension to obtain the power quality evaluation result.

[0132] In this embodiment of the application, by using information entropy to map the true activity of data, the determined dynamic weight values ​​can be made to fit the characteristics of the data itself, thereby improving the accuracy of the dynamic weight values.

[0133] Based on the above embodiments, this application provides a hardware architecture for the power quality assessment process. (Reference) Figure 7 The diagram shows the hardware architecture of the integrated voltage and current sensor. The sensor architecture includes a current sensing element, a temperature sensing element, a main control module minimum system, a Bluetooth antenna, and a debugging port. The hardware uses four PCBs to implement the overall design. High-reliability connectors are used between the PCBs to ensure signal and power connections. Each PCB implements a major functional module. The display board integrates an LCD display, buttons, indicator lights, 4G circuitry, etc.; the main control board integrates a main control processor, FPGA, memory, Ethernet, AI circuitry, etc.; the analog board integrates an ADC chip, signal conditioning circuitry, etc.; and the power board integrates a power module, voltage and current sensors, DO output circuitry, etc. Specifically, the 85V–264V AC / DC power input uses a pre-built power module for isolation; the RS-485 communication circuit signal section uses optocouplers for isolation, and the power supply section uses an isolated DC-DC module; the current measurement circuit uses a PT transformer for isolation; and the Ethernet port uses a transformer for isolation.

[0134] Figure 7 The hardware architecture block diagram shows the voltage-sensitive FPC probe (bottom): attached below the circuit under test, it senses voltage changes and outputs a weak voltage signal. The signal conditioning circuit (analog board) filters, amplifies, or attenuates the FPC probe output to match the ADC input range. The ADC acquisition (analog board) uses a 20-bit high-precision ADC to convert the conditioned analog signal into a digital value, which is then sent to the main control board via an SPI / parallel interface. The voltage reference signal provides a reference voltage for the ADC, ensuring measurement accuracy. The grounding clamp provides a system reference ground, reducing common-mode interference.

[0135] Figure 7The current measurement link in the hardware architecture block diagram shown includes: a (tunnel magnetoresistive) current measurement sensing element: using a ring array (two groups, upper and lower), used for general current measurement and waveform recording-specific current measurement respectively; signal conditioning (analog board): amplifying, biasing, and filtering the differential signal output by the TMR; AD acquisition (analog board): independently acquiring waveform recording current signals through a dedicated channel, supporting high sampling rate waveform recording; and a 20-bit ADC: some common current measurements may share this ADC, switched via a multiplexer.

[0136] Main Bluetooth SoC: Integrates an ARM main processor, Bluetooth baseband, and some peripherals. It runs data fusion algorithms (including AI inference), waveform recording and storage, and a communication protocol stack. FPGA: Used for high-speed data stream preprocessing (such as digital filtering and trigger judgment) and parallel interface management, reducing the SoC's workload. JTAG debug interface: Connects to an external emulator for firmware download and online debugging. Bluetooth antenna: Connects to the SoC's RF port for wireless data upload or parameter configuration. Indicator lights: Indicate power status, waveform recording trigger, communication connection, etc. Reset button: Manually resets the system.

[0137] In this embodiment, reference can be made to Figure 8 The software architecture block diagram of the voltage and current integrated sensor shown adopts a "front-end and back-end" approach to design software modules, achieving low-latency response and processing of events. The sensor's software modules are designed based on principles of strong real-time performance, scalability, and reliability.

[0138] Specifically, three types of sensors convert the voltage, current, and temperature of the low-voltage circuit in the power distribution room into analog electrical signals, which are then sent to the subsequent conditioning circuit. These sensors include: a voltage probe (non-contact or contact voltage tapping) that outputs a weak signal proportional to the voltage; a current element (possibly a TMR, Rogowski coil, or current transformer) that outputs an induced electromotive force; and a temperature element (such as an NTC or digital temperature sensor) attached to the busbar or switch contacts.

