Grid-connected control method and system based on configuration optimization

Abstract

The invention provides a grid-connected control method and system based on configuration optimization, and relates to the technical field of grid-connected control, and the method comprises the steps: obtaining the operation data of a power grid, carrying out the time sequence analysis, and obtaining the initial state of the power grid; determining filtering parameters of the power grid operation data; adjusting the power grid state according to the deviation of the filtered power grid operation data, and generating a correction state prediction result; performing dynamic frequency adjustment and pressure adjustment, simulating a power grid operation feedback optimization adjustment strategy, and obtaining a power grid adjustment strategy; analyzing a power grid structure and flow efficiency, optimizing configuration of power grid nodes and connections, simulating and verifying a configuration adjustment effect of the power grid nodes and connections, and generating an optimized power grid design scheme; data transmission and electric energy efficiency of cross-regional connection are analyzed, a power grid connection path is configured, and a cross-regional connection optimization result is generated; and adjusting a grid-connected control strategy, and evaluating a grid-connected control result. The operation efficiency and the operation stability of the power grid can be improved.

Description

Technical Field

[0001] The present invention relates to the technical field of grid-connected control, and in particular to a grid-connected control method and system based on configuration optimization. Background Art

[0002] The field of grid-connected control technology involves the effective connection and management of power systems and various power sources, such as renewable energy generation facilities (wind power, solar power, etc.). Its core is to ensure the quality of power and the stability of supply, while maximizing energy efficiency and environmental sustainability. Grid-connected control technology includes not only the automation and optimization of traditional power grids, but also real-time data processing, load forecasting, system fault tolerance improvement, and the integration of various power production methods. By processing and analyzing batch data, it can achieve precise control of power grid behavior and optimize decision-making.

[0003] Existing grid-connected control technologies lack flexibility in the configuration of grid nodes and connection methods. Once the configuration is set, it is difficult to quickly adjust it according to the actual operating conditions, which limits the grid's ability to respond to emergencies. This not only affects the grid's operating efficiency, but also leads to energy waste. For example, without an efficient real-time adjustment strategy, the grid needs to rely on more frequency regulation services and backup energy, which not only increases operating costs but also reduces the overall energy efficiency of the system. In addition, existing grid-connected control technologies focus on traditional grid automation and optimization, with limited real-time data processing capabilities and system fault tolerance. The technical framework faces challenges in handling large-scale and highly variable renewable energy access, especially under frequently changing load and generation conditions. Existing technologies are difficult to accurately predict and adjust grid behavior in real time, which can lead to power quality problems such as voltage fluctuations and frequency instability. Summary of the invention

[0004] In order to solve at least one of the deficiencies of the prior art, an object of the present invention is to provide a grid connection control method and system based on configuration optimization to improve the operation efficiency and stability of the power grid.

[0005] In order to achieve the above object, according to some embodiments, a first aspect of the present invention provides a grid connection control method based on configuration optimization, comprising: Obtain grid operation data, perform time series analysis on the grid operation data, and obtain the initial state of the grid; According to the initial state of the power grid, determine the filtering parameters for the power grid operation data; according to the deviation of the filtered power grid data, adjust the power grid state and generate the correction state prediction result; By correcting the state prediction results, the power grid is dynamically adjusted in frequency and pressure, and the power grid operation feedback optimization adjustment strategy is simulated to obtain the power grid adjustment strategy; According to the grid adjustment strategy, analyze the grid structure and flow efficiency, optimize the configuration of grid nodes and connections, simulate and verify the configuration adjustment effect of grid nodes and connections, and generate an optimized grid design plan; According to the optimized grid design plan, analyze the data transmission and power efficiency of cross-regional connections, configure the grid connection path, and generate cross-regional connection optimization results; Based on the cross-regional connection optimization results, combined with grid operation data and real-time application feedback, the grid control strategy is adjusted and the grid control results are evaluated.

[0006] A second aspect of the present invention provides a grid-connected control system based on configuration optimization, comprising: The power grid initial state estimation module is configured to obtain power grid operation data, perform time series analysis on the power grid operation data, and obtain the initial state of the power grid; The correction state prediction module is configured to determine the filtering parameters of the power grid operation data according to the initial state of the power grid; adjust the power grid state according to the deviation of the filtered power grid data, and generate the correction state prediction result; The grid-connected control module is configured to dynamically adjust the frequency and pressure of the power grid by correcting the state prediction results, simulating the power grid operation feedback optimization adjustment strategy, and obtaining the power grid adjustment strategy; A power grid optimization module is configured to analyze the power grid structure and flow efficiency according to the power grid adjustment strategy, optimize the configuration of power grid nodes and connections, simulate and verify the configuration adjustment effect of power grid nodes and connections, and generate an optimized power grid design solution; The cross-regional connection analysis module is configured to analyze the data transmission and power efficiency of the cross-regional connection according to the optimized power grid design scheme, configure the power grid connection path, and generate the cross-regional connection optimization result; The evaluation module is configured to adjust the grid control strategy and evaluate the grid control results based on the cross-region connection optimization results combined with the grid operation data and real-time application feedback.

[0007] According to a third aspect of the present invention, an electronic device is provided, comprising a memory, a processor and a computer program stored in the memory, wherein the processor executes the computer program to complete the steps of the above-mentioned grid-connected control method based on configuration optimization.

[0008] According to a fourth aspect of the present invention, a computer-readable storage medium is provided for storing computer instructions, and when the computer instructions are executed by a processor, the steps of the above-mentioned grid-connected control method based on configuration optimization are completed.

[0009] According to a fifth aspect of the present invention, a computer program product is provided, comprising a computer program / instruction, which, when executed by a processor, implements the steps of the above-mentioned grid-connected control method based on configuration optimization.

[0010] Compared with the prior art, the present invention has the following beneficial effects: The present invention provides a grid-connected control method and system based on configuration optimization, which introduces optimization adjustment of grid nodes and connection configurations in grid-connected control. By optimizing grid nodes and connection configurations, not only the data flow and energy distribution efficiency of the grid are enhanced, but also the overall grid operation efficiency and environmental sustainability are improved. Optimization measures reduce energy waste, improve the utilization rate of renewable energy, and ensure the continuity and safety of power supply, so that the grid can better cope with demand peaks and valleys, optimize power resource allocation, and ensure the stable operation of the grid.

[0011] Advantages of additional aspects of the present invention will be given in part in the following description, and in part will become obvious from the following description, or will be learned through practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] The accompanying drawings in the specification, which constitute a part of the present invention, are used to provide a further understanding of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute improper limitations on the present invention.

[0013] Figure 1 Flow chart of the method in Embodiment 1 of the present invention. DETAILED DESCRIPTION

[0014] The present invention will be further described below in conjunction with the accompanying drawings and embodiments.

