Analysis method and system based on high-impact meteorological-electricity event modeling data

Abstract

The present invention discloses an analysis method and system based on high-impact meteorological-electricity event modeling data, belonging to the field of electrical digital data processing technology. The method comprises the following steps: performing a quantitative determination of the state of the wind turbine according to the state parameters of each wind turbine in the wind farm, obtaining a quantitative determination result of the wind turbine state, determining whether to perform dynamic processing of the wind turbine based on the quantitative determination result of the wind turbine state, and obtaining each normal wind turbine based on the dynamic processing result of the wind turbine; performing a quantitative determination of wind speed fluctuation according to the wind speed fluctuation parameters of each normal wind turbine in each monitoring time period in the wind farm, obtaining a quantitative determination result of wind speed fluctuation, determining whether to perform dynamic correction of power generation and optimization of the ensemble empirical mode decomposition method based on the quantitative determination result of wind speed fluctuation; and performing meteorological-electricity data analysis using the optimized ensemble empirical mode decomposition method, thereby improving the accuracy of the analysis of high-impact meteorological-electricity event modeling data.

Description

Technical Field

[0001] The present invention relates to the technical field of electrical digital data processing, and in particular to an analysis method and system based on high-impact meteorological-electrical event modeling data. Background Art

[0002] With the advancement of meteorological science, the accuracy of meteorological forecasts has improved, but the proportion of meteorological sensitive loads in the total load has increased, and extreme weather has the characteristics of low probability, high severity consequences and group occurrence. Traditional risk assessment methods also have limitations. In this context, in order to improve the safety of the power system and achieve refined management, it is necessary to carry out research on analysis methods for high-impact meteorological-power event modeling data.

[0003] Existing analysis methods for high-impact meteorological-power event modeling data are implemented in a variety of ways: one is to use historical meteorological and power data to train a time-series prediction network model, and adjust the parameters to optimize the model in combination with loss values; the second is to collect data with the help of the power grid SCADA (Supervisory Control and Data Acquisition) system, process it with big data methods, and then explore the correlation between meteorological and power grid faults through clustering, classification or association algorithms; the third is to build corresponding models for the impact of extreme meteorological disasters, propose hybrid simulation methods and build a platform for implementation.

[0004] For example, the invention patent announcement with announcement number: CN105677791B discloses a method and system for analyzing the operating data of a wind turbine generator set, comprising: obtaining operating data, status data and unit fault data of the wind turbine generator set collected within a predetermined statistical time period as initial data; analyzing these data, extracting attributes related to changes in the operating state of the wind turbine generator set, and sampling the initial data to obtain sample data of multiple operating state categories; performing statistical analysis on the sample data of multiple operating state categories according to the extracted attributes to obtain multiple operating characteristic quantities of the wind turbine generator set that meet predetermined conditions of multiple operating state categories, each operating characteristic quantity including the value of a single attribute or the values ​​of multiple attributes; drawing a change graph of the operating characteristic quantity within a specified state time period based on the obtained multiple operating characteristic quantities to characterize the changes in the operating characteristic quantities of the wind turbine generator set under the specified state.

[0005] For example, the invention patent publication number CN110503131B discloses a wind turbine health monitoring system based on big data analysis, which includes a feature extraction module, a fault classification module, a black powder rule library, a big data analysis platform, and a system interface module. This system employs a fault diagnosis method based on the EMD algorithm and data binning. The IMF signal is first decomposed using the EMD algorithm, and its amplitude-domain parameter characteristics are extracted as feature vectors. These are then input into the SVM for fault classification, and verified through preliminary simulations and actual data. Using big data analysis, both the EMD and statistical descriptive feature operators are implemented in a distributed parallel structure based on the open-source big data platform Spark.

[0006] However, in the process of implementing the technical solutions of the invention in the embodiments of the present application, the present application found that the above technology has at least the following technical problems:

[0007] In the existing technology, when using wind renewable energy to obtain wind farm power generation in the context of frequent extreme weather and climate events such as typhoons, extreme wind conditions may affect the accuracy of power generation collection, thereby affecting the accuracy of the power generation processing process, resulting in low analysis accuracy of high-impact meteorological-power event modeling data. Summary of the Invention

[0008] The present invention provides an analysis method and system based on high-impact meteorological-electricity event modeling data, thereby solving the problem in the prior art that, when wind renewable energy is used to obtain wind farm power generation in the context of frequent extreme weather and climate events such as typhoons, extreme wind conditions may affect the accuracy of power generation collection, thereby affecting the accuracy of the power generation processing process, resulting in low analysis accuracy of high-impact meteorological-electricity event modeling data, thereby achieving improved analysis accuracy of high-impact meteorological-electricity event modeling data.

