Electric energy quality monitoring method and system considering broadband transmission characteristics of mutual inductor

Abstract

The invention relates to the technical field of power system monitoring, in particular to an electric energy quality monitoring method and system considering broadband transmission characteristics of a mutual inductor, and the method comprises the following steps: collecting current, voltage and frequency data of the mutual inductor under various load conditions in real time based on the operation log information of a power grid, and recording the operation data and time information of the power grid; and generating mutual inductor operation data. According to the invention, the transmission characteristics of the mutual inductor under various power grid load changes are analyzed to ensure that the monitoring data accurately reflect the actual operation state of the power grid, the data processing parameters and the data verification threshold are adjusted in combination with the real-time load to ensure the accuracy of the data acquisition process, and the occurrence frequency of the abnormal parameters is identified to ensure the accuracy of the data acquisition process. The influence of various operation parameters on the electric energy quality is quantified, the operation stability of the power grid is evaluated by calculating statistical data and volatility of various power grid quality parameters, visual power grid health condition evaluation is provided for power grid operators, and monitoring and management of the electric energy quality are achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of power system monitoring, and in particular to a method and system for monitoring power quality taking into account the broadband transmission characteristics of a transformer. Background Art

[0002] The field of power system monitoring technology focuses on real-time monitoring and evaluation of the operating status and power quality of the power grid to ensure the safety, stability and efficiency of power supply. It uses a variety of sensor equipment, monitoring systems and advanced analysis tools to track the key parameters of the power system in real time, including voltage, current, frequency and harmonics. By accurately monitoring various parameters of power quality, it prevents and diagnoses faults, optimizes power grid operation, reduces power outages, and improves energy efficiency. Combined with data processing technology, it analyzes the collected data to provide data support for the maintenance and upgrade of the power grid and achieve the development goal of smart grid.

[0003] Among them, the power quality monitoring method considering the broadband transmission characteristics of the transformer aims to improve the accuracy and reliability of the monitoring data by taking into account the transmission characteristics of the transformer in a wide frequency range. By optimizing the frequency response of the transformer, it accurately captures various signals in the power grid, effectively evaluates and monitors power quality problems, including voltage fluctuations and harmonic distortion, and realizes power quality analysis in the power system, helping operation and maintenance personnel to discover and solve the problem of power quality degradation at an early stage, ensuring the stable operation of the power system and the efficient use of electricity, improving the overall performance of the power grid, reducing energy loss, and improving the economy and environmental sustainability of the power grid through power management.

[0004] Traditional power quality monitoring methods cannot fully utilize the broadband transmission characteristics of transformers, resulting in insufficient efficiency and accuracy in monitoring power quality in complex power grid environments. Under high load and rapidly changing power grid conditions, they cannot promptly and accurately capture short-term power quality problems, including voltage fluctuations and harmonic distortion, which have a serious impact on the stability and safe operation of the power grid. Insufficient flexibility in data processing and parameter adjustment results in insensitive or in-time responses to changes in power grid status, affecting the effectiveness of power management and preventive maintenance measures. Summary of the invention

[0005] The purpose of the present invention is to solve the shortcomings in the prior art and to propose a method and system for monitoring power quality taking into account the broadband transmission characteristics of a transformer.

[0006] In order to achieve the above object, the present invention adopts the following technical solution, a method for monitoring power quality considering the broadband transmission characteristics of a transformer, comprising the following steps:

[0007] S1: Based on the power grid operation log information, the current, voltage and frequency data of the transformer under various load conditions are collected in real time, and the power grid operation data and time information are recorded to generate the transformer operation data;

[0008] S2: Based on the transformer operation data, monitor the response of the transformer under various grid load changes, analyze the performance stability of the transformer by comparing the data changes under various load states, and generate transformer characteristic information;

[0009] S3: Based on the transformer characteristic information, according to the real-time monitored grid load data and environmental conditions, adjust the data processing parameters, calibrate the verification threshold of data acquisition to match the state change of the grid, and generate grid data processing parameters;

[0010] S4: using the power grid data processing parameters, analyzing a variety of power grid quality parameters, identifying the occurrence frequencies of a variety of abnormal parameters, quantifying the impact on power quality, and generating abnormal change pattern information;

[0011] S5: According to the abnormal change pattern information, the operation stability of the power grid is evaluated by calculating the statistical data and volatility of multiple power grid quality parameters to generate a power grid stability score;

[0012] S6: Based on the grid stability score and in combination with real-time grid operation data, the impact of various nonlinear loads on power quality and grid stability is evaluated to generate power quality monitoring data.

[0013] As a further solution of the present invention, the transformer operation data includes current data acquisition records, voltage waveform information and frequency deviation data, the transformer characteristic information includes load response time, steady-state performance evaluation data and load fluctuation analysis results, the power grid data processing parameters include corrected data acquisition frequency, adjusted data verification threshold and data standardization parameters, the abnormal change pattern information includes harmonic level information, voltage fluctuation range and frequency change trend information, the power grid stability score includes power grid stability index, parameter volatility data and power grid quality parameter statistical results, and the power quality monitoring data includes nonlinear load analysis results, load change impact assessment information and power quality change trend.

[0014] As a further solution of the present invention, based on the power grid operation log information, the current, voltage and frequency data of the transformer under various load conditions are collected in real time, and the power grid operation data and time information are recorded. The steps of generating the transformer operation data are specifically as follows:

[0015] S101: Based on the power grid operation log information, the current, voltage and frequency data of the transformer in the power grid are collected in real time, and the timestamp and load condition label are recorded to generate the operation data collection record;

[0016] S102: Based on the operation data collection record, clean the operation data, including removing abnormal values ​​and filling missing values, to generate a data processing result;

[0017] S103: Based on the data processing result, the operation data is grouped according to load conditions and time information, and the data is standardized to generate transformer operation data.