[0015] Embodiment 1 Embodiment 1 of the present invention provides a grid-connected control method based on configuration optimization, such as Figure 1 As shown, including: S1: Obtain grid operation data, perform time series analysis on the grid operation data, and obtain the initial state of the grid; S2: Determine the filtering parameters for the grid operation data according to the initial state of the grid; adjust the grid state according to the deviation of the grid operation data after filtering, and generate the correction state prediction result; S3: By correcting the state prediction results, the power grid is dynamically adjusted in frequency and pressure, and the power grid operation feedback optimization adjustment strategy is simulated to obtain the power grid adjustment strategy; S4: Analyze the grid structure and flow efficiency according to the grid adjustment strategy, optimize the configuration of grid nodes and connections, simulate and verify the configuration adjustment effect of grid nodes and connections, and generate an optimized grid design plan; S5: According to the optimized grid design plan, analyze the data transmission and power efficiency of cross-regional connections, configure the grid connection path, and generate cross-regional connection optimization results; S6: Based on the cross-regional connection optimization results, combined with grid operation data and real-time application feedback, adjust the grid control strategy and evaluate the grid control results.

[0016] In step S1, an initialization model for estimating the state of the power grid is constructed, and voltage, current and frequency data are input. The data is tracked and sorted in real time through time series analysis, the behavior of the power grid in the short term is predicted, the real-time state of the power grid is evaluated, and the initial state of the power grid is generated. The generated initial state of the power grid includes the real-time voltage level, current intensity and frequency change trend of the power grid. Step S1 specifically includes: S101: construct an initialization model for power grid state estimation, input power grid operation data, namely voltage, current and frequency data, perform data entry, calibrate the input data format, screen and process abnormal values, and generate power grid parameter records.

[0017] The initialization model of the grid state estimation refers to the mathematical model initially set in the grid state estimation. This model is used to estimate the initial state of the grid and is constructed based on the network topology (such as generators, transformers, transmission lines, etc.) and basic operating parameters (such as rated voltage, frequency, etc.). The real-time grid operation data (voltage, current and frequency data) are entered and sorted in a unified data format, and preliminary quality control is performed, including identifying and processing outliers and missing values ​​in the data. The data input into the initialization model is adjusted and optimized to ensure that the actual operating state of the grid is accurately reflected. Data adjustment is performed through quality control to generate grid parameter records. Grid parameter records refer to a series of records of key grid operation parameters in the process of grid state estimation, including voltage level, current, power flow, frequency and important indicators. Important indicators refer to key parameters that can reflect the health, performance and safety of the grid, including voltage stability indicators, line load rate, power factor, frequency stability, failure rate and recovery time. Grid parameter records are used to monitor the health of the grid, perform fault analysis, and optimize grid design and operation. The grid parameter record uses the processing index of the grid parameter record to evaluate the accuracy, and the formula is: ; in, Represents the processing index of power grid parameter records, which is used to evaluate and optimize various indicators of power grid parameter record accuracy, such as error rate and data integrity index. The purpose of calculating the processing index is to quantify the effect of the data processing process, ensure data quality and accuracy, so as to make accurate estimates and decisions on the power grid status. , and Represent the voltage, current and frequency values ​​of the i-th data point respectively, and D is the total number of data points.

[0018] S102: Based on the grid parameter records, the grid status data of the differentiated time periods are updated in time series, and trend analysis and anomaly detection are performed on continuous data points to generate time series tracking data.

[0019] Based on the grid parameter records, this process involves updating and recording the grid parameter records in a time series. The updating and recording of the grid parameter records in a time series means that the data is continuously collected and stored, and recorded at preset time intervals (such as every minute, every hour), allowing the system to analyze the changing trends and periodic behaviors of the data over time, ensuring that the behavior and changes of the grid in differentiated time periods can be captured. Trend analysis and anomaly detection techniques are applied to track the changing trends of continuous data points, discover and record abnormal fluctuations in data, and generate time series tracking data. Trend analysis techniques include moving average, exponential smoothing, trend line analysis, etc., which help identify the long-term trend of data over time; anomaly detection techniques include statistical thresholds and machine learning models (such as isolation forests and autoencoders), which can identify prominent anomalies in the data, that is, data points that do not conform to normal behavior patterns, analyze the real-time and historical data of the power grid, determine the normal behavior patterns of the data and any abnormal behaviors that deviate from the patterns, improve the practicality of the data, and provide accurate basic information for further analysis and decision-making. Time series tracking data refers to real-time or near real-time tracking of power grid parameter records based on time series analysis to monitor the health and performance of the power grid, including tracking the changing trends of power grid parameters and detecting anomalies, so as to respond to potential problems in a timely manner. For the acquired time series tracking data, the quality index is used to evaluate the volatility or consistency of the data. The formula for the quality index of time series tracking data is: ; in, Represents the quality index of time series tracking data. The quality index is the accumulation of standardized deviations of each data point from the mean. It is used to measure the volatility or consistency of the data. It is directly related to time series tracking and anomaly detection, helping to identify mutations in the data or patterns that deviate from the normal state. is the data value at the jth time point, and are the mean and standard deviation of the dataset, respectively, and Q is the total number of time points.

[0020] S103: Based on the time series tracking data, the Fourier transform method is used to analyze the periodic changes and long-term trends of voltage, current and frequency, predict the grid behavior in the short term, evaluate the real-time state of the grid, and generate the initial state of the grid.

[0021] Based on the time series tracking data, the Fourier transform method is used to conduct in-depth analysis of the voltage, current and frequency data, which can reveal the periodic changes and long-term trends of the data, effectively extract meaningful periodic patterns from the power grid data, and predict the power grid behavior in the short term. Comprehensively evaluate the real-time state of the power grid to predict power grid problems and performance fluctuations, provide a scientific basis for power grid management and optimization decisions, and generate the initial state of the power grid.

[0022] The formula of Fourier transform method is as follows: ; in, For signal The frequency domain representation of -T and T represents the time interval. is a time series signal, is the transformation kernel, is an imaginary unit, is the frequency variable, is the time variable, is the weighting coefficient, Target time point time, is the weight, The number of target time points selected.