[0009] The present invention provides an analysis method based on high-impact meteorological-electricity event modeling data, comprising the following steps: performing a quantitative determination of the state of the wind turbine according to the state parameters of each wind turbine in the wind farm to obtain a quantitative determination result of the wind turbine state; determining whether to perform dynamic processing of the wind turbine based on the quantitative determination result of the wind turbine state; and obtaining each normal wind turbine according to the dynamic processing result of the wind turbine; the dynamic processing of the wind turbine means processing the wind turbine according to the quantitative determination result of the wind turbine state to reduce the influence of the wind turbine efficiency on the accuracy of the analysis of the high-impact meteorological-electricity event modeling data; performing a quantitative determination of the wind speed fluctuation according to the wind speed fluctuation parameters of each normal wind turbine in each monitoring time period in the wind farm. The wind speed fluctuation is quantitatively judged and the results of the wind speed fluctuation are obtained. Based on the results of the wind speed fluctuation quantitative judgment, it is determined whether to perform dynamic correction of power generation and optimization of the ensemble empirical mode decomposition method. The dynamic correction of power generation and optimization of the ensemble empirical mode decomposition method mean that the power generation of normal wind turbines is corrected and the standard deviation of white noise in the ensemble empirical mode decomposition method is optimized according to the results of the wind speed fluctuation quantitative judgment, so as to reduce the influence of abnormal wind speed fluctuation on the accuracy of data analysis of high-impact meteorological-power event modeling; the optimized ensemble empirical mode decomposition method is used to analyze meteorological-power data. The meteorological-power data analysis means that the AI-driven LLM Agent large model is used to perform parameterized modeling and analysis on the wind power generation data processed by the optimized ensemble empirical mode decomposition method, and a multi-dimensional coupling indicator system is constructed.

[0010] The present invention also provides a system for applying an analysis method based on high-impact meteorological-electricity event modeling data, comprising: a wind turbine processing module, a power generation correction module, a data analysis module and a meteorological-electricity database; wherein the wind turbine processing module is used to perform quantitative determination of the wind turbine state according to the wind turbine state parameters of each wind turbine in the wind farm, obtain a quantitative determination result of the wind turbine state, determine whether to perform dynamic wind turbine processing based on the quantitative determination result of the wind turbine state, and obtain each normal wind turbine based on the dynamic wind turbine processing result; the power generation correction module is used to perform quantitative determination of wind speed fluctuation according to the wind speed fluctuation parameters of each normal wind turbine in each monitoring time period in the wind farm, obtain a quantitative determination result of wind speed fluctuation, determine whether to perform dynamic power generation correction and optimize the ensemble empirical mode decomposition method based on the quantitative determination result of wind speed fluctuation; the data analysis module is used to perform meteorological-electricity data analysis using the optimized ensemble empirical mode decomposition method, and the meteorological-electricity data analysis represents the use of an AI-driven LLM Agent large model to perform parameterized modeling and analysis on the wind power generation data processed by the optimized ensemble empirical mode decomposition method, and construct a multi-dimensional coupling index system.

[0011] One or more technical solutions provided in the embodiments of this application have at least the following technical effects or advantages:

[0012] 1. The present invention provides an analysis method based on high-impact meteorological-electricity event modeling data, thereby effectively reducing the impact of wind turbine anomalies and wind speed fluctuation anomalies on the accuracy of high-impact meteorological-electricity event modeling data analysis, and further realizing dynamic processing of wind turbines, dynamic correction of power generation, and optimization of the ensemble empirical mode decomposition method, thereby improving the accuracy and reliability of data analysis, and providing more accurate data support for meteorological-electricity event modeling of wind farms.

[0013] 2. The present invention compares the wind turbine status assessment value with the preset threshold value, and dynamically processes the wind turbine and provides abnormal prompts based on different state quantification judgment results, so as to accurately identify and process normal wind turbines and abnormal wind turbines, reduce the impact of abnormal wind turbine status on the overall power generation efficiency, and further achieve the improvement of the operating stability and efficiency of the wind farm by dynamically adjusting the pitch angle and yaw angle and timely removing the abnormal wind turbines, thereby optimizing the overall performance of the wind farm and the accuracy of data analysis.