[0018] As a further solution of the present invention, based on the transformer operation data, the response of the transformer under various grid load changes is monitored, and the performance stability of the transformer is analyzed by comparing the data changes under various load states. The steps of generating transformer characteristic information are specifically as follows:

[0019] S201: Based on the transformer operation data, by comparing the current, voltage and frequency changes under various load states, recording the data fluctuations under various states, and generating load state comparison data;

[0020] S202: Based on the load state comparison data, calculating the reaction time and data fluctuation amplitude of the transformer under various load states, evaluating the response capability of the transformer to rapid changes, and generating transformer performance evaluation data;

[0021] S203: Based on the transformer performance evaluation data, analyze the performance stability of the transformer under various power grid load conditions to generate transformer characteristic information.

[0022] As a further solution of the present invention, based on the mutual inductor characteristic information, according to the real-time monitored power grid load data and environmental conditions, the data processing parameters are adjusted, the verification threshold of the data acquisition is calibrated to match the state change of the power grid, and the steps of generating the power grid data processing parameters are specifically as follows:

[0023] S301: Based on the transformer characteristic information, according to the real-time monitored power grid load data and environmental conditions, analyzing the range of data processing parameters that need to be adjusted, and generating an adjustment index list;

[0024] S302: According to the adjustment index list, adjust the verification threshold of data acquisition to match the operation status and environmental changes of the power grid and generate data acquisition parameters;

[0025] S303: Apply the data acquisition parameters to format and standardize the power grid operation data, optimize data consistency, and generate power grid data processing parameters.

[0026] As a further solution of the present invention, the steps of using the power grid data processing parameters to analyze a variety of power grid quality parameters, identify the occurrence frequency of a variety of abnormal parameters, and quantify the impact on power quality to generate abnormal change pattern information are specifically as follows:

[0027] S401: Using the power grid data processing parameters, real-time monitoring of harmonics, voltage fluctuations and frequency deviations in the power grid, recording changes in multiple key frequency indicators, and generating key frequency change records;

[0028] S402: Analyze the key frequency change records, identify abnormal change patterns, calculate regression coefficients of multiple parameters, quantify the impact of multiple parameter changes on power quality, and generate abnormal impact analysis information;

[0029] S403: Analyze and record the occurrence frequencies of various abnormal parameters according to the abnormal impact analysis information, perform quantitative analysis, and generate abnormal change pattern information.

[0030] As a further solution of the present invention, the specific formula for calculating the regression coefficients of multiple parameters is:

[0031]

[0032] Among them, β i is the regression coefficient of the ith power quality parameter, which indicates the expected change in the power quality index y when the parameter changes by one unit. It is a statistical measure of the direct effect of parameter i on power quality when controlling other parameters. i represents the index of the power quality parameter, which is used to distinguish different power quality parameters, including voltage, current, frequency and harmonics, to ensure that each parameter is analyzed separately. j represents the index of the time point of data collection, which is used to identify data at different time points to ensure time series analysis of power quality. ij represents the actual value of the ith power quality parameter measured at the jth time point, which is used to capture the performance of the parameter at the target time point. is the average of all observed values ​​of the ith power quality parameter, used to evaluate the typical performance level of this parameter, y j is the measured value of the power quality index at the jth time point, reflecting the power quality status at that moment. It is the average value of all power quality indicators, providing a reference benchmark for evaluating the deviation of power quality from the average state at each time point. n is the total number of observation points in the data set, indicating the total number of measurements, which is the basis for determining the scope and depth of the overall analysis. By calculating the regression coefficient β i , analyze the relative importance of multiple parameters in power quality monitoring.

[0033] As a further solution of the present invention, according to the abnormal change pattern information, by calculating the statistical data and volatility of multiple power grid quality parameters, the operation stability of the power grid is evaluated, and the steps of generating a power grid stability score are specifically as follows:

[0034] S501: Analyze and calculate the statistical data and volatility of multiple key power grid quality parameters, including harmonic level and voltage fluctuation frequency, according to the abnormal change pattern information, and generate power grid quality statistical data;

[0035] S502: Based on the power grid quality statistical data, real-time evaluation of the stability of power grid operation, including power grid steady-state deviation and response speed, is performed to generate a stability calculation result;

[0036] S503: Based on the stability calculation results, through time series analysis, evaluate and predict the change trend of power grid stability and operating parameters, and generate a power grid stability score.

[0037] As a further solution of the present invention, based on the grid stability score and in combination with real-time grid operation data, the impact of various nonlinear loads on power quality and grid stability is evaluated, and the steps of generating power quality monitoring data are specifically as follows:

[0038] S601: Based on the grid stability score and in combination with real-time grid operation data, analyzing the impact of various nonlinear loads on grid stability, and generating load impact assessment data;

[0039] S602: Analyze and quantify the impact of changes in various power operation parameters on power quality, including voltage drop and frequency change, based on the load impact assessment data, and generate quality impact prediction information;

[0040] S603: Analyze the impact of various nonlinear load changes on grid stability based on the quality impact prediction information, evaluate the power quality under various load conditions, and generate power quality monitoring data.