[0139] The raw sampled data is corrected into high-precision, high-reliability digital quantities. Specifically, the A / D sampling is a multi-channel synchronous sampling ADC (the number of bits is not specified in the figure, but it is known from the context to be at least 16 bits), converting analog signals into digital quantities. Self-calibration and compensation: Utilizing a built-in reference source or known conditions, it automatically compensates for sensor zero-point drift, gain error, and temperature drift. Temperature compensation can be performed on voltage / current measurements by combining temperature element readings. Inter-frequency signal injection: A weak test signal (e.g., 1kHz) at a non-power frequency is actively injected into the line to detect line impedance, insulation status, or the integrity of the sensor's own loop, providing additional information for fault diagnosis.

[0140] The processing chip, in conjunction with an algorithm library, performs real-time measurement calculations. The results are stored in a real-time data structure for communication or local storage. Specifically, voltage measurement calculates the RMS value, frequency, and phase. Current measurement calculates the fundamental RMS value, peak value, and waveform distortion rate. Harmonic measurement uses FFT decomposition to output the 2nd to 50th harmonic content and THD. Fault measurement identifies characteristics such as overvoltage, undervoltage, overcurrent, short circuit, and arcing, and records the fault waveform and time.

[0141] Data processing includes filtering (anti-aliasing, noise reduction), resampling, and feature extraction of the original sampling points. The FLASH & File System (FDS) stores waveform recordings, historical statistics, and event logs in structured files (such as FDS) on the FLASH memory, supporting power-off retention for easy post-event traceability. Configuration information saves system parameters (transformer ratio, threshold, calibration coefficient, communication whitelist, etc.) and can be updated externally.

[0142] Adaptive scheduling includes algorithmic scheduling that can dynamically allocate computing resources based on operating conditions (normal / fault / self-test), prioritize real-time fault recording, and dynamic power management for low-power scheduling.

[0143] Data output and remote interaction include adaptive communication scheduling: automatically adjusting the reporting interval and packet size based on Bluetooth connection quality, data volume, and priority. Under normal conditions, only statistics are sent (once per minute); in case of a fault, waveform data is immediately pushed. Secure encrypted transmission: all external data packets are encrypted using SM4 or AES to ensure that distribution network data is not leaked.

[0144] Configuration parameter updates and operating parameter settings can be received via Bluetooth from a mobile app or debugging tool, using configuration commands (such as calibration values ​​and alarm thresholds), and updated to the configuration information area in the FLASH memory. Operating parameters (such as sampling rate and waveform recording trigger conditions) can be modified online without restarting.

[0145] Power consumption and power management include an energy harvesting module: drawing power from low-voltage circuits (such as current transformers or voltage coupling) to provide the main power for the system. Dynamic power management and low-power scheduling: when the line current is extremely low (light load at night) and there are no faults, some high-speed ADCs and Bluetooth broadcasting are shut down, and the system enters sleep mode. Only temperature monitoring and low-power timers are retained; once the current changes abruptly or a predetermined time is reached, the system quickly wakes up to complete waveform recording. Algorithm scheduling coordination: in low-power mode, measurement calculations are reduced to once every 10 seconds; after waking up, full speed is restored.

[0146] Figure 9 This is a flowchart illustrating a power quality assessment method in another embodiment. Based on the above embodiments, this embodiment provides an optional example of a power quality assessment method. (Combined with...) Figure 9 The specific implementation process is as follows:

[0147] S901, in response to a power quality assessment request for a target power grid, acquires historical operating data of the target power grid, service quality data associated with the target power grid, and raw power data of the target power grid for each sampling period.

[0148] S902, for the raw power data in each sampling period, determine at least two sets of spectrum data in different sampling intervals based on the raw power data, and determine the local analysis data of the sampling period based on the spectrum data in each sampling interval.

[0149] The local analysis data includes at least one of the minimum, maximum, and average values ​​of the harmonic components.

[0150] Optionally, target power data within the target frequency band is extracted from the raw power data. At least two sampling intervals are extracted at equal intervals from the sampling period. For each sampling interval, discrete power data is obtained from the target power data located within the sampling interval according to a preset sampling rate, and discrete Fourier transform is performed on the discrete power data to obtain the spectrum data within the sampling interval.