[0014] 3. The present invention compares the wind speed fluctuation assessment value with the wind speed fluctuation threshold, and issues abnormal fluctuation prompts and power generation corrections to the wind turbine based on the quantitative determination results of the wind speed fluctuation, thereby being able to timely identify abnormal wind speed fluctuations and dynamically adjust relevant data, thereby reducing the impact of wind speed fluctuations on power generation. This improves the power generation efficiency and data analysis accuracy of the wind farm by correcting white noise data and matching the power generation correction value, thereby optimizing the operation and management of the wind farm. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 Flowchart of an analysis method based on high-impact meteorological-electricity event modeling data provided in an embodiment of the present application.

[0016] Figure 2 This is a flowchart of the AI-driven LLM Agent fully automatic analysis of meteorological and energy data provided in the embodiment of this application.

[0017] Figure 3 A mind map for quantitative determination of wind turbine status provided in an embodiment of the present application.

[0018] Figure 4 A mind map for quantitative determination of wind speed fluctuations provided in an embodiment of the present application.

[0019] Figure 5 A schematic diagram of the structure of an analysis system based on high-impact meteorological-electricity event modeling data provided in an embodiment of the present application. DETAILED DESCRIPTION

[0020] The embodiments of the present application provide an analysis method and system based on high-impact meteorological-electricity event modeling data, thereby solving the problem in the prior art that, when wind renewable energy is used to obtain wind farm power generation in the context of frequent extreme weather and climate events such as typhoons, extreme wind conditions may affect the accuracy of power generation collection, thereby affecting the accuracy of the power generation processing process, resulting in low analysis accuracy of high-impact meteorological-electricity event modeling data. The wind turbine state is quantitatively determined by the wind turbine state parameters of each wind turbine in the wind farm to obtain a quantitative determination result of the wind turbine state. Based on the quantitative determination result of the wind turbine state, it is determined whether to perform dynamic wind turbine processing, and each normal wind turbine is obtained according to the dynamic wind turbine processing result; the wind speed fluctuation is quantitatively determined according to the wind speed fluctuation parameters of each normal wind turbine in each monitoring time period in the wind farm to obtain a quantitative determination result of the wind speed fluctuation. Based on the quantitative determination result of the wind speed fluctuation, it is determined whether to perform dynamic power generation correction and optimization of the ensemble empirical mode decomposition method; the optimized ensemble empirical mode decomposition method is used to analyze meteorological-electricity data, thereby improving the accuracy of analysis of high-impact meteorological-electricity event modeling data.

[0021] The technical solution in the embodiments of the present application is to solve the problem that when using wind renewable energy to obtain wind farm power generation in the context of frequent extreme weather and climate events such as typhoons, extreme wind conditions may affect the accuracy of power generation collection, thereby affecting the accuracy of power generation processing, resulting in low analysis accuracy of high-impact meteorological-power event modeling data. The overall concept is as follows:

[0022] First, a quantitative judgment is made based on the operating status parameters of each wind turbine in the wind farm to generate a wind turbine status level. The wind turbines are dynamically processed based on the judgment results, and normal wind turbines are screened out to eliminate the interference of low-quality data on modeling. Then, the wind speed fluctuation parameters of normal wind turbines in each monitoring time period are quantitatively evaluated. If the fluctuation exceeds the threshold, dynamic correction of power generation and optimization of the ensemble empirical mode decomposition method are triggered, thereby reducing the impact of wind speed anomalies on data stability. Finally, the optimized ensemble empirical mode decomposition method is used to decompose the meteorological-power coupling data, and the AI-driven LLM Agent is used to perform parameterized feature extraction and multi-dimensional analysis of the IMF components to construct a coupling indicator system covering meteorological sensitivity, equipment status and grid response capability, thereby improving the accuracy of data analysis for high-impact meteorological-power event modeling.

[0023] In order to better understand the above technical solution, the above technical solution will be described in detail below with reference to the accompanying drawings and specific implementation methods.