[0023] The acquired time series tracking data of voltage, current and other signals in the power grid usually contains periodic changes, which can be analyzed by Fourier transform. Using Fourier transform to convert signals in the time domain to the frequency domain can reveal the main frequency components in the signal and help analyze the periodic and fluctuating characteristics of the signal. In the frequency domain, identify and analyze the main harmonic components and amplitudes. For example, harmonic distortion indicates problems in the power grid, such as equipment failure and overload. Based on the results of frequency domain analysis, the overall health status and performance stability of the power grid can be evaluated, and the grid problems and performance fluctuations can be predicted. Execute a finite time interval The Fourier transform in the y-sampling process is used to process the actual measurable signal, avoiding the practical infeasibility of infinite integration, and adding a weighted summation term to specifically enhance the specific moments in the signal. The influence of Adjust the contribution of time points, weights Dynamically adjusted by the characteristics of the data set and optimized by verifying the prediction accuracy and The value of is used to ensure the best results in real-time grid behavior prediction, determine the appropriate time window to perform Fourier transform on the signal, select a complete grid cycle, perform Fourier transform on the selected signal segment, obtain the frequency distribution and corresponding amplitude within the period, and estimate the initial state of the grid based on the obtained spectrum. The initial state of the grid refers to the operating conditions of the grid at a specific time point or the beginning of a time period, including normal state, fault state and filtering state. The normal state is that the grid operates normally without obvious faults or abnormalities. The fault state refers to the state when there is a problem with the grid. The transition state refers to when the grid switches from one operating state to another, such as from high load to low load, or when the equipment starts and stops, the grid parameters will undergo a short change. For example, high harmonic content indicates that there is a problem with some equipment. On the basis of continuous monitoring, the initial state is updated and adjusted in real time to adapt to the changes in the grid state. The Fourier transform can not only help understand the current state of the grid, but also provide early warning of potential problems in the future, promoting the stable operation and reliability of the grid.

[0024] In step S2, dynamic filtering technology is used based on the initial state of the power grid. The object of dynamic filtering is real-time power grid operation data, including information such as voltage, current and frequency. The purpose of filtering is to extract useful information from the power grid operation data, while filtering out noise and irrelevant data, so as to more accurately predict and adjust the power grid state, adjust the filtering parameters, process the power grid operation data in real time, analyze the deviations and trends of voltage, current and frequency, and generate the correction state prediction results through state adjustment. The correction state prediction results include the expected adjustment range of the power grid voltage, the expected rate of change of the current and the expected stability analysis of the frequency. Step S2 specifically includes: S201: Perform initial setting of filter parameters according to the initial state of the power grid, match the real-time state data of the power grid by calibrating the parameters, adjust the sensitivity and response threshold of the parameters, and generate parameter adjustment records.

[0025] According to the initial state of the power grid, determine the normal operating range and performance indicators of the power grid under different operating conditions, analyze these data to identify common fluctuation patterns, peaks and abnormal conditions, select appropriate filters (such as low-pass, high-pass, band-pass or band-stop filters) to process specific types of data or remove specific types of interference, set the initial parameters of the filter, such as cutoff frequency, gain, etc., according to the basic operating data of the power grid, and calibrate the parameters of the filter through simulation data or small-scale field tests. This process requires adjusting the sensitivity and response threshold of the filter to match the actual performance of the power grid, setting and calibrating the filter parameters. The object of the filter is the power grid operation data, including information such as voltage, current and frequency. The purpose of the filter is to extract useful information from the real-time data, while filtering out noise and irrelevant data, so as to more accurately predict and adjust the power grid state and ensure that the parameters can closely match the real-time state data of the power grid. By adjusting the sensitivity and response threshold of the filter, sensitivity adjustment means changing the degree of reaction of the filter to changes in input data. Increasing sensitivity can make the filter react to state changes faster, but it will cause excessive response to noise or error data; the response threshold sets the minimum limit of the amount of data change that the filter starts to react to. Adjusting the threshold can help avoid overreacting to small-scale or insignificant changes, and focus on processing more significant state changes to optimize the reaction speed and accuracy in the data processing process. Parameter adjustment is based on the operating conditions and performance data of the power grid, taking into account the load changes of the power grid, the influence of environmental factors, etc., and testing and verifying the effect of the adjustment through simulation or actual operation data to ensure that the adjusted parameters can capture key power grid dynamics and generate parameter adjustment records. The quality index of the parameter adjustment record is used to evaluate the effect of parameter adjustment. The specific formula is: ; in, The quality index representing the parameter adjustment record is used to measure the accuracy of the grid state estimation and the quality of the filtering effect. The calculation of the quality index is based on comparing the difference between the filter output and the actual measurement value, or evaluating the efficiency of the filter in suppressing noise and responding to state changes. By analyzing the quality index, the effect of the current parameter setting can be evaluated to guide whether parameter adjustment is needed. The detailed parameter adjustment data recorded provides a specific basis for implementing the adjustment, helps understand the effect changes brought about by the adjustment, and provides data support for subsequent operations and strategy adjustments. , and represent the ith adjusted value of voltage, current and frequency, T, T and is the corresponding response threshold, and D is the total number of data points.

[0026] S202: Perform data deviation tracking through parameter adjustment records, analyze voltage, current and frequency deviations for each grid measurement point, record the frequency of the deviations, and generate deviation analysis data.

[0027] Through parameter adjustment records, analyze the deviations of voltage, current and frequency for each grid measurement point, determine the normal operating range or threshold of each parameter (voltage, current and frequency), and define the deviation as the difference between the measured value and the set threshold. Perform real-time or periodic analysis on the collected data to calculate the difference between the actual measured value and the threshold at each measurement point. The difference is expressed as a deviation value, which can be expressed in absolute values ​​or percentages, and the frequency of the deviation is recorded. Recording the frequency of deviations at each measurement point, that is, the number of deviations and the time interval, helps to identify the pattern of deviations and the location of frequent problems. By statistically analyzing the deviation data, areas or equipment in the grid where problems often occur can be identified. Use statistical charts, heat maps or cluster analysis methods to visualize and identify problem areas and focus on monitoring. Analyzing the deviation data is conducive to understanding the unstable factors and fault sources in the grid and providing a basis for further technical intervention. After the analysis is completed, the deviation analysis data is generated, and the formula used is: ; in, The comprehensive index represents the deviation analysis. The comprehensive index is a summary indicator that takes into account various deviation factors and their impact on grid stability and security. The comprehensive index includes weighted scores, risk assessments, etc. to provide a quick measure of the overall grid status. , and are the deviation values ​​of voltage, current and frequency respectively, , and and , and are the mean and standard deviation of voltage, current and frequency respectively, and L is the total number of measurement points.

[0028] S203: Using the deviation analysis data, dynamically adjust the grid state, and predict the short-term grid behavior, including voltage changes and peak response, by modifying the filter parameters to generate a correction state prediction result.

[0029] Deviation analysis data is used to dynamically adjust the state of the power grid. According to the results of the deviation analysis, the adjustment measures that need to be taken are determined, such as increasing or decreasing the power generation, adjusting the load of the transmission line, etc., to maintain the stability of the power grid. Dynamic adjustment also includes optimizing scheduling decisions, such as starting and stopping some power stations or switching the operation mode of the transmission line to adapt to the changes in the power grid load. Modify the filter parameters and select appropriate filtering technologies according to the specific needs of the power grid, such as Kalman filtering, low-pass filtering, moving average filtering, etc., in order to more accurately predict and adjust the state of the power grid. The parameter adjustment is based on the results of the deviation analysis. For example, if the voltage fluctuation is too large, the window size or gain of the filter needs to be adjusted to reduce noise and improve the accuracy of the prediction. Adjusting parameters also includes changing the damping coefficient in the filtering algorithm to improve the response speed and sensitivity to the dynamic changes of the power grid, and predicting and adjusting the voltage changes and peak response of the power grid in the short term. Through dynamic adjustment, the state changes in the power grid can be responded to in a timely manner to reduce potential risks and failures. The adjustment process also includes real-time monitoring and feedback of the power grid to ensure that all adjustments can effectively improve the overall performance and reliability of the power grid and generate correction state prediction results. The formula used is: ; in, represents the prediction result of the correction state, It is The adjustment coefficient of the filter parameters, is the frequency parameter to be adjusted, is the phase adjustment, It's time. is the number of parameters.

