Reactive power dynamic regulation and control method and system for transformer substation

Abstract

The invention discloses a reactive power dynamic regulation and control method and system for a transformer substation, and the method comprises the steps: collecting key parameters through a power grid sensor, and generating time series data; and then analyzing the response relationship of each node voltage to reactive power by using a voltage-controlled quantity sensitivity model S (U, Q) = U / Q, and further calculating the optimal reactive output target value of the SVG and the capacitor bank by using a dynamic programming algorithm. According to the reactive power dynamic regulation and control method and system for the transformer substation provided by the invention, the voltage-reactive power dynamic response characteristics are accurately grasped, so that the voltage and the power factor of the power distribution network are accurately regulated, the voltage fluctuation is effectively inhibited, and the operation stability and the economical efficiency of the power distribution network are improved; an intelligent and adaptive solution is provided for reactive power optimization of the power distribution network, and the method has important engineering application value.

Description

Technical Field

[0001] This invention relates to the field of power supply and distribution technology, and in particular discloses a method and system for dynamic control of reactive power in substations. Background Technology

[0002] As the core pillar of energy supply in modern society, the stable operation and efficient regulation of the power system are of vital importance to ensuring economic development and social life.

[0003] With the rapid growth of electricity demand and the widespread integration of renewable energy, the complexity of power grid operation is increasing, and problems such as voltage fluctuations and power factor decline are becoming more prominent, urgently requiring advanced control technologies to ensure system safety and economy.

[0004] Traditional solutions often rely on static compensation devices or single control strategies. While these can alleviate the problem to some extent, they often exhibit slow response and insufficient regulation accuracy when faced with real-time load changes and the dynamic impact of distributed power sources.

[0005] These methods often lack consideration for multivariate collaborative optimization, leading to inefficient resource utilization and potentially triggering new instabilities.

[0006] Currently, the core challenges in the field of dynamic regulation of power systems mainly focus on how to achieve real-time monitoring and precise control of power grid parameters, and how to coordinate the collaborative operation of various devices.

[0007] Specifically, the difficulty in modeling the sensitivity of voltage-control quantities, the inadequacy of dynamic optimization of compensation capacity, and the poor coordination of action sequences of various types of equipment have become technical bottlenecks that urgently need to be overcome.

[0008] Because these factors have not been effectively addressed, the system is prone to lag or over-adjustment when facing complex operating conditions, which in turn affects the stability and operating efficiency of the power grid.

[0009] Furthermore, the shortcomings of existing technologies in real-time data processing and control strategy generation also limit the flexibility and adaptability of dynamic regulation.

[0010] Therefore, how to optimize the compensation capacity through a voltage-control quantity sensitivity model based on real-time monitoring of key parameters such as grid voltage, active and reactive loads, and power factor, and how to coordinate the action sequences of various types of equipment such as SVG (Static Var Generator), capacitor banks, and main transformer tap changers, has become a key issue in improving the dynamic regulation capability of the power system. Summary of the Invention

[0011] This invention provides a method and system for dynamic reactive power control in substations, aiming to solve the problem of... One aspect of the present invention relates to a method for dynamic control of reactive power in a substation, comprising the following steps: Acquire voltage, active power, reactive power, and power factor data from power grid sensors to generate time series parameters of load change trends and distributed power source fluctuations; Based on the time series parameters, the voltage-control quantity sensitivity model S(U,Q) is adopted. U / Q calculates the response relationship between the voltage at each node and the reactive power, where S(U,Q) represents the partial derivative of voltage U with respect to reactive power Q. Based on the response relationship, a dynamic programming algorithm is used to generate the reactive power output target values ​​of SVG and capacitor bank. The target values ​​correspond to the voltage fluctuation amplitude and power factor offset.

[0012] Furthermore, the steps for acquiring voltage, active power, reactive power, and power factor data from power grid sensors to generate time series parameters of load change trends and distributed power source fluctuations include: Acquire real-time voltage data, active power, reactive power, and power factor from power grid sensors and store them as an initial dataset; The load change trend is obtained by calculating the time series of load changes using the initial dataset; Time series analysis was used to process load change trends and determine trend characteristic parameters. Extract active and reactive power related to distributed power sources from the initial dataset to generate power fluctuation sequences; Fourier transform is applied to the power supply fluctuation sequence to obtain the frequency domain distribution of the fluctuation parameters; If the frequency domain distribution exceeds a preset threshold, the fluctuation parameters are smoothed to obtain stable fluctuation characteristics. The correlation coefficient between the stable fluctuation characteristics and the load change trend is determined by comparing the two.

[0013] Furthermore, the steps for determining the correlation coefficient between stable fluctuation characteristics and load change trends include: By acquiring data on stable fluctuation characteristics and load change trends, time series analysis is used to calculate the correlation coefficient between the two. By comparing the fluctuation characteristics and changing trends obtained from time series analysis, the initial value of the correlation coefficient is determined; If the initial value of the correlation coefficient exceeds the preset threshold, the data range of the fluctuation feature is adjusted through feature extraction to obtain the corrected correlation coefficient. Based on the corrected correlation coefficient, the Pearson correlation algorithm is used to determine the strength of the correlation between stable fluctuations and load changes; Based on the results of the correlation strength assessment, the characteristic calculation values ​​of trend comparison are obtained to determine the final correlation coefficient; For the final correlation coefficient, data comparison analysis is used to determine the degree of matching between the load change trend and fluctuation characteristics, and trend analysis results are obtained. Based on the trend analysis results, the long-term correlation coefficient between stable fluctuation characteristics and load change trends is calculated, and complete comparative data is output.

[0014] Furthermore, based on the time series parameters, a voltage-control quantity sensitivity model S(U,Q) is adopted. U / The steps for calculating the response of each node voltage to reactive power include: Voltage and reactive power data were obtained from an initial dataset, and a voltage-control quantity sensitivity model S(U,Q) was adopted. U / Q calculates the sensitivity parameters and determines the response relationship of each node voltage to reactive power; Sensitivity parameters are extracted from the voltage-control quantity sensitivity model, and node distribution sequences are generated for power grid nodes to obtain the sensitivity distribution characteristics between nodes; Based on the node distribution sequence, the changing trend of the sensitivity parameter is determined by partial derivative analysis to judge the degree of mutual influence between nodes. Obtain trend data, process sensitivity parameters using data smoothing methods, and obtain a stable response relationship sequence; For stable response relationship sequences, correlation analysis is used to determine the correlation coefficient between voltage data and reactive power, and to determine the distribution of control variables at grid nodes; Key node parameters are extracted from the distribution of control variables, and cluster analysis is used to divide the node groups to obtain the sensitivity characteristics between the node groups. By analyzing the sensitivity characteristics among node groups, we can determine the optimization direction of reactive power adjustment on voltage data and ultimately determine the adjustment parameters for the response relationship.