[0041] A power quality monitoring system considering the broadband transmission characteristics of a transformer, the power quality monitoring system considering the broadband transmission characteristics of a transformer is used to execute the above-mentioned power quality monitoring method considering the broadband transmission characteristics of a transformer, the system comprising:

[0042] The data acquisition and analysis module collects the current, voltage and frequency data of the transformer in real time based on the grid operation log information, records the time information, analyzes the response of the transformer under various grid load conditions, evaluates the performance stability of the transformer, and generates transformer performance analysis data;

[0043] The parameter adjustment module uses the transformer performance analysis data to adjust the data processing parameters according to the real-time monitored grid load data and environmental conditions, calibrates the verification threshold of data acquisition to match the state change of the grid, and generates a processing parameter adjustment record;

[0044] The anomaly detection module uses the processing parameter adjustment records to analyze various quality parameters of the power grid, identify the frequency of occurrence of abnormal parameters, and quantify the impact on power quality to generate quality parameter analysis results;

[0045] The stability calculation module calculates the statistical data and volatility of various quality parameters of the power grid based on the quality parameter analysis results, evaluates the operational stability of the power grid, and generates stability evaluation records;

[0046] The load impact assessment module evaluates the impact of various nonlinear loads on power quality and grid stability based on stability assessment records and combined with real-time grid operation data, analyzes the impact of load changes on grid stability, and generates power quality monitoring data.

[0047] Compared with the prior art, the advantages and positive effects of the present invention are:

[0048] In the present invention, by analyzing the transmission characteristics of the mutual inductor under various grid load changes, it is ensured that the monitoring data accurately reflects the actual operating status of the power grid, and the data processing parameters and data verification thresholds are adjusted in combination with the real-time load to ensure the accuracy of the data acquisition process. By identifying the frequency of occurrence of abnormal parameters, the influence of various operating parameters on the power quality is quantified, and by calculating the statistical data and volatility of various grid quality parameters, the grid operation stability is evaluated, providing the grid operator with an intuitive grid health status assessment, thereby realizing the monitoring and management of the power quality. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] Figure 1 It is a schematic diagram of the workflow of the present invention;

[0050] Figure 2 This is a detailed flow chart of S1 of the present invention;

[0051] Figure 3 This is a detailed flow chart of S2 of the present invention;

[0052] Figure 4 This is a detailed flow chart of S3 of the present invention;

[0053] Figure 5 This is a detailed flow chart of S4 of the present invention;

[0054] Figure 6 This is a detailed flow chart of S5 of the present invention;

[0055] Figure 7 This is a detailed flow chart of S6 of the present invention;

[0056] Figure 8 It is a system flow chart of the present invention. DETAILED DESCRIPTION

[0057] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0058] In the description of the present invention, it should be understood that the terms "length", "width", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside" and the like indicate positions or positional relationships based on the positions or positional relationships shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as limiting the present invention. In addition, in the description of the present invention, "multiple" means two or more, unless otherwise clearly and specifically defined.

[0059] See also Figure 1 The present invention provides a technical solution, a method for monitoring power quality considering the broadband transmission characteristics of a transformer, comprising the following steps:

[0060] S1: Based on the power grid operation log information, the current, voltage and frequency data of the transformer under various load conditions are collected in real time, and the power grid operation data and time information are recorded to generate the transformer operation data;

[0061] S2: Based on the transformer operation data, monitor the response of the transformer under various grid load changes, analyze the performance stability of the transformer by comparing the data changes under various load states, and generate transformer characteristic information;

[0062] S3: Based on the transformer characteristic information, according to the real-time monitored grid load data and environmental conditions, adjust the data processing parameters, calibrate the verification threshold of data acquisition to match the state change of the grid, and generate grid data processing parameters;

[0063] S4: using the grid data processing parameters, by analyzing various grid quality parameters, identifying the occurrence frequency of various abnormal parameters, and quantifying the impact on power quality, generating abnormal change pattern information;

[0064] S5: Based on the abnormal change pattern information, the operational stability of the power grid is evaluated by calculating the statistical data and volatility of various power grid quality parameters to generate a power grid stability score;

[0065] S6: Based on the grid stability score and combined with real-time grid operation data, the impact of various nonlinear loads on power quality and grid stability is evaluated to generate power quality monitoring data.

[0066] The transformer operation data includes current data acquisition records, voltage waveform information and frequency deviation data. The transformer characteristic information includes load response time, steady-state performance evaluation data and load fluctuation analysis results. The power grid data processing parameters include the corrected data acquisition frequency, adjusted data verification threshold and data standardization parameters. The abnormal change pattern information includes harmonic level information, voltage fluctuation range and frequency change trend information. The power grid stability score includes the power grid stability index, parameter volatility data and power grid quality parameter statistical results. The power quality monitoring data includes nonlinear load analysis results, load change impact assessment information and power quality change trend.

[0067] See also Figure 2 Based on the power grid operation log information, the current, voltage and frequency data of the transformer under various load conditions are collected in real time, and the power grid operation data and time information are recorded. The steps for generating the transformer operation data are as follows:

[0068] S101: Based on the power grid operation log information, the current, voltage and frequency data of the transformer in the power grid are collected in real time, and the timestamp and load condition label are recorded to generate the operation data collection record;

[0069] Based on the power grid operation log information, the current, voltage and frequency data of the transformers in the power grid are collected in real time. The current, voltage and frequency data are obtained from each node through sensors. The data is recorded in combination with timestamps and load condition tags. The data points are closely associated with the specific load states at the moment of capture. Combined with automatic fault diagnosis and network optimization algorithms, data capture is maximized and errors are minimized, which improves data reliability and ensures accuracy and efficiency in subsequent data processing and analysis. The collected current, voltage and frequency data will record timestamps and be associated with load condition tags to generate operation data collection records.