[0151] S903 determines the power analysis data based on the local analysis data of each sampling period.

[0152] S904 determines the power assessment parameters based on standard power information, raw power data, and power analysis data.

[0153] The power assessment parameters include at least one of the following: voltage compliance rate, harmonic compliance rate, three-phase imbalance compliance rate, and frequency quality.

[0154] S905 determines power quality parameters based on historical operating data.

[0155] The power supply quality parameters include at least one of the following: power supply safety parameters, power supply reliability parameters, and voltage quality parameters.

[0156] S906, determine user evaluation parameters based on service quality data.

[0157] S907: Based on the evaluation information of each user on different evaluation dimensions, construct a subjective judgment matrix associated with each evaluation dimension, and perform calculations on the subjective judgment matrix to obtain the subjective weight values ​​corresponding to each evaluation dimension.

[0158] The evaluation dimensions include power assessment parameters, power supply quality parameters, and user evaluation parameters.

[0159] S908 determines the dynamic weight values ​​corresponding to each evaluation dimension based on the original power data, historical operation data, and service quality data.

[0160] Optionally, the energy information entropy is determined based on the time-series energy data corresponding to the original energy data. The operation information entropy is determined based on the time-series operation data corresponding to the historical operation data. The quality information entropy is determined based on the time-series quality data corresponding to the service quality data. Based on the energy information entropy, operation information entropy, and quality information entropy, the dynamic weight values ​​corresponding to each evaluation dimension are determined.

[0161] S909: For each evaluation dimension, the dynamic weight value corresponding to the evaluation dimension is used to process the subjective weight value corresponding to the evaluation dimension, so as to obtain the comprehensive weight value corresponding to the evaluation dimension.

[0162] S910 uses the comprehensive weight values ​​corresponding to each evaluation dimension to perform a weighted summation of the power evaluation parameters, power supply quality parameters, and user evaluation parameters to obtain the power quality evaluation result of the target power grid.

[0163] The specific processes of S901-S910 described above can be found in the description of the above method embodiments. Their implementation principles and technical effects are similar, and will not be repeated here.

[0164] It should be understood that although the steps in the flowcharts of the embodiments described above are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the embodiments described above may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages of other steps.

[0165] Based on the same inventive concept, this application also provides a power quality assessment device for implementing the power quality assessment method described above. The solution provided by this device is similar to the solution described in the above method; therefore, the specific limitations in one or more power quality assessment device embodiments provided below can be found in the limitations of the power quality assessment method described above, and will not be repeated here.

[0166] In one exemplary embodiment, such as Figure 10 As shown, a power quality assessment device 1 is provided, comprising: a data acquisition module 10, a data analysis module 20, a parameter determination module 30, and a result determination module 40, wherein:

[0167] The data acquisition module 10 is used to respond to the power quality assessment request for the target power grid by acquiring the historical operating data of the target power grid, the service quality data associated with the target power grid, and the raw power data of the target power grid in each sampling period.

[0168] Data analysis module 20 is used to process the raw electrical energy data to obtain electrical energy analysis data;

[0169] The parameter determination module 30 is used to determine the power assessment parameters, power supply quality parameters, and user evaluation parameters based on the original power data, power analysis data, historical operation data, and service quality data.

[0170] The result determination module 40 is used to determine the power quality assessment result of the target power grid based on power assessment parameters, power supply quality parameters, and user evaluation parameters.

[0171] In one exemplary embodiment, the power analysis data includes local analysis data for each sampling period; the data analysis module 20 is specifically used for:

[0172] For the raw power data in each sampling period, at least two sets of spectrum data in different sampling intervals are determined based on the raw power data; based on the spectrum data in each sampling interval, local analysis data for the sampling period is determined; wherein, the local analysis data includes at least one of the minimum, maximum and average values ​​of harmonic components.