[0015] Furthermore, based on the response relationship, the steps for generating the SVG and the reactive power output target values ​​of the capacitor bank using a dynamic programming algorithm include: The initial reactive power output data of the SVG device and capacitor bank are obtained by using a dynamic programming algorithm, and the basic target value is determined through the calculation process. By analyzing the voltage fluctuation amplitude, the basic target value is determined, and the adjustment coefficient corresponding to the fluctuation amplitude is obtained. The adjustment coefficient is matched with the power factor offset. If the offset exceeds the preset threshold, the reactive power is adjusted through dynamic programming to obtain the optimized target value. The voltage fluctuation trend is extracted from the optimization target value, and the support vector machine algorithm is used to predict the trend and determine the predicted fluctuation amplitude. Based on the relationship between the predicted fluctuation amplitude and the factor offset, the output allocation ratio of the capacitor bank is obtained, and the reactive power output data after allocation is obtained. The operating parameters of the SVG device are updated by the allocated reactive power output data. If the parameters meet the fluctuation range requirements, the final target value is determined. The power factor variation trend is extracted from the final target value, and the relationship between the variation trend and the offset is analyzed by linear regression algorithm to obtain stable operating parameters.

[0016] Another aspect of the present invention relates to a dynamic reactive power control system for a substation, comprising: The acquisition module uses five sensors to acquire voltage, active power, reactive power, and power factor data from the power grid, and generates time series parameters of load change trends and distributed power source fluctuations. The calculation module is used to calculate the voltage-control quantity sensitivity model S(U,Q) based on time series parameters. U / Q calculates the response relationship between the voltage at each node and the reactive power, where S(U,Q) represents the partial derivative of voltage U with respect to reactive power Q. The generation module is used to generate the reactive power output target values ​​of the SVG and the capacitor bank based on the response relationship and using a dynamic programming algorithm. The target values ​​correspond to the voltage fluctuation amplitude and the power factor offset.

[0017] Furthermore, the acquisition module includes: The first acquisition unit is used to acquire voltage data, active power, reactive power and power factor collected in real time by the power grid sensor and store them as an initial dataset. The second acquisition unit is used to calculate the time series of load changes using the initial dataset to obtain the load change trend. The first determining unit is used to process load change trends using time series analysis methods and determine trend characteristic parameters. The first generation unit is used to extract active and reactive power related to distributed power sources from the initial dataset and generate power fluctuation sequences. The third acquisition unit is used to apply Fourier transform to the power supply fluctuation sequence to obtain the frequency domain distribution of the fluctuation parameters. The fourth acquisition unit is used to smooth the fluctuation parameters and obtain stable fluctuation characteristics if the frequency domain distribution exceeds a preset threshold. The first judgment unit is used to determine the correlation coefficient between stable fluctuation characteristics and load change trends.

[0018] Furthermore, the judgment unit includes: The first calculation subunit is used to calculate the correlation coefficient between stable fluctuation characteristics and load change trends by acquiring data on the two and using time series analysis. The first defined subunit is used to obtain the comparison results of fluctuation characteristics and changing trends from time series analysis, and to determine the initial value of the correlation coefficient; The first acquisition subunit is used to adjust the data range of fluctuation features through feature extraction if the initial value of the correlation coefficient exceeds the preset threshold, so as to obtain the corrected correlation coefficient. The judgment sub-unit is used to determine the correlation strength between stable fluctuations and load changes based on the corrected correlation coefficient and the Pearson correlation algorithm. The second determining subunit is used to obtain the characteristic calculation value of trend comparison based on the judgment result of the correlation strength, and to determine the final correlation coefficient; The second acquisition subunit is used to compare and analyze the degree of matching between the load change trend and fluctuation characteristics based on the final correlation coefficient, and obtain the trend analysis results. The second calculation subunit is used to calculate the long-term correlation coefficient between stable fluctuation characteristics and load change trends based on trend analysis results, and output complete comparison data.

[0019] Furthermore, the computing module includes: The second determining unit is used to acquire voltage and reactive power data from the initial dataset, and adopts the voltage-control quantity sensitivity model S(U,Q)= U / Q calculates the sensitivity parameters and determines the response relationship of each node voltage to reactive power; The fifth acquisition unit is used to extract sensitivity parameters from the voltage-control quantity sensitivity model, generate a node distribution sequence for the power grid nodes, and obtain the sensitivity distribution characteristics between nodes; The second judgment unit is used to determine the changing trend of the sensitivity parameter based on the node distribution sequence through partial derivative analysis, and to judge the degree of mutual influence between the nodes. The sixth acquisition unit is used to acquire trend data and process sensitivity parameters using a data smoothing method to obtain a stable response relationship sequence. The third determining unit is used to determine the correlation coefficient between voltage data and reactive power through correlation analysis for a stable response relationship sequence, and to determine the distribution of control variables at the grid nodes. The seventh acquisition unit is used to extract key node parameters from the distribution of control variables, divide the node groups using cluster analysis, and obtain the sensitivity characteristics between the node groups. The third judgment unit is used to determine the optimization direction of reactive power adjustment on voltage data by using the sensitivity characteristics between node groups, and to determine the final response relationship adjustment parameters.

[0020] Furthermore, the generation module includes: The fourth unit is used to obtain initial reactive power output data from SVG devices and capacitor banks using dynamic programming algorithms, and to determine the basic target value through calculation. The eighth acquisition unit is used to analyze the basic target value through voltage fluctuation amplitude and obtain the adjustment coefficient corresponding to the fluctuation amplitude; The fourth judgment unit is used to match the adjustment coefficient with the power factor offset. If the offset exceeds the preset threshold, the reactive power is adjusted through dynamic planning to obtain the optimized target value. The fourth determining unit is used to extract the voltage fluctuation trend from the optimization target value, and uses the support vector machine algorithm to predict the trend and determine the predicted fluctuation amplitude. The ninth acquisition unit is used to obtain the output allocation ratio of the capacitor bank based on the relationship between the predicted fluctuation amplitude and the factor offset, and to obtain the reactive power output data after allocation. The fifth judgment unit is used to update the operating parameters of the SVG device through the allocated reactive power output data, and determine the final target value if the parameters meet the fluctuation range requirements. The tenth acquisition unit is used to extract the power factor change trend from the final target value, and uses a linear regression algorithm to analyze the relationship between the change trend and the offset to obtain stable operating parameters.