[0030] In step S3, the grid adjustment strategy includes frequency adjustment parameters and pressure adjustment schemes adjusted according to the real-time power demand of the grid and the renewable energy supply. Step S3 specifically includes: S301: Using the correction state prediction results, frequency adjustment parameters are set, responding to the real-time demand and prediction deviation of the power grid, designing frequency management measures, and generating frequency adjustment parameter configuration.

[0031] The correction state prediction results are used to set frequency adjustment parameters to respond to the real-time demand and prediction deviation of the power grid. The actual performance of the current power grid frequency and the correction state prediction results are analyzed to determine the necessary adjustment range and target frequency. The current frequency data, including load demand, power generation, frequency fluctuations, etc., are collected from the real-time monitoring system of the power grid. Prediction technologies such as machine learning models are used to predict the power grid status in the future, including load and available power generation, based on historical data. Correcting the state prediction results means updating the prediction of the power grid state under known actual conditions (such as a sudden increase in load or generator failure), comparing real-time data with the prediction results, evaluating the accuracy and deviation of the prediction, and analyzing whether the current frequency is consistent with the predicted value, so as to understand the actual performance of the power grid. If the actual frequency deviates from the normal value (50Hz or 60Hz), the necessary adjustment range needs to be determined based on the degree and trend of the deviation. The target frequency will be set to the ideal state, such as 50Hz or 60Hz. According to the deviation between the prediction and actual data, the degree of adjustment required to return the frequency to the target value is evaluated, which involves calculating the additional power generation required or the load reduction required, and designing specific frequency management measures, including adjusting the output frequency of the generator and / or modifying the response strategy of the frequency regulation load. Collect key indicators such as load demand, power generation, and frequency fluctuations from the real-time monitoring system of the power grid. Use machine learning models or statistical analysis methods to compare real-time data with the prediction results to calculate the deviation between the actual frequency and the target frequency (such as 50Hz or 60Hz). Establish a mathematical model that uses current deviations and historical data as inputs to predict the amount of adjustment required to achieve the target frequency in the short term. Simulate frequency response under different load and generation conditions to determine the optimal adjustment strategy. Plan and guide power plants to increase or decrease power output according to the required increase in power generation. For example, if the frequency needs to be increased, guide fast-responding power generation units (such as gas turbines) to increase output. Through demand response management, dynamically adjust the power use of industrial and commercial users to reduce power consumption during non-critical time periods to reduce the total load. Modify the working mode of load equipment so that it can automatically reduce power consumption when the grid frequency is low, or increase power consumption when the frequency is high, for example, automatically adjust the operating time of large appliances through smart home systems. All adjustments are aimed at ensuring the stability and efficiency of the power grid and generating frequency adjustment parameter configurations. Frequency adjustment parameter configurations refer to setting parameters in related control systems. The parameters guide how to automatically or manually adjust power generation, adjust loads, or start backup power to match grid demand and maintain frequency stability. The effect of frequency adjustment parameter configuration is evaluated using effect evaluation indicators, and the formula is: ; in, It indicates the effect evaluation index of frequency adjustment. The frequency adjustment parameter configuration directly affects the response capability and stability of the power grid to load changes and determines the effect evaluation index of frequency adjustment. Reasonable parameter configuration can improve the accuracy and efficiency of frequency control, ensure the stability and reliability of power grid operation, and also affect the economic operation of the entire system. is the actual frequency measurement, is the desired frequency, is the number of measurement points.

[0032] S302: Based on the frequency adjustment parameter configuration, the pressure control parameters are adjusted to match the grid load changes, and a pressure adjustment strategy is generated by real-time monitoring of the grid load and pressure feedback data.

[0033] Based on the frequency adjustment parameter configuration, the pressure control parameters are adjusted to match the load changes of the power grid. The power grid load data is monitored in real time and compared with the pressure feedback data. The pressure control parameters are adjusted in real time to adapt to the load changes and maintain the stable operation of the power grid. In the power grid system, pressure refers to the voltage level or current intensity in the power grid, and adjusting the pressure control parameters involves adjusting the settings of transformers, voltage regulators and electrical equipment in the power grid. Based on the frequency adjustment parameter configuration, the output of the generator is changed or the input voltage of the adjustable load is adjusted to match the real-time changes of the power grid load. The real-time monitored power grid load data is compared with the pressure feedback data, and the deviation or consistency between the data is analyzed to determine whether the pressure control parameters need to be further adjusted. According to the comparison results, the pressure control parameters are adjusted automatically or manually to ensure the stability and efficiency of the power grid. If the load is detected to increase and the voltage drops, it is necessary to increase the power generation or adjust the settings of the voltage control device, and vice versa. The adjustment is automatically completed by the control system to respond quickly to load changes. Accurate adjustment of pressure parameters helps to optimize the operating efficiency and response speed of the entire power grid, and obtain the pressure adjustment strategy. The pressure adjustment strategy refers to a set of predetermined operations and procedures, which aims to automatically adjust the pressure control parameters in the power grid according to the changes in the power grid load, predict and respond to changes in the power grid state, and ensure the efficient and stable operation of the power grid. The formula used is: ; in, It represents the optimization index of pressure adjustment. The optimization index of pressure adjustment includes grid efficiency, stability, response speed, equipment life, etc., which measures the effect of implementing the pressure adjustment strategy. is the current pressure value, is the optimized pressure target, is the number of data points.

[0034] S303: adopt a pressure adjustment strategy, integrate the adjustment results of frequency and pressure, simulate the operation of the power grid to verify the efficiency of the adjustment, analyze the changes in the operation of the power grid through real-time feedback, and generate a power grid adjustment strategy.

[0035] Adopt a pressure adjustment strategy, integrate the adjustment results of frequency and pressure, and simulate the operation of the power grid to verify the efficiency of the adjustment. Use real-time feedback data to analyze changes in power grid operation, collect real-time data on power grid operation, including load, frequency, voltage (pressure), power flow and other parameters of the power grid, continuously monitor the operation status of the power grid through sensors and measuring equipment installed at key nodes of the power grid, ensure real-time update of data, use statistical analysis and machine learning models to identify abnormal patterns in the data, such as sudden changes in frequency and voltage, which indicate potential problems or failures, detect whether the adjustment measures have achieved the expected results, analyze the contribution of frequency and pressure adjustment to power grid stability and operational risks, analyze the relationship between frequency and voltage changes and power grid operation events (such as equipment failures, sudden demand increases, etc.) to determine which factors are the main causes of changes in power grid status, evaluate the impact of various changes on power grid stability based on simulation results, including quantitative analysis of power grid operation risks, adjust strategies based on simulation results and actual operation feedback, continuously optimize adjustment parameters, such as adjusting power generation, reconfiguring network resources, and generating power grid adjustment strategies. The power grid adjustment strategy is expressed by the following formula: ; in, represents the grid adjustment strategy, is the actual data point after grid adjustment, is the standard operating data point, is the number of data points.