[0173] In one exemplary embodiment, the data analysis module 20 is further configured to:

[0174] Extract target power data within the target frequency band from the original power data; extract at least two sampling intervals at equal intervals from the sampling period; for each sampling interval, obtain discrete power data from the target power data located within the sampling interval according to the preset sampling rate, and perform discrete Fourier transform processing on the discrete power data to obtain the spectrum data within the sampling interval.

[0175] In one exemplary embodiment, the parameter determination module 30 is specifically used for:

[0176] Based on standard power information, various raw power data, and power analysis data, power assessment parameters are determined; among them, power assessment parameters include at least one of voltage qualification rate, harmonic qualification rate, three-phase imbalance qualification rate, and frequency quality; based on historical operating data, power supply quality parameters are determined; among them, power supply quality parameters include at least one of power supply safety parameters, power supply reliability parameters, and voltage quality parameters; based on service quality data, user evaluation parameters are determined.

[0177] In one exemplary embodiment, the result determination module 40 is specifically used for:

[0178] Based on the evaluation information of each user for different evaluation dimensions, a subjective judgment matrix relating each evaluation dimension is constructed, and the subjective judgment matrix is ​​calculated to obtain the subjective weight values ​​corresponding to each evaluation dimension. The evaluation dimensions include power evaluation parameter dimensions, power supply quality parameter dimensions, and user evaluation parameter dimensions. Based on the original power data, historical operation data, and service quality data, the dynamic weight values ​​corresponding to each evaluation dimension are determined. For each evaluation dimension, the subjective weight values ​​corresponding to the evaluation dimension are processed using the dynamic weight values ​​corresponding to the evaluation dimension to obtain the comprehensive weight value corresponding to the evaluation dimension. Finally, the power evaluation parameters, power supply quality parameters, and user evaluation parameters are weighted and summed using the comprehensive weight values ​​corresponding to each evaluation dimension to obtain the power quality evaluation result of the target power grid.

[0179] In one exemplary embodiment, the result determination module 40 is further configured to:

[0180] Based on the time-series power data corresponding to the original power data, determine the power information entropy; based on the time-series operation data corresponding to the historical operation data, determine the operation information entropy; based on the time-series quality data corresponding to the service quality data, determine the quality information entropy; based on the power information entropy, operation information entropy, and quality information entropy, determine the dynamic weight values ​​corresponding to each evaluation dimension.

[0181] Each module in the aforementioned power quality assessment device can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device, or stored in the memory of a computer device as software, so that the processor can call and execute the corresponding operations of each module.

[0182] In one exemplary embodiment, a computer device is provided, which may be a server, and its internal structure diagram may be as follows: Figure 11As shown, this computer device includes a processor, memory, input / output interfaces (I / O), and a communication interface. The processor, memory, and I / O interfaces are connected via a system bus, and the communication interface is also connected to the system bus via the I / O interfaces. The processor provides computational and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer programs, and a database. The internal memory provides the environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The database stores electrical energy data. The I / O interfaces are used for exchanging information between the processor and external devices. The communication interface is used for communicating with external terminals via a network connection. When executed by the processor, the computer program implements a power quality assessment method.

[0183] Those skilled in the art will understand that Figure 11 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0184] In one embodiment, a computer device is also provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps in the above method embodiments.

[0185] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon that, when executed by a processor, implements the steps in the above method embodiments.

[0186] In one embodiment, a computer program product is provided, including a computer program that, when executed by a processor, implements the steps in the above method embodiments.

[0187] It should be noted that the data involved in this application (including but not limited to electricity data) are all data authorized by the user or fully authorized by all parties, and the collection, use and processing of the relevant data must comply with relevant regulations.

[0188] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile memory and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM). The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, artificial intelligence (AI) processors, etc., and are not limited to these.

[0189] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this application.

[0190] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of this patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this application should be determined by the appended claims.

Claims

1. A method for assessing power quality, characterized in that, The method includes: In response to a power quality assessment request for a target power grid, historical operating data of the target power grid, service quality data associated with the target power grid, and raw power data of the target power grid for each sampling period are obtained. The raw electrical energy data are processed to obtain electrical energy analysis data; Based on the original power data, the power analysis data, the historical operation data, and the service quality data, determine the power assessment parameters, power supply quality parameters, and user evaluation parameters; The power quality assessment result of the target power grid is determined based on the power assessment parameters, the power supply quality parameters, and the user evaluation parameters.