[0021] The beneficial effects achieved by this invention are as follows: This invention provides a method and system for dynamic reactive power control in substations. First, key parameters are collected using grid sensors to generate time-series data. Then, the voltage-control quantity sensitivity model S(U,Q) is used. U / The Q-analysis analyzes the response relationship between voltage and reactive power at each node, and then uses a dynamic programming algorithm to calculate the optimal reactive power output target values ​​for the SVG and capacitor banks. The dynamic reactive power control method and system for substations provided by this invention accurately grasps the dynamic response characteristics of voltage and reactive power, achieving precise adjustment of distribution network voltage and power factor, effectively suppressing voltage fluctuations, and improving the operational stability and economy of the distribution network; it provides an intelligent and adaptive solution for reactive power optimization in distribution networks, and has significant engineering application value. Attached Figure Description

[0022] Figure 1 This is a flowchart illustrating an embodiment of a dynamic reactive power control method for substations according to the present invention. Detailed Implementation

[0023] To better understand the above technical solutions, the following will provide a detailed explanation of the technical solutions in conjunction with the accompanying drawings and specific implementation methods.

[0024] like Figure 1 As shown, the first embodiment of the present invention proposes a method for dynamic control of reactive power in a substation, comprising the following steps: Step S100: Obtain voltage, active power, reactive power and power factor data from the power grid sensor, and generate time series parameters of load change trend and distributed power source fluctuation.

[0025] Time series parameters are parameters used in time series analysis to describe and predict changes in data. Time series data refers to a sequence of values ​​of a variable at different points in time, representing the state of that variable over time.

[0026] The time series parameters of load change trends and distributed power source fluctuations refer to the parameters used in time series analysis to describe and predict load change trends and distributed power source fluctuations.

[0027] Step S200: Based on the time series parameters, use the voltage-control quantity sensitivity model S(U,Q)= U / Q calculates the response relationship between the voltage at each node and the reactive power, where S(U,Q) represents the partial derivative of voltage U with respect to reactive power Q.

[0028] The voltage-control sensitivity model is a mathematical model used to analyze the sensitivity of voltage changes in a power system to specific control variables. It quantifies the dynamic response relationship between system node voltage and reactive power through mathematical formulas. Specifically, it is expressed as the ratio of voltage change to reactive power adjustment.

[0029] Step S300: Based on the response relationship, a dynamic programming algorithm is used to generate the reactive power output target values ​​of the SVG and the capacitor bank. The target values ​​correspond to the voltage fluctuation amplitude and the power factor offset.

[0030] Dynamic programming is an optimization algorithm that improves efficiency by decomposing complex problems into overlapping subproblems and avoiding redundant computations by storing intermediate results. Its core lies in two main characteristics: optimal substructure and overlapping subproblems. It constructs solutions through state transition equations, achieving a space-for-time tradeoff. In this embodiment, dynamic programming is used to generate the target reactive power output values ​​for the SVG and the capacitor bank. The target value for reactive power output refers to the target set value for reactive power output in a power system. In this embodiment, the target value corresponds to the voltage fluctuation amplitude and the power factor offset.

[0031] Furthermore, the dynamic reactive power control method for substations provided in this embodiment includes step S100: Step S110: Obtain the voltage data, active power, reactive power and power factor collected in real time by the power grid sensor, and store them as the initial dataset.

[0032] The system acquires real-time voltage, active power, reactive power, and power factor data from power grid sensors and stores them as an initial dataset to provide a foundation for subsequent analysis.

[0033] For example, a power grid sensor in a certain area collects data once per second: voltage is 220V, active power is 100kW, reactive power is 30kvar, and power factor is 0.95. These data can reflect the operating status of the power grid.

[0034] It should be noted that the initial dataset must be real-time and accurate so that subsequent calculations of load change trends will not be biased due to missing or abnormal data.

[0035] Step S120: Calculate the time series of load changes using the initial dataset to obtain the load change trend.

[0036] By calculating the time series of load changes using the initial dataset, the trend of load changes can be obtained, with the aim of understanding the dynamic characteristics of the power grid load.

[0037] For example, suppose an industrial park collects 24 hours of data. The active power gradually increases from 50kW in the morning to 120kW at noon, and then drops back to 40kW at night. Plotting this as a time series curve, it can be clearly seen that the load peak occurs at noon. This trend reflects the cyclical changes in electricity demand.

[0038] In one possible implementation, the data can be initially smoothed using a sliding window averaging method to reduce noise interference and highlight trend characteristics.

[0039] Step S130: Use time series analysis to process the load change trend and determine the trend characteristic parameters.

[0040] Using time series analysis to process load change trends and determine trend characteristic parameters can further quantify the load change patterns.

[0041] Specifically, autoregressive models can be used to analyze load sequences and extract parameters such as periodicity and trend slope. For example, the analysis results show that the load fluctuates in a 12-hour cycle each day, and the slope indicates that the load increases at a rate of 5 kW / h during the working hours. This helps to predict future load demand and improve the efficiency of power grid dispatching.

[0042] Step S140: Extract the active and reactive power related to distributed power sources from the initial dataset to generate a power fluctuation sequence.

[0043] The active and reactive power related to distributed generation sources are extracted from the initial dataset to generate power fluctuation sequences in order to study the impact of distributed generation sources on the power grid.

[0044] In one embodiment, suppose the active power of a distributed photovoltaic (PV) power source increases from 10kW in the morning to 50kW at noon on a sunny day, but only 20kW on a cloudy day, with reactive power varying accordingly. A power fluctuation sequence records these changes, reflecting the characteristic of power output being affected by external factors such as weather.

[0045] Step S150: Apply Fourier transform to the power supply fluctuation sequence to obtain the frequency domain distribution of the fluctuation parameters.

[0046] Applying Fourier transform to a power supply fluctuation sequence yields the frequency domain distribution of the fluctuation parameters, revealing the fluctuation period and amplitude. Understandably, the Fourier transform converts a time series into a frequency domain signal. For example, a photovoltaic power supply fluctuation sequence showing a main frequency of 1 time per day and an amplitude of 20kW indicates that the fluctuation is predominantly daily.