[0036] In step S4, a grid adjustment strategy is adopted to analyze the grid structure and flow efficiency, optimize the configuration of grid nodes and connections, optimize network efficiency and stability, verify the adjustment effect through adjusted grid simulation, and generate an optimized grid design scheme. The optimized grid design scheme includes the reconfiguration of grid nodes, the optimized layout of connection lines, and the expected flow efficiency optimization measures. Step S4 specifically includes: S401: Through the grid adjustment strategy, analyze the connection efficiency and flow characteristics of the grid structure, identify the existing bottlenecks and inefficient areas, including mobility assessment and structure scanning, and generate grid structure efficiency analysis results.

[0037] Through the grid adjustment strategy, the connection efficiency and flow characteristics of the grid structure are analyzed, and detailed information about each node (such as power stations, substations, electricity users, etc.) and connection (transmission lines) in the grid is collected.

[0038] The collected data includes parameters such as voltage level, line length, resistance, load flow, etc. The data is processed to ensure data quality and integrity to provide accurate input for analysis. Connection efficiency refers to the loss of electricity during transmission. Efficiency is evaluated by calculating the voltage drop and line loss on the line. Use power system analysis tools (such as Simulink, PSS / E in MATLAB, etc.) to simulate the operation of the power grid and observe the performance of voltage and power under different load conditions. Considering the physical properties of the transmission line, such as conductor material, cross-sectional area, environmental factors, etc., use this data to simulate and calculate the transmission efficiency. Flow characteristics focus on the flow mode and efficiency of electricity from the power generation point to the load center. Use load flow analysis to determine the distribution of current in the system, especially the response of the power grid under high load or fault conditions. Identify bottleneck areas that cause poor energy flow. These areas are due to insufficient transmission capacity or unreasonable wiring between nodes. The goal is to identify bottlenecks and inefficient areas in the power grid, areas that cause delays or losses in energy transmission, and conduct liquidity assessments. By analyzing current and voltage data, monitoring the load response of key nodes and connections, and performing structural scans, we can gain a deep understanding of the operation of the power grid by checking the integrity and efficiency of physical and logical connections, and generate power grid structure efficiency analysis results using the formula: ; in, represents the efficiency of the power grid structure, represents the load of the ith connection, represents the capacity of the ith connection, is the total number of connection points.

[0039] S402: Based on the results of the grid structure efficiency analysis, reconfigure the grid nodes and connection methods, adjust the connection paths and optimize the node layout, optimize the network efficiency and stability, and generate the optimal configuration of nodes and connections.

[0040] Based on the results of the grid structure efficiency analysis, reconfigure the grid nodes and connections, including adjusting the connection paths to avoid the identified bottleneck areas and optimizing the node layout to improve the overall network efficiency and stability. Use network planning tools and algorithms to redesign the grid structure to ensure that each node and connection can achieve optimal performance in load distribution and energy transmission. Collect and analyze data from the existing grid, including node loads, connected current and voltage levels, fault records, etc. Use this data to build a mathematical model of the grid for simulation and analysis. Use network analysis tools, such as load flow analysis software, to identify bottleneck areas in the grid, that is, nodes and connections that experience performance degradation or failure under high load conditions. Use optimization algorithms (such as genetic algorithms, particle swarm optimization, etc.) to find the optimal node layout and connection paths. Algorithms can help find the best configuration to reduce losses and improve transmission efficiency under given network load and resource conditions. Adjust node locations and connection lines based on the algorithm results, including adding new connection lines, upgrading existing lines, and reconfiguring node connections to distribute loads and reduce the risk of single point failures. Before the actual adjustment, the simulation tool is used to test the performance of the new grid configuration under various load and fault conditions to ensure that the newly designed network structure has higher efficiency and stability than the original structure. After the network reconfiguration is implemented, the network performance and stability are continuously monitored to ensure that the expected results are achieved in actual operation, and fine-tuning is performed as needed. Through adjustment, the node and connection optimization configuration is generated. The node and connection optimization configuration refers to the adjustment of each node and connection method in the power grid based on detailed network performance analysis and optimization algorithms to improve the overall efficiency and stability of the power grid, including selecting appropriate locations and capacity configuration nodes to make energy distribution more efficient and reduce energy losses, adjusting connection paths and methods, using more efficient line materials and technologies, reducing losses during transmission, and ensuring the robustness of the power grid in the event of a fault. The network efficiency after the node and connection optimization configuration is calculated using the following formula: ; in, represents the network efficiency after the nodes and connections are optimally configured, It is The processing power of each node, It is The number of connections to a node, is the total number of nodes.

[0041] S403: According to the node and connection optimization configuration, a simulation operation of the adjusted power grid design is performed, the adjustment effect is tested by simulating various load and fault scenarios, the real-time effectiveness of the optimization measures is checked, and an optimized power grid design solution is generated.

[0042] According to the node and connection optimization configuration, the simulation operation of the adjusted power grid design is performed, and the adjustment effect is tested by simulating various load and fault scenarios to ensure that the power grid can maintain high efficiency and stability under different conditions. The simulation test includes applying different load modes and deliberately introducing fault points to evaluate the response and recovery capabilities of the power grid and obtain the optimized power grid design solution. The formula is expressed as follows: ; in, represents the optimized grid design scheme, It is Stability scores under each simulation scenario, is the ideal stability target, is the number of simulation scenarios.

[0043] In step S5, according to the optimized grid design scheme, the data transmission and power efficiency of the cross-regional connection are analyzed, the grid connection path is configured, the layout and stability of the cross-terrain power transmission are optimized, and the cross-regional connection optimization results are generated. The cross-regional connection optimization results include the optimization of data transmission efficiency, the reduction of power transmission losses, and the stability adjustment of key connection points. Step S5 specifically includes: S501: Based on the optimized power grid design plan, analyze the data transmission efficiency of the cross-regional connection of the power grid, evaluate the power loss and transmission delay of multiple connection paths, record the areas with low parameter analysis efficiency, and generate data transmission efficiency analysis results.

[0044] Based on the optimized grid design, the data transmission efficiency of cross-regional grid connections is deeply analyzed. The connection path data between different regions, including power loss and transmission delay, are collected through real-time data monitoring equipment. Data analysis tools and techniques, such as flow analysis and energy consumption models, are used to quantitatively evaluate the efficiency of each connection path. The evaluation focuses on identifying areas with excessive power loss or severe data transmission delays to facilitate subsequent optimization and generate data transmission efficiency analysis results. The formula used is: ; in, Indicates the data transmission efficiency, It is The power loss of the connection path, is the corresponding data transmission delay, is the total number of connection paths.