2. The method according to claim 1, characterized in that, The power analysis data includes local analysis data for each sampling period; the processing of each raw power data to obtain the power analysis data includes: For the raw power data in each sampling period, at least two sets of spectrum data in different sampling intervals are determined based on the raw power data. Based on the spectral data within each sampling interval, local analysis data for the sampling period is determined; wherein, the local analysis data includes at least one of the minimum, maximum, and average values ​​of harmonic components.

3. The method according to claim 2, characterized in that, The step of determining at least two sets of spectral data within different sampling intervals based on the original electrical energy data includes: Extract the target power data within the target frequency band from the raw power data; Extract at least two sets of sampling intervals at equal intervals from the sampling period; For each sampling interval, discrete power data is obtained from the target power data located within the sampling interval according to a preset sampling rate, and the discrete power data is processed by discrete Fourier transform to obtain the spectrum data within the sampling interval.

4. The method according to claim 1, characterized in that, The step of determining power assessment parameters, power supply quality parameters, and user evaluation parameters based on the original power data, the power analysis data, the historical operation data, and the service quality data includes: Based on standard power information, the original power data, and the power analysis data, power evaluation parameters are determined; wherein, the power evaluation parameters include at least one of voltage qualification rate, harmonic qualification rate, three-phase imbalance qualification rate, and frequency quality. Based on the historical operating data, power quality parameters are determined; wherein, the power quality parameters include at least one of power safety parameters, power reliability parameters, and voltage quality parameters; Based on the service quality data, determine the user evaluation parameters.

5. The method according to claim 1, characterized in that, The step of determining the power quality assessment result of the target power grid based on the power assessment parameters, the power supply quality parameters, and the user evaluation parameters includes: Based on the evaluation information of each user on different evaluation dimensions, a subjective judgment matrix associated with each evaluation dimension is constructed, and the subjective judgment matrix is ​​calculated to obtain the subjective weight value corresponding to each evaluation dimension; wherein, the evaluation dimensions include the power evaluation parameter dimension, the power supply quality parameter dimension, and the user evaluation parameter dimension. Based on the original power data, the historical operation data, and the service quality data, determine the dynamic weight values ​​corresponding to each evaluation dimension; For each evaluation dimension, the dynamic weight value corresponding to the evaluation dimension is used to process the subjective weight value corresponding to the evaluation dimension to obtain the comprehensive weight value corresponding to the evaluation dimension. The power quality assessment results of the target power grid are obtained by weighting and summing the power assessment parameters, power supply quality parameters, and user evaluation parameters using the comprehensive weight values ​​corresponding to each assessment dimension.

6. The method according to claim 5, characterized in that, The step of determining the dynamic weight values ​​corresponding to each evaluation dimension based on the original power data, the historical operation data, and the service quality data includes: The energy information entropy is determined based on the time-series energy data corresponding to the original energy data; The operational information entropy is determined based on the time-series operational data corresponding to the historical operational data. Determine the quality information entropy based on the time-series quality data corresponding to the service quality data; Based on the energy information entropy, the operation information entropy, and the quality information entropy, determine the dynamic weight values ​​corresponding to each evaluation dimension.

7. A power quality assessment device, characterized in that, The device includes: The data acquisition module is used to respond to a power quality assessment request for a target power grid by acquiring historical operating data of the target power grid, service quality data associated with the target power grid, and raw power data of the target power grid for each sampling period. The data analysis module is used to process the raw electrical energy data to obtain electrical energy analysis data; The parameter determination module is used to determine power assessment parameters, power supply quality parameters, and user evaluation parameters based on the original power data, the power analysis data, the historical operation data, and the service quality data. The result determination module is used to determine the power quality assessment result of the target power grid based on the power assessment parameters, the power supply quality parameters, and the user evaluation parameters.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 6.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 6.

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