[0047] Step S160: If the frequency domain distribution exceeds the preset threshold, the fluctuation parameters are smoothed to obtain stable fluctuation characteristics.

[0048] If the frequency domain distribution exceeds a preset threshold, such as an amplitude greater than 30kW, the fluctuation is considered too large and may affect the stability of the power grid. If the frequency domain distribution exceeds the threshold, the fluctuation parameters are smoothed to obtain stable fluctuation characteristics, with the aim of reducing the impact of fluctuations on the power grid.

[0049] Preferably, a moving average method can be used to smooth the data, for example, smoothing the photovoltaic power fluctuations from the drastic 10-50kW range to the 20-40kW range, reducing peak values. The smoothed characteristics are more stable and easier for grid regulation.

[0050] Step S170: By comparing the stable fluctuation characteristics with the load change trend, determine the correlation coefficient between the two.

[0051] By comparing stable fluctuation characteristics with load change trends, the correlation coefficient between the two can be determined, thus assessing the matching degree between power supply fluctuations and load changes.

[0052] In one embodiment, the correlation coefficient is calculated to be 0.85, indicating a high correlation between peak photovoltaic output and peak load. This means that distributed power sources can effectively support load demand and optimize energy allocation. For example, when the load reaches 120kW at noon, photovoltaic power can provide 50kW, reducing the pressure on the main grid. It should be noted that high correlation helps reduce reserve capacity requirements, improves economic efficiency, and enhances power supply reliability.

[0053] Preferably, the dynamic reactive power control method for substations provided in this embodiment includes step S170: Step S171: By acquiring data on stable fluctuation characteristics and load change trends, time series analysis is used to calculate the correlation coefficient between the two.

[0054] By acquiring data on stable fluctuation characteristics and load change trends, and using time series analysis to calculate correlation coefficients, statistical processing can be performed based on historical data.

[0055] For example, a regional power grid collected load data and distributed power generation output data for seven consecutive days, and used time series analysis to make a preliminary comparison of the changing patterns of the two.

[0056] Understandably, time series analysis can arrange data in chronological order to reveal potential dynamic relationships.

[0057] Step S172: Obtain the comparison results of fluctuation characteristics and changing trends from time series analysis, and determine the initial value of the correlation coefficient.

[0058] In one possible implementation, the initial value of the correlation coefficient is determined by comparing the fluctuation characteristics and the changing trends obtained from time series analysis.

[0059] Specifically, assuming that the load change trend over 7 days shows a daily peak of 100kW and a trough of 40kW, while the output fluctuation range of distributed power sources is between 10kW and 50kW.

[0060] The initial correlation coefficient was calculated to be 0.75, indicating a certain correlation between the two. It should be noted that the initial value reflects the characteristics of the original, unadjusted data.

[0061] Step S173: If the initial value of the correlation coefficient exceeds the preset threshold, the data range of the fluctuation feature is adjusted by feature extraction to obtain the corrected correlation coefficient.

[0062] If the initial value of the correlation coefficient exceeds the preset threshold, for example, if the threshold is set to 0.8, then the data range of the fluctuation feature needs to be adjusted through feature extraction.

[0063] For example, filtering methods can be applied to power supply fluctuation data to remove sudden spikes, such as adjusting an abnormal 60kW output to a normal range of less than 50kW. After adjustment, the corrected correlation coefficient may drop to 0.78. This adjustment can more accurately reflect the relationship between long-term trends.

[0064] Step S174: Based on the corrected correlation coefficient, use the Pearson correlation algorithm to determine the correlation strength between stable fluctuations and load changes.

[0065] When using the Pearson correlation algorithm to determine the strength of a correlation based on the corrected correlation coefficient, one can start by examining the linear relationship of the data.

[0066] Preferably, the Pearson algorithm quantifies the degree of matching between two sets of data by calculating their covariance and standard deviation. For example, a corrected value of 0.78 indicates a strong positive correlation between power supply fluctuations and load changes; when power output increases, the load often increases simultaneously. This assessment of the strength of the correlation provides a reliable basis for subsequent analysis.

[0067] Step S175: Based on the results of the correlation strength assessment, obtain the characteristic calculation values ​​of the trend comparison and determine the final correlation coefficient.

[0068] When determining the correlation strength and obtaining the characteristic calculation values ​​for trend comparison, the final correlation coefficient can be further refined by combining the time dimension.

[0069] In one embodiment, focusing on data from a specific day, it was found that when power output reached 50kW at midday, the load was also close to 100kW, and the correlation coefficient eventually stabilized at 0.82. This reflects the dynamic matching characteristics between the two, which helps optimize the allocation of power grid resources.

[0070] Step S176: For the final correlation coefficient, use data comparison analysis to determine the degree of matching between the load change trend and fluctuation characteristics, and obtain the trend analysis results.

[0071] To verify the final correlation coefficient, data comparison analysis can be used to assess the degree of matching, and the results can be validated from multiple perspectives. For example, aligning peak load periods with peak power output periods reveals an overlap of over 85%; conversely, during off-peak periods, the decrease in power output correlates with the reduction in load. Trend analysis indicates that power fluctuations can effectively follow changes in load demand.

[0072] Step S177: Based on the trend analysis results, calculate the long-term correlation coefficient between the stable fluctuation characteristics and the load change trend, and output complete comparison data.

[0073] Based on trend analysis results, the calculation of long-term correlation coefficients can be extended to one month of data. For example, the average correlation coefficient between power supply and load remains stable at 0.80 over 30 days, with fluctuations ranging from 0.75 to 0.85. After outputting complete comparative data, the long-term consistency between the two can be clearly seen. This analysis helps improve the accuracy of power grid planning and provides data support for energy management.

[0074] Furthermore, the dynamic reactive power control method for substations provided in this embodiment includes step S200: Step S210: Obtain voltage and reactive power data from the initial dataset, and use the voltage-control quantity sensitivity model S(U,Q)= U / Q calculates the sensitivity parameters and determines the response relationship of each node voltage to reactive power.

[0075] After obtaining voltage and reactive power data from the initial dataset, the relationship between the two can be analyzed using a voltage-control quantity sensitivity model.

[0076] For example, in a certain regional power grid, the voltage of a node is 230V and the reactive power is 40kvar. Through sensitivity model calculation, it is found that when the reactive power increases by 1kvar, the voltage rises by 0.02V. This sensitivity parameter reflects the response strength of the node voltage to reactive power.