[0024] like Figure 1As shown, it is a flow chart of the analysis method based on high-impact meteorological-electricity event modeling data provided by an embodiment of the present application, the method comprising the following steps: performing a quantitative determination of the state of the wind turbine according to the state parameters of each wind turbine in the wind farm, obtaining a quantitative determination result of the wind turbine state, determining whether to perform dynamic processing of the wind turbine based on the quantitative determination result of the wind turbine state, and obtaining each normal wind turbine according to the dynamic processing result of the wind turbine, wherein the dynamic processing of the wind turbine means processing the wind turbine according to the quantitative determination result of the wind turbine state to reduce the influence of the wind turbine efficiency on the accuracy of the analysis of the high-impact meteorological-electricity event modeling data; performing a wind speed determination according to the wind speed fluctuation parameters of each normal wind turbine in each monitoring time period in the wind farm. Fluctuations are quantitatively determined to obtain the quantitative determination results of wind speed fluctuations. Based on the quantitative determination results of wind speed fluctuations, it is determined whether to perform dynamic correction of power generation and optimization of the ensemble empirical mode decomposition method. The dynamic correction of power generation and optimization of the ensemble empirical mode decomposition method mean that the power generation of normal wind turbines is corrected and the standard deviation of white noise in the ensemble empirical mode decomposition method is optimized according to the quantitative determination results of wind speed fluctuations to reduce the impact of abnormal wind speed fluctuations on the accuracy of data analysis of high-impact meteorological-power event modeling; meteorological-power data analysis is performed using the optimized ensemble empirical mode decomposition method. The meteorological-power data analysis means using the AI ​​(Artificial Intelligence)-driven LLM Agent (Large Language Model Agent) large model to perform parameterized modeling and analysis on the wind power generation data processed by the optimized ensemble empirical mode decomposition method, and construct a multi-dimensional coupling indicator system. The wind power generation data includes power generation, temperature, precipitation, relative humidity, wind speed, sunshine hours, etc. in the wind farm.

[0025] In addition, the meteorological and power database stores relevant data for analytical methods based on high-impact meteorological and power event modeling data. This includes critical wind energy utilization, critical wind speed variation coefficient, critical active power fluctuation rate, pitch angle adjustment values ​​corresponding to each wind turbine state deviation range, and first and second wind turbine state thresholds. Data in the meteorological and power database can be directly queried through public databases such as the Meteorological Science Data Center and the Electric Power Research Institute database, or obtained through collaboration with relevant meteorological departments and power companies.

[0026] In this embodiment, the present invention implements hierarchical dynamic processing based on the quantitative determination results of wind turbine status parameters. This intelligently adjusts the pitch and yaw angles to optimize wind turbine operating status, effectively isolates abnormal wind turbines, and ensures the quality of input data. Secondly, it quantitatively determines wind speed fluctuations of normal wind turbines. By dynamically correcting power generation data and optimizing the white noise standard deviation parameter in the ensemble empirical mode decomposition method, it effectively suppresses the interference of abnormal wind speed fluctuations on data analysis. Finally, the semantic understanding capabilities of the LLM Agent in the LLM Agent large model are utilized to comprehensively understand the relevant content of the optimized multi-source meteorological-power data parameterization scheme, deeply analyze the parameter relationships and data characteristics involved, and automatically generate valuable analysis questions and directions. During this understanding process, the AI-driven LLM Agent's fully automatic meteorological-energy data analysis process utilizes its knowledge reserves and reasoning capabilities to process data, explore potential associations, and construct a coupling indicator system based on the analysis results. This not only improves the operating efficiency and safety warning capabilities of wind farms, but also provides reliable data support and decision-making basis for meteorological-power coupling research, maximizing the value of wind power data mining.

[0027] like Figure 2 The figure shows the AI-driven LLM Agent automatic analysis flow chart for meteorological-energy data provided by the embodiment of this application. First, based on the questions raised by the LLM, the intention is understood and in-depth thinking is carried out to generate a task plan. When executing the task, references are searched and researched in real time. Then, data analysis and visualization operations are performed on the data processed by the collective empirical mode decomposition method. Finally, conclusions are generated and a report is written. During the task execution process, reflection and plan updates are also carried out to ensure the effective progress of the task.

[0028] Figure 3 A mind map for quantitative determination of fan status provided in an embodiment of the present application includes: real-time monitoring of the fan status parameters of each fan to obtain a fan status assessment value of each fan, performing quantitative determination of the fan status according to the fan status assessment value of each fan, if the fan status assessment value of a certain fan is less than a first fan status threshold, the corresponding fan status quantitative determination result is recorded as an unqualified fan status, and no additional processing is performed; if the fan status assessment value of a certain fan is greater than or equal to the first fan status threshold and less than a second fan status threshold, the corresponding fan status quantitative determination result is recorded as a fan status to be optimized, and dynamic fan processing is performed; if the fan status assessment value of a certain fan is greater than or equal to the second fan status threshold, the corresponding fan status quantitative determination result is recorded as a qualified fan status, the abnormal fan is marked, and the preset personnel are notified to cut out the abnormal fan.