[0045] S502: Using the data transmission efficiency analysis results, adopting a genetic algorithm, reconfiguring the connection path of the power grid, optimizing data flow and energy distribution by adjusting the number of connection nodes and the layout of connection lines, and generating a connection path configuration plan.

[0046] Using the results of data transmission efficiency analysis, a genetic algorithm is applied to reconfigure the connection paths of the power grid. The process involves adjusting the number of connection nodes and replanning the layout of the connection lines to optimize the data flow and energy distribution of the entire power grid. The genetic algorithm is defined as the genetic code of the power grid nodes and connection paths. Through iterative evolution, the optimal path configuration scheme is selected. After multiple generations of selection, crossover and mutation, the connection path configuration scheme that can maximize data transmission efficiency and minimize energy consumption is determined, which not only improves the operating efficiency of the network, but also optimizes energy distribution and generates a connection path configuration scheme.

[0047] The formula of genetic algorithm is as follows: ; in, is the total cost, For connection Length, is the number of connections, For connection The energy consumption per unit length is Yes Connect The reliability score of is the frequency utilization rate, is the weight factor of reliability and frequency utilization, is the safety factor weight, Yes Connect The safety level, is the priority coefficient.

[0048] Calculate the basic cost of each connection, i.e. the length Multiply by the energy consumption per unit length , adding the reliability score of the connection and frequency utilization , reliability scoring involves evaluating the robustness of the network under various conditions (such as load, failures, etc.). The genetic algorithm evaluates the performance of different network configurations by simulating node or path failures, measuring the network's ability to maintain functionality (i.e., effectively transmit data / energy). Configurations that maintain high performance in most scenarios will receive higher reliability scores. Frequency utilization refers to the frequency with which a specific path or node is used in the process of transmitting data or energy. During the optimization process, the algorithm tracks the frequency of use of each path or node in the simulation. Paths or nodes that are critical for effective transmission will have higher utilization. The algorithm aims to balance the network load or enhance the efficiency of frequently used paths to minimize energy consumption and maximize data flow, and uses weight factors Adjustment impact, weighting factor Determined through historical data and optimization simulation to reflect the actual impact of reliability and frequency utilization on total cost, safety level and priority coefficient By weight Impact cost, weight Also obtained through data analysis, it ensures that important and high-security connections are given priority in optimization, and the entire calculation process ensures the meticulousness and pertinence of cost calculation.

[0049] S503: Implement the connection path configuration plan, perform network simulation, test the power grid performance under the new configuration, including load response test and fault recovery test, verify the effect of the new design, and generate cross-regional connection optimization results.

[0050] Implement the connection path configuration plan and perform network simulation tests of the grid performance, including evaluation of the load response and fault recovery capabilities under the new configuration. Through simulation software, various network load and fault conditions are simulated to test how the newly configured grid responds to various challenges encountered in actual operation to verify the practicality and effectiveness of the new design and ensure that the new grid design can remain efficient and stable under different operating conditions. Determine the optimal layout of power transmission lines, substations and network components to ensure that power can be efficiently and stably transmitted from the generation point to the consumption area. Use specialized grid simulation software to perform detailed network simulations based on the new configuration plan. Including applying various predetermined load and fault scenarios in a simulated environment to evaluate the performance of the new grid design in actual operation. The simulation test will focus on the load response capability and fault recovery capability under the new configuration. These tests help evaluate the stability and recovery speed of the grid in the face of high load or sudden faults. By analyzing the test results, especially the efficiency of load response and fault recovery, the connection path configuration plan can be adjusted and optimized. The optimization result is an improved network configuration that improves the performance and stability of the entire grid and generates cross-regional connection optimization results. The simulation test process quantifies the performance of the newly configured grid through the average response score, which is: ; in, Represents the average response score of the simulation test, which is mainly used to quantify the performance of the new grid configuration. It is Performance scores under each test scenario, is the total number of test scenarios. The average response score is based on multiple indicators, such as response time, recovery speed, system stability, and ability to handle maximum load. The average response score provides a metric to evaluate the pros and cons of a new design compared to existing or remaining candidate solutions. The average response score obtained in the simulation test is compared with the preset target or historical data to evaluate the performance of the new configuration. Based on the scoring results, the connection path configuration is further refined and adjusted, and different network design variants are tried to find the optimal solution. The adjusted network configuration is tested repeatedly until the configuration with the highest score is found. The final optimized configuration is used as the cross-regional connection optimization result for actual grid construction and operation improvement.

[0051] In step S6, based on the cross-regional connection optimization results, combined with the grid operation data and real-time application feedback, the grid behavior analysis is performed, the adjustment strategy and the prediction model are optimized, the impact of the control strategy on the performance of the grid is evaluated, and the grid control evaluation results are obtained. The grid control evaluation results include the analysis of the grid behavior, the adaptability verification of the adjustment strategy, and the consistency of the prediction model. Step S6 specifically includes: S601: Based on the cross-regional connection optimization results, collect and analyze the operation data of the power grid nodes, including load changes, energy consumption rate and fault frequency, identify key problems and performance bottlenecks in power grid operation, and generate power grid operation analysis results.

[0052] Based on the results of cross-regional connection optimization, the comprehensive operation data of the power grid nodes is collected and analyzed, involving key operation indicators such as load changes, energy consumption rate and fault frequency of each node. Data analysis techniques, such as machine learning and pattern recognition, are used to identify key problems and performance bottlenecks in power grid operation. The analysis process includes data processing and trend identification to ensure early detection and timely response to problems, and to generate power grid operation analysis results. The power grid operation analysis results include evaluating the load changes of each node in the power grid, including load peaks, load valleys and load fluctuations, which helps to understand the load carrying capacity and load distribution of the power grid in different time periods; analyzing the energy efficiency of each node, including total energy consumption, unit service energy consumption, etc., can identify areas or equipment with low energy efficiency and guide the implementation of energy efficiency improvement measures; statistics on the frequency, type and distribution of faults in the power grid, as well as the response time and efficiency to faults, help evaluate the reliability of the power grid and the efficiency of the maintenance team. Use data analysis techniques to identify key problems and bottlenecks that lead to performance degradation, including equipment aging, unreasonable network structure, or insufficient operation and maintenance strategies. Through charts, maps and visualization tools, the analysis results and key indicators are intuitively displayed, making it easier for decision makers to understand and take action. The formula used is: ; in, Indicates the power grid operation efficiency, It is The load variation of each node, is the corresponding energy consumption, is the total number of nodes.