[0070] S102: Based on the operation data collection records, the operation data is cleaned, including removing abnormal values ​​and filling missing values, and generating data processing results;

[0071] Based on the operation data collection records, the operation data is cleaned to remove outliers that appear in the collection and fill in missing values ​​in the data. By setting the anomaly detection parameters, the standard deviation and quartile method are used to identify and eliminate data points that deviate from the normal range. Sudden changes in current or voltage usually indicate equipment failure or measurement errors. The data will be regarded as abnormal and eliminated. For missing data, interpolation and historical average values ​​are used to fill in the data based on the data of adjacent time points to ensure data integrity and enhance data accuracy. This provides a data basis for data processing and analysis. The generated data processing results include filtered and completed current, voltage and frequency information, providing a clean and reliable data source for data analysis.

[0072] S103: based on the data processing result, grouping the operation data according to the load condition and time information, and performing standardization processing on the data to generate transformer operation data;

[0073] In the above content, based on the cleaned data, the data is grouped according to the load conditions and time information, and the data is standardized according to the formula Calculate the standardized transformer operating data.

[0074] In the formula, X represents the original data, μ represents the mean value of each group of data, and σ represents the standard deviation.

[0075] Detailed explanation of the formula and the process of formula calculation and derivation:

[0076] After the data is grouped, the mean and standard deviation of each group of data are calculated, and each data point is converted to a standard score using the formula. Assuming that the mean of a group of data is 50A current and the standard deviation is 5A, the standard score of the data point with a current value of 60A after conversion is:

[0077]

[0078] It means that the data point is 2 standard deviations above the mean. Data between different groups can be compared on the same scale for statistical analysis and pattern recognition.

[0079] See also Figure 3 Based on the transformer operation data, monitor the response of the transformer under various grid load changes, analyze the performance stability of the transformer by comparing the data changes under various load states, and generate the transformer characteristic information in the following steps:

[0080] S201: Based on the transformer operation data, by comparing the current, voltage and frequency changes under various load states, recording the data fluctuations under various states, and generating load state comparison data;

[0081] Based on the transformer operation data, by comparing the current, voltage and frequency changes under various load conditions, the execution process includes recording the fluctuation of the data under each load state, classifying the data according to the load condition labels in different time periods, including peak, trough and normal operation, and calculating the change rate and volatility of the current, voltage and frequency under each state respectively, revealing the operating characteristics of the transformer under different load conditions. For the statistical analysis of data fluctuations, the mean and variance statistical methods are used for calculation to ensure that the typical fluctuation behavior under each state is accurately captured, the performance of the transformer under various load conditions is analyzed, and load state comparison data is generated to reflect the electrical characteristics of the transformer under different states, providing basic data for performance evaluation.

[0082] S202: Based on the load state comparison data, calculate the reaction time and data fluctuation amplitude of the transformer under various load states, evaluate the response capability of the transformer to rapid changes, and generate transformer performance evaluation data;

[0083] Based on the load state comparison data, the reaction time and data fluctuation amplitude of the transformer under various load states are calculated, and the current and voltage data of the transformer under different load conditions are analyzed. Through the load state comparison data, the current and voltage readings of each load state are calibrated, including high load, low load, and normal operation. The reaction time in each state is calculated, that is, the time required from the load change to the stabilization of the transformer data. The data fluctuation amplitude, that is, the maximum deviation of the data during the load change, is calculated. The response ability of the transformer to rapid changes is evaluated, and the performance of the transformer in actual operation is understood. Data support is provided for equipment optimization and maintenance decisions. The generated transformer performance evaluation data reflects the performance of the transformer under different operating conditions, providing a key basis for the stable operation and efficiency improvement of the power grid.

[0084] S203: Based on the transformer performance evaluation data, analyze the performance stability of the transformer under various power grid load conditions and generate transformer characteristic information;

[0085] Based on the transformer performance evaluation data, the performance stability of the transformer under various power grid load conditions is analyzed, including evaluating the performance of the transformer under different load states and generating transformer characteristic information. Using the previous performance evaluation data, the changing trends of the transformer's response time and fluctuation amplitude under various load conditions are analyzed. Through regression analysis or variance analysis, it is evaluated whether the changes are within the allowable performance stability range, and performance degradation or abnormal behavior is detected. The long-term reliability and operating efficiency of the transformer are described. The generated transformer characteristic information illustrates the transformer's adaptability to various operating loads under actual power grid conditions, providing a data basis for power grid management.

[0086] See also Figure 4Based on the transformer characteristic information, according to the real-time monitored grid load data and environmental conditions, the data processing parameters are adjusted, and the verification threshold of the data acquisition is calibrated to match the state change of the grid. The steps of generating the grid data processing parameters are as follows:

[0087] S301: Based on the mutual inductor characteristic information, according to the real-time monitored power grid load data and environmental conditions, analyzing the range of data processing parameters that need to be adjusted, and generating an adjustment index list;

[0088] Based on the transformer characteristic information, according to the real-time monitored power grid load data and environmental conditions, the range of data processing parameters that need to be adjusted is analyzed. By comparing the actual monitoring data with the transformer characteristics, the data processing parameters that need to be adjusted are determined, including evaluating the efficiency and accuracy of the transformer data processing algorithm according to the current power grid load and environmental conditions, including the impact of temperature, humidity and load fluctuations on the transformer performance. According to the target real-time data, the threshold of the filter and the standard for data anomaly detection are adjusted to ensure the adaptability and sensitivity of data processing. The analysis of the adjustment range is based on statistical models and prediction algorithms to predict the response of the transformer under different environmental conditions, improve data quality to ensure the reliability and response speed of the power grid monitoring system, and generate a list of adjustment indicators, reflecting the parameters that need to be optimized to maintain data processing efficiency under various environmental and load conditions.