[0029] Specifically, the steps of quantitatively determining the status of wind turbines according to the status parameters of wind turbines in the wind farm include: obtaining reference data of wind turbine status parameters from a preset meteorological power database, specifically including: critical wind energy utilization rate, critical wind speed variation coefficient and critical active power fluctuation rate; performing a proportion approach calculation on the wind energy utilization rate, wind speed variation coefficient and critical active power fluctuation rate of each wind turbine respectively with the corresponding critical wind energy utilization rate, critical wind speed variation coefficient and active power fluctuation rate, the proportion approach calculation being a ratio calculation, and then using the weight ratio of the wind turbine status parameters to weight the results of the proportion approach calculation and then couple them to obtain the wind turbine status assessment value of each wind turbine, the wind turbine status parameter weight ratio including the wind energy utilization rate weight ratio, the wind speed variation coefficient weight ratio and the active power fluctuation rate weight ratio, and the wind turbine status assessment value represents the wind turbine status. The invention also provides quantitative data on the degree of influence of the wind turbine state parameters on the accuracy of wind turbine data, and the wind turbine state parameters include wind energy utilization rate, wind speed variation coefficient and active power fluctuation rate; the first threshold value of the wind turbine state and the second threshold value of the wind turbine state are obtained from the preset meteorological and power database; the wind turbine state assessment value of each wind turbine is compared with the first threshold value of the wind turbine state and the second threshold value of the wind turbine state respectively; if the wind turbine state assessment value of a certain wind turbine is less than the first threshold value of the wind turbine state, the corresponding wind turbine state quantitative determination result is recorded as the wind turbine state is unqualified; if the wind turbine state assessment value of a certain wind turbine is greater than or equal to the first threshold value of the wind turbine state and less than the second threshold value of the wind turbine state, the corresponding wind turbine state quantitative determination result is recorded as the wind turbine state to be optimized; if the wind turbine state assessment value of a certain wind turbine is greater than or equal to the second threshold value of the wind turbine state, the corresponding wind turbine state quantitative determination result is recorded as the wind turbine state is qualified.

[0030] The fan status assessment value of each fan is obtained as follows:

[0031] ;

[0032] Where, represents the fan status assessment value of the i-th fan, represents the weight ratio of wind energy utilization, represents the weight ratio of wind speed variation coefficient, Indicates the weight ratio of active power fluctuation rate, It represents the wind energy utilization rate of the i-th fan, which is the ratio of the airflow kinetic energy passing through the impeller range to the output electrical energy during the current monitoring period. represents the critical wind energy utilization rate, The coefficient of variation of wind speed of the i-th wind turbine is a key indicator for measuring the intensity of wind speed fluctuation. Specifically, it is the ratio of the standard deviation of wind speed to the average wind speed during the current monitoring period. The wind speed can be directly measured by an anemometer. represents the critical wind speed variation coefficient, The active power fluctuation rate of the i-th wind turbine refers to the degree of fluctuation of the active power output of the wind turbine during the current monitoring period. The output active power of the wind turbine can be collected in real time using the power measurement equipment at the output end of the wind turbine. The active power fluctuation rate is the ratio of the difference between the maximum and minimum active power values ​​during the current monitoring period to the average active power. represents the critical active power fluctuation rate, where i is the number of each wind turbine, i=1,2,3,...,N, and N is the total number of wind turbines.

[0033] 、 and These are the weight ratios corresponding to the wind energy utilization rate, wind speed variation coefficient, and active power fluctuation rate preset in the meteorological and power database, respectively, which represent the numerical values ​​of the degree of influence of the wind energy utilization rate, wind speed variation coefficient, and active power fluctuation rate on the wind turbine status assessment value, and can be directly obtained from the meteorological and power database when used. These relationships are organized into a "mapping set", that is, a lookup table or corresponding rules. When the value of a wind turbine status parameter is actually monitored, this value can be searched according to the corresponding mapping set, and then the corresponding weight ratio can be obtained. This weight ratio is a number between 0 and 1, which represents the evaluation result of the degree of interference of the current wind turbine status parameter on the accuracy of power generation. For example, for wind energy utilization, there is a mapping set of wind energy utilization and the corresponding weight ratio of wind energy utilization. By inputting the value of wind energy utilization, we can get the corresponding weight ratio of wind energy utilization. For wind speed variation coefficient, there is a mapping set of wind speed variation coefficient and the corresponding weight ratio of wind speed variation coefficient. By inputting the value of wind speed variation coefficient, we can get the corresponding weight ratio of wind speed variation coefficient. For active power fluctuation rate, there is a mapping set of active power fluctuation rate and the corresponding weight ratio of active power fluctuation rate. By inputting the value of active power fluctuation rate, we can get the corresponding weight ratio of active power fluctuation rate. These mapping relationships can be many-to-one or one-to-one.