[0077] Step S220: Extract sensitivity parameters from the voltage-control quantity sensitivity model, generate node distribution sequences for power grid nodes, and obtain the sensitivity distribution characteristics between nodes.

[0078] Specifically, if the initial voltage of a node is too low, such as 218V, the voltage can be increased by adjusting the reactive power, and the sensitivity parameter becomes the basis for adjustment.

[0079] In one possible implementation, after extracting sensitivity parameters from the model, a node distribution sequence can be generated for 10 nodes in the power grid. For example, the sensitivity of node 1 is 0.02 V / kvar, and that of node 2 is 0.015 V / kvar. The node distribution sequence shows that the sensitivity gradually decreases from upstream to downstream. This distribution characteristic indicates that upstream nodes are more sensitive to changes in reactive power.

[0080] Step S230: Based on the node distribution sequence, determine the changing trend of the sensitivity parameters through partial derivative analysis, and judge the degree of mutual influence between the nodes.

[0081] It should be noted that partial derivative analysis can further reveal the trend of change. For example, if the sensitivity of a certain node decreases from 0.02V / kvar to 0.018V / kvar over time, it indicates that its response capability weakens as the load increases, and the mutual influence between nodes requires dynamic adjustment.

[0082] Step S240: Obtain trend data, process sensitivity parameters using data smoothing methods, and obtain a stable response relationship sequence.

[0083] After obtaining the trend data, the sensitivity parameters can be smoothed using the sliding window averaging method.

[0084] Preferably, for sensitivity data collected at a node within a day, such as 0.02, 0.025, and 0.019 V / kvar, a stable value of 0.021 V / kvar is obtained after smoothing over a 3-hour window. This stable response sequence can reduce short-term fluctuation interference and more accurately reflect long-term characteristics.

[0085] Step S250: For a stable response relationship sequence, determine the correlation coefficient between voltage data and reactive power through correlation analysis, and determine the distribution of control variables of the power grid nodes.

[0086] In one embodiment, correlation analysis revealed that the correlation coefficient between the voltage and reactive power of a certain node reached 0.9, indicating that the two are highly correlated and that adjusting the reactive power can effectively control the voltage.

[0087] Step S260: Extract key node parameters from the distribution of control variables, use cluster analysis to divide the node groups, and obtain the sensitivity characteristics between the node groups.

[0088] After extracting key node parameters from the distribution of control variables, cluster analysis can be used to divide the node groups.

[0089] For example, 10 nodes in a power grid are divided into a high-response group and a low-response group based on their sensitivity. The average sensitivity of the high-response group is 0.02V / kvar, and that of the low-response group is 0.01V / kvar.

[0090] Understandably, high-response groups are better suited to prioritize reactive power adjustment to optimize voltage. Sensitivity characteristics among node groups show that high-response groups are twice as efficient at improving voltage through reactive power adjustment as low-response groups.

[0091] Step S270: By analyzing the sensitivity characteristics between node groups, determine the optimization direction of reactive power adjustment on voltage data, and determine the final response relationship adjustment parameters.

[0092] When determining the direction of reactive power adjustment based on node group characteristics, for example, increasing the reactive power of a high-response group by 20 kvar raises the voltage from 220V to 224V, while the low-response group only increases by 1V. In one embodiment, the final response relationship adjustment parameter is determined to be an increase of 15 kvar of reactive power in the high-response group, while the low-response group remains unchanged. This strategy can quickly stabilize the voltage distribution and improve the grid operating efficiency.

[0093] Preferably, the dynamic reactive power control method for substations provided in this embodiment includes step S200: Step S310: Use dynamic programming algorithm to obtain initial reactive power output data from SVG equipment and capacitor bank, and determine basic target value through calculation process.

[0094] When acquiring the initial reactive power output data of SVG devices and capacitor banks, dynamic programming algorithms can gradually approach the target value through phased optimization. For example, if the initial reactive power output of SVG devices in a power grid is 50 kvar and that of capacitor banks is 30 kvar, dynamic programming can adjust the output step by step according to load demand, ultimately determining a basic target value of 70 kvar. This phased approach effectively balances computational efficiency and accuracy.

[0095] Step S320: Analyze the basic target value through voltage fluctuation amplitude and obtain the adjustment coefficient corresponding to the fluctuation amplitude.

[0096] In voltage fluctuation analysis, the fluctuation range corresponding to the baseline target value of 70 kvar may be ±5V. For example, if the voltage at a certain node fluctuates around 225V, the analysis yields an adjustment factor of 1.2, reflecting the amplification effect of the fluctuation on the output. This factor can intuitively guide the direction of subsequent adjustments.

[0097] Step S330: Match the adjustment coefficient with the power factor offset. If the offset exceeds the preset threshold, adjust the reactive power output through dynamic programming to obtain the optimized target value.

[0098] Regarding the matching of the adjustment coefficient and the power factor offset, assuming the preset threshold for the power factor offset is 0.05, and the actual value is 0.07, exceeding the threshold, it should be noted that in this case, reactive power output is adjusted through dynamic programming, such as increasing the output of the SVG device to 60 kvar, and updating the optimization target value to 75 kvar. This adjustment can quickly respond to situations where the offset exceeds the limit.

[0099] Step S340: Extract the voltage fluctuation trend from the optimized target value, use the support vector machine algorithm to predict the trend, and determine the predicted fluctuation amplitude.

[0100] When extracting voltage fluctuation trends from the target value, the support vector machine algorithm can predict future fluctuations based on historical data. For example, using the past 24 hours' output data of 75 kvar as input, it can predict a fluctuation range of ±3V for the next hour. This prediction can provide early warning of potential instability.

[0101] Step S350: Based on the relationship between the predicted fluctuation amplitude and the factor offset, obtain the output distribution ratio of the capacitor bank and obtain the reactive power output data after distribution.

[0102] The output distribution ratio of the capacitor bank is determined based on the relationship between the predicted fluctuation amplitude and the factor offset.

[0103] In one possible implementation, if the predicted fluctuation range is ±3V and the offset is 0.04, then the SVG device is allocated 60% of its output (45kvar), and the capacitor bank is allocated 40% (30kvar). This proportional allocation optimizes resource utilization efficiency.

[0104] Step S360: Update the operating parameters of the SVG device using the allocated reactive power output data, and determine the final target value if the parameters meet the fluctuation range requirements.