[0089] S302: According to the adjustment index list, adjust the verification threshold of data collection to match the operation status and environmental changes of the power grid, and generate data collection parameters;

[0090] In the above content, according to the adjustment index list, the verification threshold of data collection is adjusted to match the operation status and environmental changes of the power grid, according to the formula T new =T old ×(1+k′·ΔE), generate new data acquisition parameters.

[0091] Where, T old represents the original verification threshold, k′ represents the adjustment coefficient, and ΔE represents the environmental change index.

[0092] Detailed explanation of the formula and the process of formula calculation and derivation:

[0093] According to the environmental change index, adjust the original verification threshold to adapt to the new environmental conditions. Assuming that the original threshold is 10V, the environmental change index is 0.05, and the adjustment coefficient is 0.1, the new threshold is

[0094] T new =10×(1+0.1×0.05)=10.05V

[0095] Used to ensure that the sensitivity and accuracy of data collection match environmental changes, ensuring data quality and the stability of equipment operation.

[0096] S303: Apply data acquisition parameters to format and standardize power grid operation data, optimize data consistency, and generate power grid data processing parameters;

[0097] Apply data collection parameters, format and standardize the power grid operation data, optimize data consistency, use the newly adjusted collection parameters to process the power grid data, ensure that all data are managed in accordance with a unified standard format and specification, format the data, including unifying the timestamp format of the data, standardize the data, including adjusting the data scale to make it suitable for the analysis model, use Z-score or minimum and maximum standardization technology to ensure that data from multiple sources and time periods are comparable, improve the efficiency of data processing, and improve the accuracy of subsequent data analysis and decision-making. The generated power grid data processing parameters reflect the optimization of data consistency and provide data support for the stable and reliable operation of the power grid.

[0098] See also Figure 5 ,Using the power grid data processing parameters, by analyzing various power grid quality parameters, identifying the occurrence frequency of various abnormal parameters, and quantifying the impact on power quality, the steps of generating abnormal change pattern information are as follows:

[0099] S401: using the power grid data processing parameters, real-time monitoring of harmonics, voltage fluctuations and frequency deviations in the power grid, recording changes in multiple key frequency indicators, and generating key frequency change records;

[0100] Utilize the power grid data processing parameters to monitor the harmonics, voltage fluctuations and frequency deviations in the power grid in real time, record the changes of multiple key frequency indicators, use data acquisition technology and analysis tools to monitor and record the key performance indicators of the power grid, and continuously monitor the harmonic levels of current and voltage, voltage fluctuations, and frequency deviations from standard values ​​through the real-time data acquisition module. When the target indicators exceed the preset safety range, the abnormal events are automatically recorded and classified as key frequency changes. The generated key frequency change records show the fluctuations in power grid performance during the target period, providing basic data for subsequent analysis.

[0101] S402: Analyze key frequency change records, identify abnormal change patterns, calculate regression coefficients of multiple parameters, quantify the impact of multiple parameter changes on power quality, and generate abnormal impact analysis information;

[0102] The specific formula for calculating the regression coefficients of multiple parameters is:

[0103]

[0104] Among them, β iis the regression coefficient of the ith power quality parameter, which indicates the expected change in the power quality index y when the parameter changes by one unit. It is a statistical measure of the direct effect of parameter i on power quality when controlling other parameters. i represents the index of the power quality parameter, which is used to distinguish different power quality parameters, including voltage, current, frequency and harmonics, to ensure that each parameter is analyzed separately. j represents the index of the time point of data collection, which is used to identify data at different time points to ensure time series analysis of power quality. ij represents the actual value of the ith power quality parameter measured at the jth time point, which is used to capture the performance of the parameter at the target time point. is the average of all observed values ​​of the ith power quality parameter, used to evaluate the typical performance level of this parameter, y j is the measured value of the power quality index at the jth time point, reflecting the power quality status at that moment. It is the average value of all power quality indicators, providing a reference benchmark for evaluating the deviation of power quality from the average state at each time point. n is the total number of observation points in the data set, indicating the total number of measurements, which is the basis for determining the scope and depth of the overall analysis. By calculating the regression coefficient β i , analyze the relative importance of multiple parameters in power quality monitoring.

[0105] formula:

[0106]

[0107] Detailed explanation of the formula and the process of formula calculation and derivation:

[0108] The formula is used to calculate the regression coefficient of each power quality parameter's effect on the overall power quality. The results are used to evaluate the expected change in power quality when different parameters change.

[0109] Parameter meaning and setting value:

[0110] x ij is the actual value of the ith power quality parameter measured at the jth time point, which is used to capture the performance of the parameter at a specific time point. is the average value of all observed values ​​of the ith power quality parameter, which is used to evaluate the typical performance level of the parameter. Assuming that the measured values ​​of the voltage parameter at three different time points are 230, 235, and 225, the average value is 230.

[0111] y j is the measured value of the power quality index at the jth time point, reflecting the power quality status at that moment. It is the average value of all power quality indicators and provides a reference benchmark for evaluating the deviation of power quality at each time point from the average state. Assuming that the measured values ​​at three different time points are 90, 85, and 88, the average value is 87.67.

[0112] n is the total number of observation points in the data set, indicating the total number of measurements, which is used to determine the basis for the overall analysis scope and depth, and is assumed to be 3;

[0113] Substitute the parameters into the formula for calculation:

[0114]

[0115]

[0116] Results i =-0.3 indicates that for every unit increase in voltage, the power quality score decreases by 0.3 units, which is used to evaluate the expected change in power quality when the voltage parameters change.