[0053] S602: Use the results of the grid operation analysis to adjust the grid connection control strategy, optimize the input parameters of the prediction model, including adjusting energy distribution and response time, optimize the overall responsiveness and efficiency of the grid, and generate an adjusted control strategy. The prediction model is used to predict the load demand of the grid, the output of renewable energy (such as wind and solar energy), and key factors such as market price changes. The prediction helps operators make decisions to ensure the stability and efficiency of the grid. The model can be a physics-based simulation or a data-driven prediction method, such as time series analysis, machine learning, etc.

[0054] The grid operation analysis results are used to adjust and optimize the grid control strategy. The overall responsiveness and efficiency of the grid are optimized by modifying the input parameters of the forecast model, such as the energy allocation ratio and response time. Adjusting and optimizing the input parameters of the forecast model includes adjusting the input ratio of different energy sources according to the predicted load and renewable energy output. For example, increasing the wind power output ratio in response to high wind speed forecasts, or adjusting fossil fuel-based power generation to cope with low solar output weather. Response time refers to the speed at which the grid control system reacts to load changes. Optimizing the response time involves reducing the delay from the system detecting load changes to taking action, ensuring that the grid can quickly balance supply and demand. By adjusting key parameters and combining actual operating data to verify the effect of parameter adjustment, ensure that the power grid can maintain optimal performance under different operating conditions, and generate an adjusted control strategy. The adjusted control strategy is a new set of operating rules based on the above analysis and optimization results, which are programmed into the power grid management system to automatically adjust and optimize the operation of the power grid. By using power grid simulation software to simulate the operation of the power grid under different parameter configurations, identify the optimal solution, and integrate real-time monitoring data to dynamically adjust the control strategy to ensure adaptability and efficiency, the optimized parameters and rules are updated to the power grid control system through software updates or configuration changes. The control efficiency of the adjusted control strategy is calculated using the following formula: ; in, represents the adjusted control efficiency, It is The responsiveness of the adjustment parameters, is the corresponding time delay, is the adjustment factor, is the number of tuning parameters.

[0055] S603: According to the adjusted control strategy, analyze the real-time application feedback, evaluate the effectiveness of the strategy through simulation test and real-time operation data comparison, evaluate the impact of the control strategy on the performance of the power grid, and generate the grid-connected control evaluation results.

[0056] According to the adjusted control strategy, feedback analysis of real-time application is carried out, and the effectiveness of the new strategy is compared and evaluated by combining simulation test results and real-time operation data. Data analysis tools are used to compare the data before and after, and the specific impact of the control strategy on the performance of the power grid is evaluated, especially under high load and high change conditions. Ensure the stable operation of the power grid, and make necessary fine-tuning of the strategy to meet future operation challenges, and generate grid-connected control evaluation results. The strategy evaluation score is used to evaluate the adjusted control strategy, and the formula is: ; in, represents the strategy evaluation score, It is Performance data under each test scenario, is the degree of restriction of the scene, is the performance weight, is the number of test scenarios. The strategy evaluation score is an evaluation of the adjusted control strategy. It measures the effectiveness of the control strategy by comparing the real-time or simulated test results with the established standards. The grid control evaluation results are generated using the strategy evaluation score as an important metric. The performance of the overall control system is evaluated by taking a weighted sum of the ratio of the control deviation to the evaluation standard for each section. The strategy evaluation score indicates the overall performance of the entire control system during the evaluation period. The grid control evaluation results evaluate the performance of the control strategy in actual operation after adjustment, especially under high load and high change conditions. The strategy evaluation score is used to measure the effectiveness of the adjusted control strategy and is a key component in generating the grid control evaluation results.

[0057] The present invention adopts real-time data tracking and dynamic filtering technology to improve the response speed and flexibility of the power grid, and makes timely adjustments according to the deviations and trends of the power grid data to ensure the stability of the power grid state. The tracking and feedback adjustment of real-time data significantly improves the power grid's ability to respond to emergencies, reduces dependence on frequency regulation services and backup energy, and reduces operating costs. By optimizing the configuration of power grid nodes and connections and combining power grid operation data with real-time application feedback to analyze power grid behavior, not only the overall energy efficiency of the system is improved, but also the environmental sustainability of the power grid is enhanced.

[0058] Embodiment 2 This embodiment provides a grid-connected control system based on configuration optimization, including: The power grid initial state estimation module is configured to obtain power grid operation data, perform time series analysis on the power grid operation data, and obtain the initial state of the power grid; The correction state prediction module is configured to determine the filtering parameters of the power grid operation data according to the initial state of the power grid; adjust the power grid state according to the deviation of the filtered power grid data, and generate the correction state prediction result; The grid-connected control module is configured to dynamically adjust the frequency and pressure of the power grid by correcting the state prediction results, simulating the power grid operation feedback optimization adjustment strategy, and obtaining the power grid adjustment strategy; A power grid optimization module is configured to analyze the power grid structure and flow efficiency according to the power grid adjustment strategy, optimize the configuration of power grid nodes and connections, simulate and verify the configuration adjustment effect of power grid nodes and connections, and generate an optimized power grid design solution; The cross-regional connection analysis module is configured to analyze the data transmission and power efficiency of the cross-regional connection according to the optimized power grid design scheme, configure the power grid connection path, and generate the cross-regional connection optimization result; The evaluation module is configured to adjust the grid control strategy and evaluate the grid control results based on the cross-region connection optimization results combined with the grid operation data and real-time application feedback.

[0059] It should be noted here that the various modules in this embodiment correspond one-to-one to the steps of the method in Example 1, and the specific implementation process is the same, which will not be repeated here.

[0060] Embodiment 3 This embodiment provides an electronic device, including a memory, a processor, and a computer program stored in the memory, wherein the processor executes the computer program to complete the steps of the method in the first embodiment.

[0061] Embodiment 4 This embodiment provides a computer-readable storage medium for storing computer instructions. When the computer instructions are executed by a processor, the steps of the method in the first embodiment are completed.

[0062] Embodiment 5 This embodiment provides a computer program product, including a computer program / instruction, which implements the steps of the method in Embodiment 1 when executed by a processor.

[0063] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A grid-connected control method based on configuration optimization, characterized in that: include: Obtain grid operation data, perform time series analysis on the grid operation data, and obtain the initial state of the grid; Determine filtering parameters for grid operation data according to the initial state of the grid; According to the deviation of the filtered power grid data, the power grid state is adjusted to generate a correction state prediction result; By correcting the state prediction results, the power grid is dynamically adjusted in frequency and pressure, and the power grid operation feedback optimization adjustment strategy is simulated to obtain the power grid adjustment strategy; According to the grid adjustment strategy, analyze the grid structure and flow efficiency, optimize the configuration of grid nodes and connections, simulate and verify the configuration adjustment effect of grid nodes and connections, and generate an optimized grid design plan; According to the optimized grid design plan, analyze the data transmission and power efficiency of cross-regional connections, configure the grid connection path, and generate cross-regional connection optimization results; Based on the cross-regional connection optimization results, combined with grid operation data and real-time application feedback, the grid control strategy is adjusted and the grid control results are evaluated.