[0105] After updating the SVG equipment operating parameters using the allocated reactive power output data, it is determined whether the fluctuation range requirements are met.

[0106] Specifically, if the voltage fluctuation drops to ±2V after the update, meeting the preset standard, the final target value is determined to be 73kvar. This verification method ensures the practicality of the adjustment.

[0107] Step S370: Extract the power factor change trend from the final target value, and use a linear regression algorithm to analyze the relationship between the change trend and the offset to obtain stable operating parameters.

[0108] When extracting the trend of power factor change from the final target value, a linear regression algorithm can analyze its relationship with the offset. For example, based on the change of power factor from 0.92 to 0.95 at a power output of 73 kvar, a positive correlation is found between the two. This analysis provides data support for long-term stable operation.

[0109] In one embodiment, after adjustment using the above method, the SVG device output stabilizes at 45 kvar, the capacitor bank output at 28 kvar, voltage fluctuations are controlled within ±1.5V, and the power factor is maintained at 0.96. These stable operating parameters significantly improve the reliability of the power grid.

[0110] It should be noted that the combination of dynamic programming and support vector machines can form a complete closed loop from initial data acquisition to prediction and adjustment. For example, if the power grid load in a certain area suddenly increases, and the predicted fluctuation range increases to ±4V, the output ratio of the capacitor bank can be adjusted in a timely manner to avoid voltage instability. This flexibility is particularly crucial in actual operation.

[0111] Preferably, for high-load nodes, the allocation ratio can be dynamically adjusted to 70% for SVG devices and 30% for capacitor banks to cope with greater fluctuations. This expansion scheme enriches the adaptability of the strategy and ensures efficient operation in different scenarios.

[0112] This invention also provides a dynamic reactive power control system for substations, comprising an acquisition module, a calculation module, and a generation module. The acquisition module uses five sensors to acquire voltage, active power, reactive power, and power factor data from the power grid, generating time-series parameters of load change trends and distributed power source fluctuations. The calculation module is used to apply a voltage-control quantity sensitivity model S(U,Q) = ... U / Q calculates the response relationship between the voltage of each node and the reactive power, where S(U,Q) represents the partial derivative of voltage U with respect to reactive power Q; the generation module is used to generate the target values ​​of reactive power output of SVG and capacitor bank based on the response relationship using a dynamic programming algorithm, and the target values ​​correspond to the voltage fluctuation amplitude and power factor offset.

[0113] Furthermore, the reactive power dynamic control system for substations provided in this embodiment includes an acquisition module comprising a first acquisition unit, a second acquisition unit, a first determination unit, a first generation unit, a third acquisition unit, a fourth acquisition unit, and a first judgment unit. The first acquisition unit acquires voltage data, active power, reactive power, and power factor collected in real-time by power grid sensors and stores them as an initial dataset. The second acquisition unit calculates the time series of load changes using the initial dataset to obtain the load change trend. The first determination unit processes the load change trend using time series analysis methods to determine trend characteristic parameters. The first generation unit extracts active and reactive power related to distributed power sources from the initial dataset to generate a power fluctuation sequence. The third acquisition unit applies Fourier transform to the power fluctuation sequence to obtain the frequency domain distribution of the fluctuation parameters. The fourth acquisition unit smooths the fluctuation parameters if the frequency domain distribution exceeds a preset threshold to obtain stable fluctuation characteristics. The first judgment unit compares the stable fluctuation characteristics with the load change trend to determine the correlation coefficient between the two.

[0114] Preferably, the reactive power dynamic control system for substations provided in this embodiment includes a judgment unit comprising a first calculation subunit, a first determination subunit, a first acquisition subunit, a second determination subunit, a second acquisition subunit, and a second calculation subunit. The first calculation subunit is used to calculate the correlation coefficient between stable fluctuation characteristics and load change trends by acquiring data and employing time series analysis. The first determination subunit is used to obtain the comparison results of fluctuation characteristics and change trends from time series analysis and determine the initial value of the correlation coefficient. The first acquisition subunit is used to adjust the system by feature extraction if the initial value of the correlation coefficient exceeds a preset threshold. The system employs a first subunit to determine the correlation coefficient, which is calculated based on the data range of the stability fluctuation characteristics. The second subunit uses the Pearson correlation algorithm to determine the strength of the correlation between stable fluctuations and load changes based on the corrected correlation coefficient. The third subunit uses the correlation strength determination result to obtain the characteristic calculation value for trend comparison and determine the final correlation coefficient. The fourth subunit uses data comparison analysis to determine the degree of matching between the load change trend and the fluctuation characteristics, obtaining trend analysis results. Finally, the fifth subunit calculates the long-term correlation coefficient between the stable fluctuation characteristics and the load change trend based on the trend analysis results, outputting complete comparison data.

[0115] Furthermore, the dynamic reactive power control system for substations provided in this embodiment includes a calculation module comprising a second determining unit, a fifth acquiring unit, a second judging unit, a sixth acquiring unit, a third determining unit, a seventh acquiring unit, and a third judging unit. The second determining unit is used to acquire voltage data and reactive power data through an initial dataset, employing a voltage-control quantity sensitivity model S(U,Q)= U / The first unit calculates sensitivity parameters to determine the response relationship between voltage and reactive power at each node; the second unit extracts sensitivity parameters from the voltage-control quantity sensitivity model, generates a node distribution sequence for the power grid nodes, and obtains the sensitivity distribution characteristics between nodes; the third unit determines the trend of sensitivity parameter changes based on the node distribution sequence through partial derivative analysis, and judges the degree of mutual influence between nodes; the fourth unit acquires trend data, processes sensitivity parameters using data smoothing methods, and obtains a stable response relationship sequence; the fifth unit determines the correlation coefficient between voltage data and reactive power for the stable response relationship sequence through correlation analysis, and determines the distribution of control variables for the power grid nodes; the sixth unit extracts key node parameters from the control variable distribution, divides node groups using cluster analysis, and obtains the sensitivity characteristics between node groups; the seventh unit judges the optimization direction of reactive power adjustment on voltage data based on the sensitivity characteristics between node groups, and determines the final response relationship adjustment parameters.