[0117] S403: Analyze and record the occurrence frequencies of various abnormal parameters according to the abnormal impact analysis information, perform quantitative analysis, and generate abnormal change pattern information;

[0118] According to the abnormal impact analysis information, analyze and record the occurrence frequency of various abnormal parameters, conduct quantitative analysis, identify and record the occurrence frequency of abnormal parameters, including voltage surges or frequency jumps, and determine the types of parameters that frequently cause power quality problems by analyzing the data in the abnormal impact analysis information. Use statistical software and data analysis tools to calculate the frequency and statistical significance of various abnormal events, analyze the correlation between abnormal events and the operating status of the power grid, and generate abnormal change pattern information to provide analysis on the instability of power grid performance, providing data support for the formulation of monitoring strategies and preventive measures.

[0119] See also Figure 6 ,According to the abnormal change pattern information, the operation stability of the power grid is evaluated by calculating the statistical data and volatility of various power grid quality parameters. The specific steps for generating the power grid stability score are as follows:

[0120] S501: Analyze and calculate the statistical data and volatility of multiple key power grid quality parameters, including harmonic levels and voltage fluctuation frequencies, based on the abnormal change pattern information, and generate power grid quality statistical data;

[0121] According to the abnormal change pattern information, key quality parameters in the power grid are analyzed and calculated, including harmonic levels and voltage fluctuation frequency, including analysis of historical data, and evaluation of the volatility of target parameters through standard deviation and coefficient of variation. Statistical analysis of harmonic levels is used to reveal the prevalence and potential hazards of harmonics in the power grid. Analysis of voltage fluctuation frequency is used to reflect the responsiveness and stability of the power grid to load changes. Statistical data is used to understand the real-time power grid status, provide a basis for formulating improvement measures and maintenance strategies, manage and optimize the performance of the power grid, and ensure the reliability and efficiency of power supply.

[0122] S502: Based on the power grid quality statistical data, the stability of the power grid operation is evaluated in real time, including the power grid steady-state deviation and response speed, and a stability calculation result is generated;

[0123] In the above content, based on the statistical data of power grid quality, the stability of power grid operation is evaluated in real time, including steady-state deviation and response speed, according to the formula Generate stability calculation results.

[0124] Where x represents the real-time parameters of the power grid, including voltage or frequency, x0 represents the target parameter value, k is the adjustment coefficient, and S is the stability index.

[0125] Detailed explanation of the formula and the process of formula calculation and derivation:

[0126] The stability is evaluated using the sigmoid function, which quantifies the degree to which the parameter deviates from the target value. For example, if the target voltage is 220V, the actual voltage is 210V, and the adjustment coefficient is set to 0.1, then the stability index is

[0127]

[0128] Dynamically monitor the operation status of the power grid and evaluate stability in real time.

[0129] S503: Based on the stability calculation results, through time series analysis, evaluate and predict the change trend of power grid stability and operating parameters, and generate a power grid stability score;

[0130] Based on the stability calculation results, time series analysis is used to evaluate and predict the changing trends of power grid stability and operating parameters. Historical data is used to model power grid behavior. By identifying patterns and trends in time series, power grid stability issues are predicted. Autoregressive models and moving average models are used to evaluate the long-term and short-term trends of parameter changes, helping power grid operators prepare for and respond to potential unstable factors. A method for evaluating the overall health of the power grid is provided. The generated stability score is a quantitative prediction of the future operating status of the power grid, which is used to maintain the safety and efficient operation of the power grid.

[0131] See also Figure 7, based on the grid stability score, combined with real-time grid operation data, the impact of various nonlinear loads on power quality and grid stability is evaluated. The steps for generating power quality monitoring data are as follows:

[0132] S601: Based on the grid stability score and combined with real-time grid operation data, analyze the impact of various nonlinear loads on grid stability and generate load impact assessment data;

[0133] Based on the grid stability score and combined with real-time grid operation data, the impact of various nonlinear loads on grid stability is analyzed. The grid stability score is used to evaluate and analyze nonlinear loads, including the impact of the startup and operation of large industrial equipment and electronic equipment on grid stability. The real-time data acquisition system monitors the grid operation parameters, including the instant changes in voltage and frequency, and combines them with the grid stability score data. Regression analysis and variance analysis are used to evaluate the impact size and impact mode of nonlinear loads. The generated load impact assessment data records the impact of nonlinear loads on grid stability, providing a scientific basis for power quality optimization.

[0134] S602: Analyze and quantify the impact of various power operation parameter changes on power quality, including voltage drop and frequency change, based on the load impact assessment data, and generate quality impact prediction information;

[0135] Based on the load impact assessment data, the impact of changes in various power operating parameters on power quality, including voltage drops and frequency changes, is analyzed and quantified. The changes in grid operating parameters including voltage and frequency are analyzed to evaluate the impact on power quality. The load impact assessment data is used to quantify the impact of voltage drops and frequency changes through statistical models and machine learning algorithms. The analysis results are used to identify weak links and potential risks in the power grid. The generated quality impact prediction information describes the changing trend of power quality under various load conditions, providing key data support for grid management and decision-making.

[0136] S603: Analyze the impact of various nonlinear load changes on grid stability based on the quality impact prediction information, evaluate the power quality under various load conditions, and generate power quality monitoring data;

[0137] In the above content, based on the quality impact prediction information, the impact of various nonlinear load changes on the grid stability is analyzed, and the power quality under various load conditions is calculated according to the formula Q=V×F.

[0138] In the formula, Q represents power quality, V represents voltage stability index, and F represents frequency stability index.