2. A grid-connected control method based on configuration optimization as claimed in claim 1, characterized in that: The obtaining of the grid operation data and performing time series analysis on the grid operation data to obtain the grid initial state includes: Construct an initialization model for grid state estimation, input grid operation data, and generate grid parameter records; Based on the grid parameter records, the grid status data of differentiated time periods are updated in time series, and trend analysis and anomaly detection are performed on continuous data points to generate time series tracking data; Based on the time series tracking data, the Fourier transform method is used to predict the short-term grid behavior, evaluate the real-time state of the grid, and generate the initial state of the grid.

3. A grid-connected control method based on configuration optimization as claimed in claim 1, characterized in that: Determining filtering parameters for grid operation data according to the initial state of the grid; According to the deviation of the filtered grid data, the grid state is adjusted to generate the correction state prediction results, including: According to the initial state of the power grid, the initial setting of the filter parameters is performed, the sensitivity and response threshold of the parameters are adjusted by calibrating the parameters to match the real-time state data of the power grid, and the parameter adjustment record is generated; Through parameter adjustment records, data deviation tracking is performed, voltage, current and frequency deviations are analyzed for each grid measurement point, the frequency of deviations is recorded, and deviation analysis data is generated; Deviation analysis data is used to dynamically adjust the power grid state, and the short-term power grid behavior is predicted by modifying the filter parameters to generate correction state prediction results.

4. A grid-connected control method based on configuration optimization as claimed in claim 1, characterized in that: The method of dynamically adjusting the frequency and pressure of the power grid by correcting the state prediction result, simulating the power grid operation feedback optimization adjustment strategy, and obtaining the power grid adjustment strategy includes: Use the correction state prediction results to set frequency adjustment parameters, respond to the real-time demand and prediction deviation of the power grid, design frequency management measures, and generate frequency adjustment parameter configurations; Based on the frequency adjustment parameter configuration, adjust the pressure control parameters to match the grid load changes, monitor the grid load and pressure feedback data, and generate the pressure adjustment strategy; Adopt pressure adjustment strategy, integrate frequency and pressure adjustment results, simulate grid operation to verify adjustment efficiency, analyze changes in grid operation through real-time feedback, and generate grid adjustment strategy.

5. A grid-connected control method based on configuration optimization as claimed in claim 1, characterized in that: The grid adjustment strategy is used to analyze the grid structure and flow efficiency, optimize the configuration of grid nodes and connections, simulate and verify the configuration adjustment effect of grid nodes and connections, and generate an optimized grid design solution, including: Through the grid adjustment strategy, the connection efficiency and flow characteristics of the grid structure are analyzed, the existing bottlenecks and inefficient areas are identified, and the grid structure efficiency analysis results are generated; Based on the results of the grid structure efficiency analysis, reconfigure the grid nodes and connection methods, adjust the connection paths and optimize the node layout to generate the optimal configuration of nodes and connections; Based on the optimized configuration of nodes and connections, a simulation run of the adjusted power grid design is performed, and the adjustment effect is tested by simulating various load and fault scenarios to generate an optimized power grid design solution.

6. A grid-connected control method based on configuration optimization as claimed in claim 1, characterized in that: The method of analyzing the data transmission and power efficiency of cross-regional connections according to the optimized grid design scheme, configuring grid connection paths, and generating cross-regional connection optimization results includes: Based on the optimized grid design scheme, analyze the data transmission efficiency of cross-regional grid connections, evaluate the power loss and transmission delay of multiple connection paths, and generate data transmission efficiency analysis results; Using the data transmission efficiency analysis results, reconfigure the connection path of the power grid, adjust the number of connection nodes and the layout of the connection lines, and generate a connection path configuration plan; Implement connection path configuration plans, perform network simulations, test grid performance under new configurations, and generate cross-regional connection optimization results.

7. A grid-connected control method based on configuration optimization as claimed in claim 6, characterized in that: The genetic algorithm is used to reconfigure the connection path of the power grid, adjust the number of connection nodes and the layout of the connection lines, and generate a connection path configuration plan; the formula of the genetic algorithm is: ； Among them, C is the total cost, l i is the length of connection i, d is the number of connections, e i is the energy consumption per unit length of connection i, r i is the reliability score of connection i, f i is the frequency utilization rate, k is the weight factor of reliability and frequency utilization rate, m is the safety factor weight, s i is the security level of connection i, p i is the priority coefficient.

8. A grid-connected control method based on configuration optimization as claimed in claim 1, characterized in that: The above-mentioned method, based on the cross-regional connection optimization result, combines the grid operation data and real-time application feedback, adjusts the grid control strategy, and evaluates the grid control result, including: Based on the cross-regional connection optimization results, analyze the operation data of the power grid nodes, identify the key problems and performance bottlenecks of the power grid operation, and generate the power grid operation analysis results; Use the results of grid operation analysis to adjust the grid connection control strategy, optimize the overall responsiveness and efficiency of the grid, and generate an adjusted control strategy; According to the adjusted control strategy, the effectiveness of the control strategy and its impact on the performance of the power grid are evaluated through simulation tests and real-time operation data comparison, and the grid-connected control results are evaluated.

9. A grid-connected control system based on configuration optimization, characterized in that: include: The power grid initial state estimation module is configured to obtain power grid operation data, perform time series analysis on the power grid operation data, and obtain the initial state of the power grid; The correction state prediction module is configured to determine the filtering parameters of the power grid operation data according to the initial state of the power grid; adjust the power grid state according to the deviation of the filtered power grid data, and generate the correction state prediction result; The grid-connected control module is configured to dynamically adjust the frequency and pressure of the power grid by correcting the state prediction results, simulating the power grid operation feedback optimization adjustment strategy, and obtaining the power grid adjustment strategy; A power grid optimization module is configured to analyze the power grid structure and flow efficiency according to the power grid adjustment strategy, optimize the configuration of power grid nodes and connections, simulate and verify the configuration adjustment effect of power grid nodes and connections, and generate an optimized power grid design solution; The cross-regional connection analysis module is configured to analyze the data transmission and power efficiency of the cross-regional connection according to the optimized power grid design scheme, configure the power grid connection path, and generate the cross-regional connection optimization result; The evaluation module is configured to adjust the grid control strategy and evaluate the grid control results based on the cross-region connection optimization results combined with the grid operation data and real-time application feedback.

10. An electronic device, characterized in that: The invention comprises a memory, a processor and a computer program stored in the memory, wherein the processor executes the computer program to complete the steps of the method according to any one of claims 1 to 8.

11. A computer-readable storage medium, characterized in that: Used to store computer instructions, which, when executed by a processor, complete the steps of the method according to any one of claims 1 to 8.

12. A computer program product comprising a computer program / instructions, characterized in that When the computer program / instructions are executed by a processor, the steps of the method according to any one of claims 1 to 8 are implemented.

Images