[0116] Preferably, the reactive power dynamic control system for substations provided in this embodiment includes a generation module comprising a fourth unit, an eighth acquisition unit, a fourth judgment unit, a fourth determination unit, a ninth acquisition unit, a fifth judgment unit, and a tenth acquisition unit. The fourth unit is used to acquire initial reactive power output data from SVG equipment and capacitor banks using a dynamic programming algorithm, and determine the basic target value through calculation. The eighth acquisition unit is used to analyze the basic target value through voltage fluctuation amplitude and obtain the adjustment coefficient corresponding to the fluctuation amplitude. The fourth judgment unit is used to match the adjustment coefficient with the power factor offset, and if the offset exceeds a preset threshold, adjust the reactive power output through dynamic programming to obtain the desired result. The system comprises the following steps: a fourth determination unit, which extracts the voltage fluctuation trend from the optimized target value, uses a support vector machine algorithm to predict the trend, and determines the predicted fluctuation amplitude; a ninth acquisition unit, which obtains the output allocation ratio of the capacitor bank based on the relationship between the predicted fluctuation amplitude and the factor offset, and obtains the allocated reactive power output data; a fifth judgment unit, which updates the operating parameters of the SVG equipment using the allocated reactive power output data, and determines the final target value if the parameters meet the fluctuation amplitude requirements; and a tenth acquisition unit, which extracts the power factor change trend from the final target value, uses a linear regression algorithm to analyze the relationship between the change trend and the offset, and obtains stable operating parameters.

[0117] This embodiment provides a method and system for dynamic reactive power control in a substation. Compared with existing technologies, it first collects key parameters through grid sensors to generate time series data; then, it utilizes the voltage-control quantity sensitivity model S(U,Q)= U / Q analyzes the response relationship between voltage and reactive power at each node, and then uses a dynamic programming algorithm to calculate the optimal reactive power output target value of the SVG and capacitor bank. The dynamic reactive power control method and system for substations provided in this embodiment achieves precise adjustment of distribution network voltage and power factor by accurately grasping the dynamic response characteristics of voltage-reactive power, effectively suppressing voltage fluctuations and improving the operational stability and economy of the distribution network; it provides an intelligent and adaptive solution for reactive power optimization of distribution networks and has significant engineering application value.

[0118] Although preferred embodiments of the invention have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including both the preferred embodiments and all changes and modifications falling within the scope of the invention. Clearly, those skilled in the art can make various alterations and modifications to the invention without departing from its spirit and scope. Thus, if these modifications and modifications of the invention fall within the scope of the claims and their equivalents, the invention is also intended to include these modifications and modifications.

Claims

1. A method for dynamic control of reactive power in a substation, characterized in that, Includes the following steps: Acquire voltage, active power, reactive power, and power factor data from power grid sensors to generate time series parameters of load change trends and distributed power source fluctuations; Based on the time series parameters, the voltage-control quantity sensitivity model S(U,Q)= U / Q calculates the response relationship between the voltage at each node and the reactive power, where S(U,Q) represents the partial derivative of voltage U with respect to reactive power Q; Based on the aforementioned response relationship, a dynamic programming algorithm is used to generate target values ​​for the reactive power output of the SVG and the capacitor bank. These target values ​​correspond to the voltage fluctuation amplitude and the power factor offset.

2. The method for dynamic control of reactive power in a substation as described in claim 1, characterized in that, The steps of acquiring voltage, active power, reactive power, and power factor data from power grid sensors to generate time series parameters of load change trends and distributed power source fluctuations include: Acquire real-time voltage data, active power, reactive power, and power factor from power grid sensors and store them as an initial dataset; The load change time series is calculated using the initial dataset to obtain the load change trend; The load change trend was processed using time series analysis to determine the trend characteristic parameters; Active and reactive power related to distributed power sources are extracted from the initial dataset to generate a power fluctuation sequence. Fourier transform is applied to the power supply fluctuation sequence to obtain the frequency domain distribution of the fluctuation parameters; If the frequency domain distribution exceeds a preset threshold, the fluctuation parameters are smoothed to obtain stable fluctuation characteristics. The correlation coefficient between the stable fluctuation characteristics and the load change trend is determined by comparing the two.

3. The method for dynamic control of reactive power in a substation as described in claim 2, characterized in that, The step of determining the correlation coefficient between the stable fluctuation characteristics and the load change trend includes: By acquiring data on stable fluctuation characteristics and load change trends, time series analysis is used to calculate the correlation coefficient between the two. By comparing the fluctuation characteristics and changing trends obtained from time series analysis, the initial value of the correlation coefficient is determined; If the initial value of the correlation coefficient exceeds the preset threshold, the data range of the fluctuation feature is adjusted through feature extraction to obtain the corrected correlation coefficient. Based on the corrected correlation coefficient, the Pearson correlation algorithm is used to determine the strength of the correlation between stable fluctuations and load changes; Based on the results of the correlation strength assessment, the characteristic calculation values ​​of trend comparison are obtained to determine the final correlation coefficient; For the final correlation coefficient, data comparison analysis is used to determine the degree of matching between the load change trend and fluctuation characteristics, and trend analysis results are obtained. Based on the trend analysis results, the long-term correlation coefficient between stable fluctuation characteristics and load change trends is calculated, and complete comparative data is output.

4. The method for dynamic reactive power control of a substation as described in claim 1, characterized in that, Based on the time series parameters, the voltage-control quantity sensitivity model S(U,Q)= U / The steps for calculating the response of each node voltage to reactive power include: Voltage and reactive power data were obtained from an initial dataset, and a voltage-control quantity sensitivity model S(U,Q) was adopted. U / Q calculates the sensitivity parameters and determines the response relationship of each node voltage to reactive power; Sensitivity parameters are extracted from the voltage-control quantity sensitivity model, and node distribution sequences are generated for power grid nodes to obtain the sensitivity distribution characteristics between nodes; Based on the node distribution sequence, the changing trend of the sensitivity parameter is determined by partial derivative analysis to judge the degree of mutual influence between nodes. Obtain trend data, process sensitivity parameters using data smoothing methods, and obtain a stable response relationship sequence; For stable response relationship sequences, correlation analysis is used to determine the correlation coefficient between voltage data and reactive power, and to determine the distribution of control variables at grid nodes; Key node parameters are extracted from the distribution of the control variables, and cluster analysis is used to divide the node groups to obtain the sensitivity characteristics between the node groups. By analyzing the sensitivity characteristics among node groups, we can determine the optimization direction of reactive power adjustment on voltage data and ultimately determine the adjustment parameters for the response relationship.