[0139] Detailed explanation of the formula and the process of formula calculation and derivation:

[0140] Based on the voltage and frequency change data provided in the quality impact prediction information, assuming that the voltage stability index V is 0.95 and the frequency stability index F is 0.98, the power quality under specific nonlinear load conditions is evaluated by calculating the product of these two indicators. Substituting the values ​​into the formula, we can get

[0141] Q = 0.95 × 0.98 = 0.931

[0142] The results are used to quantify the power quality, provide data support for practical operations for power grid operation and maintenance, and evaluate the power quality of the power grid under the influence of nonlinear loads.

[0143] See also Figure 8 A power quality monitoring system considering the broadband transmission characteristics of a transformer, the power quality monitoring system considering the broadband transmission characteristics of a transformer is used to execute the above-mentioned power quality monitoring method considering the broadband transmission characteristics of a transformer, the system comprises:

[0144] The data acquisition and analysis module collects the current, voltage and frequency data of the transformer in real time based on the grid operation log information, records the time information, analyzes the response of the transformer under various grid load conditions, evaluates the performance stability of the transformer, and generates transformer performance analysis data;

[0145] The parameter adjustment module uses the transformer performance analysis data to adjust the data processing parameters according to the real-time monitored grid load data and environmental conditions, calibrates the verification threshold of data acquisition to match the state change of the grid, and generates a processing parameter adjustment record;

[0146] The anomaly detection module uses the processing parameter adjustment records to analyze various quality parameters of the power grid, identify the frequency of occurrence of abnormal parameters, and quantify the impact on power quality to generate quality parameter analysis results;

[0147] The stability calculation module calculates the statistical data and volatility of various quality parameters of the power grid based on the quality parameter analysis results, evaluates the operational stability of the power grid, and generates stability evaluation records;

[0148] The load impact assessment module evaluates the impact of various nonlinear loads on power quality and grid stability based on stability assessment records and combined with real-time grid operation data, analyzes the impact of load changes on grid stability, and generates power quality monitoring data.

[0149] The above are only preferred embodiments of the present invention and are not intended to limit the present invention in other forms. Any technician familiar with the profession may use the technical contents disclosed above to change or modify them into equivalent embodiments with equivalent changes and apply them to other fields. However, any simple modification, equivalent change and modification made to the above embodiments based on the technical essence of the present invention without departing from the technical solution of the present invention still falls within the protection scope of the technical solution of the present invention.

Claims

1. A method for monitoring power quality considering the broadband transmission characteristics of a transformer, characterized in that: The following steps are involved: Based on the power grid operation log information, the current, voltage and frequency data of the transformer under various load conditions are collected in real time, and the power grid operation data and time information are recorded to generate the transformer operation data; Based on the transformer operation data, monitor the response of the transformer under various grid load changes, analyze the performance stability of the transformer by comparing the data changes under various load states, and generate transformer characteristic information; Based on the mutual inductor characteristic information, according to the real-time monitored power grid load data and environmental conditions, the data processing parameters are adjusted, the verification threshold of the data acquisition is calibrated to match the state change of the power grid, and the power grid data processing parameters are generated; By using the power grid data processing parameters, the various power grid quality parameters are analyzed to identify the occurrence frequency of various abnormal parameters, and the impact on power quality is quantified to generate abnormal change pattern information; According to the abnormal change pattern information, the operational stability of the power grid is evaluated by calculating the statistical data and volatility of various power grid quality parameters to generate a power grid stability score; Based on the grid stability score and in combination with real-time grid operation data, the impact of various nonlinear loads on power quality and grid stability is evaluated to generate power quality monitoring data.

2. The power quality monitoring method considering the broadband transmission characteristics of the transformer according to claim 1 is characterized in that: The transformer operation data includes current data acquisition records, voltage waveform information and frequency deviation data, the transformer characteristic information includes load response time, steady-state performance evaluation data and load fluctuation analysis results, the power grid data processing parameters include corrected data acquisition frequency, adjusted data verification threshold and data standardization parameters, the abnormal change pattern information includes harmonic level information, voltage fluctuation range and frequency change trend information, the power grid stability score includes power grid stability index, parameter volatility data and power grid quality parameter statistical results, and the power quality monitoring data includes nonlinear load analysis results, load change impact assessment information and power quality change trend.

3. The power quality monitoring method considering the broadband transmission characteristics of the transformer according to claim 1 is characterized in that: Based on the power grid operation log information, the current, voltage and frequency data of the transformer under various load conditions are collected in real time, and the power grid operation data and time information are recorded. The specific steps for generating the transformer operation data are as follows: Based on the power grid operation log information, the current, voltage and frequency data of the transformer in the power grid are collected in real time, and the timestamp and load condition label are recorded to generate operation data collection records; Based on the operation data collection record, the operation data is cleaned, including removing outliers and filling missing values, to generate data processing results; Based on the data processing result, the operation data is grouped according to load conditions and time information, and the data is standardized to generate transformer operation data.

4. The power quality monitoring method considering the broadband transmission characteristics of the transformer according to claim 1 is characterized in that: Based on the transformer operation data, the response of the transformer under various grid load changes is monitored, and the performance stability of the transformer is analyzed by comparing the data changes under various load states. The steps of generating transformer characteristic information are specifically as follows: Based on the transformer operation data, by comparing the current, voltage and frequency changes under various load states, recording the data fluctuations under various states, and generating load state comparison data; Based on the load state comparison data, the reaction time and data fluctuation amplitude of the transformer under various load states are calculated, the response capability of the transformer to rapid changes is evaluated, and transformer performance evaluation data is generated; Based on the transformer performance evaluation data, the performance stability of the transformer under various power grid load conditions is analyzed to generate transformer characteristic information.