5. The method for dynamic reactive power control of a substation as described in claim 1, characterized in that, The step of generating the target values ​​of reactive power output of the SVG and the capacitor bank using a dynamic programming algorithm based on the response relationship includes: The initial reactive power output data of the SVG device and capacitor bank are obtained by using a dynamic programming algorithm, and the basic target value is determined through the calculation process. By analyzing the voltage fluctuation amplitude, the basic target value is determined, and the adjustment coefficient corresponding to the fluctuation amplitude is obtained. The adjustment coefficient is matched with the power factor offset. If the offset exceeds the preset threshold, the reactive power is adjusted through dynamic programming to obtain the optimized target value. The voltage fluctuation trend is extracted from the optimization target value, and the support vector machine algorithm is used to predict the trend and determine the predicted fluctuation amplitude. Based on the relationship between the predicted fluctuation amplitude and the factor offset, the output allocation ratio of the capacitor bank is obtained, and the reactive power output data after allocation is obtained. The operating parameters of the SVG device are updated by the allocated reactive power output data. If the parameters meet the fluctuation range requirements, the final target value is determined. The power factor variation trend is extracted from the final target value, and the relationship between the variation trend and the offset is analyzed by linear regression algorithm to obtain stable operating parameters.

6. A dynamic reactive power control system for a substation, characterized in that, include: The acquisition module uses five sensors to acquire voltage, active power, reactive power, and power factor data from the power grid, and generates time series parameters of load change trends and distributed power source fluctuations. The calculation module is used to calculate the voltage-control quantity sensitivity model S(U,Q) based on the time series parameters. U / Q calculates the response relationship between the voltage at each node and the reactive power, where S(U,Q) represents the partial derivative of voltage U with respect to reactive power Q; The generation module is used to generate the reactive power output target values ​​of the SVG and the capacitor bank based on the response relationship using a dynamic programming algorithm. The target values ​​correspond to the voltage fluctuation amplitude and the power factor offset.

7. The dynamic reactive power control system for a substation as described in claim 6, characterized in that, The acquisition module includes: The first acquisition unit is used to acquire voltage data, active power, reactive power and power factor collected in real time by the power grid sensor and store them as an initial dataset. The second acquisition unit is used to calculate the time series of load changes using the initial dataset to obtain the load change trend. The first determining unit is used to process the load change trend using time series analysis methods to determine trend characteristic parameters; The first generation unit is used to extract active power and reactive power related to distributed power sources from the initial dataset and generate a power fluctuation sequence. The third acquisition unit is used to apply Fourier transform to the power supply fluctuation sequence to obtain the frequency domain distribution of the fluctuation parameters. The fourth acquisition unit is used to smooth the fluctuation parameters and obtain stable fluctuation characteristics if the frequency domain distribution exceeds a preset threshold. The first judgment unit is used to determine the correlation coefficient between the stable fluctuation characteristics and the load change trend.

8. The dynamic reactive power control system for a substation as described in claim 7, characterized in that, The determination unit includes: The first calculation subunit is used to calculate the correlation coefficient between stable fluctuation characteristics and load change trends by acquiring data on the two and using time series analysis. The first defined subunit is used to obtain the comparison results of fluctuation characteristics and changing trends from time series analysis, and to determine the initial value of the correlation coefficient; The first acquisition subunit is used to adjust the data range of fluctuation features through feature extraction if the initial value of the correlation coefficient exceeds the preset threshold, so as to obtain the corrected correlation coefficient. The judgment sub-unit is used to determine the correlation strength between stable fluctuations and load changes based on the corrected correlation coefficient and the Pearson correlation algorithm. The second determining subunit is used to obtain the characteristic calculation value of trend comparison based on the judgment result of the correlation strength, and to determine the final correlation coefficient; The second acquisition subunit is used to compare and analyze the degree of matching between the load change trend and fluctuation characteristics based on the final correlation coefficient, and obtain the trend analysis results. The second calculation subunit is used to calculate the long-term correlation coefficient between stable fluctuation characteristics and load change trends based on trend analysis results, and output complete comparison data.

9. The dynamic reactive power control system for a substation as described in claim 6, characterized in that, The computing module includes: The second determining unit is used to acquire voltage and reactive power data from the initial dataset, and adopts the voltage-control quantity sensitivity model S(U,Q)= U / Q calculates the sensitivity parameters and determines the response relationship of each node voltage to reactive power; The fifth acquisition unit is used to extract sensitivity parameters from the voltage-control quantity sensitivity model, generate a node distribution sequence for the power grid nodes, and obtain the sensitivity distribution characteristics between nodes; The second judgment unit is used to determine the changing trend of the sensitivity parameter based on the node distribution sequence through partial derivative analysis, and to judge the degree of mutual influence between the nodes. The sixth acquisition unit is used to acquire trend data and process sensitivity parameters using a data smoothing method to obtain a stable response relationship sequence. The third determining unit is used to determine the correlation coefficient between voltage data and reactive power through correlation analysis for a stable response relationship sequence, and to determine the distribution of control variables at the grid nodes. The seventh acquisition unit is used to extract key node parameters from the distribution of the control variables, divide the node groups using cluster analysis, and obtain the sensitivity characteristics between the node groups. The third judgment unit is used to determine the optimization direction of reactive power adjustment on voltage data by using the sensitivity characteristics between node groups, and to determine the final response relationship adjustment parameters.

10. The dynamic reactive power control system for a substation as described in claim 6, characterized in that, The generation module includes: The fourth unit is used to obtain initial reactive power output data from SVG devices and capacitor banks using dynamic programming algorithms, and to determine the basic target value through calculation. The eighth acquisition unit is used to analyze the basic target value through voltage fluctuation amplitude and obtain the adjustment coefficient corresponding to the fluctuation amplitude; The fourth judgment unit is used to match the adjustment coefficient with the power factor offset. If the offset exceeds the preset threshold, the reactive power is adjusted through dynamic planning to obtain the optimized target value. The fourth determining unit is used to extract the voltage fluctuation trend from the optimization target value, and uses the support vector machine algorithm to predict the trend and determine the predicted fluctuation amplitude. The ninth acquisition unit is used to obtain the output allocation ratio of the capacitor bank based on the relationship between the predicted fluctuation amplitude and the factor offset, and to obtain the reactive power output data after allocation. The fifth judgment unit is used to update the operating parameters of the SVG device through the allocated reactive power output data, and determine the final target value if the parameters meet the fluctuation range requirements. The tenth acquisition unit is used to extract the power factor change trend from the final target value, and uses a linear regression algorithm to analyze the relationship between the change trend and the offset to obtain stable operating parameters.

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