5. The power quality monitoring method considering the broadband transmission characteristics of the transformer according to claim 1, characterized in that: Based on the transformer characteristic information, according to the real-time monitored grid load data and environmental conditions, the data processing parameters are adjusted, and the verification threshold of the data acquisition is calibrated to match the state change of the grid. The steps of generating the grid data processing parameters are specifically as follows: Based on the transformer characteristic information, according to the real-time monitored power grid load data and environmental conditions, analyzing the range of data processing parameters that need to be adjusted, and generating an adjustment index list; According to the adjustment index list, the verification threshold of data acquisition is adjusted to match the operation status and environmental changes of the power grid, and data acquisition parameters are generated; The data acquisition parameters are applied to format and standardize the power grid operation data, optimize data consistency, and generate power grid data processing parameters.

6. The power quality monitoring method considering the broadband transmission characteristics of the transformer according to claim 1, characterized in that: The steps of using the power grid data processing parameters to analyze a variety of power grid quality parameters, identifying the occurrence frequencies of a variety of abnormal parameters, and quantifying the impact on power quality to generate abnormal change pattern information are specifically as follows: Using the power grid data processing parameters, real-time monitoring of harmonics, voltage fluctuations and frequency deviations in the power grid is performed, changes in multiple key frequency indicators are recorded, and key frequency change records are generated; Analyze the key frequency change records, identify abnormal change patterns, calculate regression coefficients of multiple parameters, quantify the impact of multiple parameter changes on power quality, and generate abnormal impact analysis information; According to the abnormal impact analysis information, the occurrence frequencies of various abnormal parameters are analyzed and recorded, quantitative analysis is performed, and abnormal change pattern information is generated.

7. The method for monitoring power quality considering the broadband transmission characteristics of a transformer according to claim 6, characterized in that: The specific formula for calculating the regression coefficients of multiple parameters is: Among them, β i is the regression coefficient of the ith power quality parameter, which indicates the expected change in the power quality index y when the parameter changes by one unit. It is a statistical measure of the direct effect of parameter i on power quality when controlling other parameters. i represents the index of the power quality parameter, which is used to distinguish different power quality parameters, including voltage, current, frequency and harmonics, to ensure that each parameter is analyzed separately. j represents the index of the time point of data collection, which is used to identify data at different time points to ensure time series analysis of power quality. ij represents the actual value of the ith power quality parameter measured at the jth time point, and is used to capture the performance of the parameter at the target time point. is the average of all observed values ​​of the ith power quality parameter, used to evaluate the typical performance level of this parameter, y j is the measured value of the power quality index at the jth time point, reflecting the power quality status at that moment. It is the average value of all power quality indicators, providing a reference benchmark for evaluating the deviation of power quality from the average state at each time point. n is the total number of observation points in the data set, indicating the total number of measurements, which is the basis for determining the scope and depth of the overall analysis. By calculating the regression coefficient β i , analyze the relative importance of multiple parameters in power quality monitoring.

8. The power quality monitoring method considering the broadband transmission characteristics of the transformer according to claim 1, characterized in that: According to the abnormal change pattern information, the operation stability of the power grid is evaluated by calculating the statistical data and volatility of various power grid quality parameters, and the steps of generating the power grid stability score are specifically as follows: Analyze and calculate the statistical data and volatility of multiple key power grid quality parameters, including harmonic levels and voltage fluctuation frequencies, based on the abnormal change pattern information, and generate power grid quality statistical data; Based on the power grid quality statistical data, real-time evaluation of the stability of power grid operation, including power grid steady-state deviation and response speed, and generation of stability calculation results; Based on the stability calculation results, the changing trends of power grid stability and operating parameters are evaluated and predicted through time series analysis to generate a power grid stability score.

9. The method for monitoring power quality considering the broadband transmission characteristics of a transformer according to claim 1, characterized in that: Based on the grid stability score and in combination with real-time grid operation data, the impact of various nonlinear loads on power quality and grid stability is evaluated, and the steps of generating power quality monitoring data are specifically as follows: Based on the grid stability score and in combination with real-time grid operation data, the impact of various nonlinear loads on grid stability is analyzed to generate load impact assessment data; Analyze and quantify the impact of various power operation parameter changes on power quality, including voltage drop and frequency change, based on the load impact assessment data, and generate quality impact prediction information; According to the quality impact prediction information, the impact of various nonlinear load changes on the stability of the power grid is analyzed, the power quality under various load conditions is evaluated, and power quality monitoring data is generated.

10. A power quality monitoring system considering the broadband transmission characteristics of a transformer, characterized in that: According to the method for monitoring power quality taking into account the broadband transmission characteristics of the transformer according to any one of claims 1 to 9, the system comprises: The data acquisition and analysis module collects the current, voltage and frequency data of the transformer in real time based on the grid operation log information, records the time information, analyzes the response of the transformer under various grid load conditions, evaluates the performance stability of the transformer, and generates transformer performance analysis data; The parameter adjustment module uses the transformer performance analysis data to adjust the data processing parameters according to the real-time monitored grid load data and environmental conditions, calibrates the verification threshold of data acquisition to match the state change of the grid, and generates a processing parameter adjustment record; The anomaly detection module uses the processing parameter adjustment records to analyze various quality parameters of the power grid, identify the frequency of occurrence of abnormal parameters, and quantify the impact on power quality to generate quality parameter analysis results; The stability calculation module calculates the statistical data and volatility of various quality parameters of the power grid based on the quality parameter analysis results, evaluates the operational stability of the power grid, and generates stability evaluation records; The load impact assessment module evaluates the impact of various nonlinear loads on power quality and grid stability based on stability assessment records and combined with real-time grid operation data, analyzes the impact of load changes on grid stability, and generates power quality monitoring data.

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