[0034] In this embodiment, the wind energy utilization rate, wind speed variation coefficient, and active power fluctuation rate are interrelated. For example, a higher wind speed variation coefficient reflects an increase in the ratio of the wind speed standard deviation to the mean, indicating an increase in the disorder of the wind speed field. By enhancing vortex shedding, energy dissipation, and load pulsation, the turbulence intensity increases, resulting in dramatic fluctuations in the instantaneous input energy to the impeller and a significant increase in the active power fluctuation rate. The power fluctuation rate reflects the fluctuation of the wind turbine output power. A larger power fluctuation rate indicates unstable output power, making it difficult for the wind turbine to operate under optimal conditions and unable to continuously and efficiently convert wind energy into electrical energy. The wind energy utilization rate may also be lower. When the wind speed variation coefficient increases, the instantaneous angle of attack of the impeller frequently deviates from the optimal design range, the lift coefficient fluctuates more, and the wind energy capture efficiency decreases. The wind turbine status assessment value obtained through comprehensive analysis can accurately assess the operating status of the wind turbine, determine whether the wind turbine is operating normally, and whether adjustment or maintenance is required. It can effectively improve the productivity and stability of the wind farm, avoid power loss caused by unstable factors, and ensure the efficient operation of the wind farm system. By dynamically processing the wind turbines through the wind turbine status assessment value of each wind turbine, the operating parameters of the wind turbines can be optimized according to the changes in wind speed and wind turbine status, ensuring that the wind turbines are always in the best working state under different environmental conditions, thereby improving the working efficiency and power generation capacity of the wind turbines.

[0035] Furthermore, the steps of determining whether to perform dynamic processing of the wind turbine based on the result of the quantification of the wind turbine state, and obtaining each normal wind turbine according to the result of the dynamic processing of the wind turbine include: if the result of the quantification of the wind turbine state for a certain wind turbine is that the wind turbine state is qualified, no additional processing is performed; if the result of the quantification of the wind turbine state for a certain wind turbine is that the wind turbine state needs to be optimized, the pitch angle and the yaw angle are dynamically processed according to the wind turbine state evaluation value and wind speed of the wind turbine; if the result of the quantification of the wind turbine state for a certain wind turbine is that the wind turbine state is unqualified, the wind turbine is recorded as an abnormal wind turbine, and a wind abnormality prompt is issued; it is determined whether the number of wind turbines whose quantification results for the wind turbine state are unqualified exceeds a preset wind turbine number threshold, and if so, the preset personnel are prompted to cut out the abnormal fans in batches according to the priority parameters of each abnormal fan, otherwise the preset personnel are directly prompted to cut out the abnormal fans; the fans that are not recorded as abnormal fans are recorded as normal fans.

[0036] Among them, the step of dynamically processing the pitch angle and yaw angle according to the fan status assessment value and wind speed of the wind turbine includes: when the wind speed of the wind turbine is greater than the maximum value of the standard wind speed range preset in the meteorological and power database and the fan status assessment value of the current monitoring time period is greater than or equal to the fan status assessment value of the previous monitoring time period, the pitch angle of the wind turbine is gradually increased according to the obtained pitch angle adjustment value until the fan status change value of the next monitoring time period exceeds the fan status assessment value change threshold preset in the meteorological and power database, and the fan status change value is the difference between the fan status assessment value of the next monitoring time period and the fan status assessment value of the current monitoring time period. The pitch angle adjustment value is obtained by matching the current wind turbine state deviation value with the pitch angle adjustment value corresponding to each wind turbine state deviation range preset in the meteorological and power database. The current wind turbine state deviation value represents the difference between the wind turbine state assessment value of the current monitoring time period and the wind turbine state assessment value of the previous monitoring time period. When the wind speed of the wind turbine is greater than the maximum value of the preset standard wind speed range and the wind turbine state assessment value of the current monitoring time period is less than the wind turbine state assessment value of the previous monitoring time period, the pitch angle of the wind turbine is gradually reduced according to the obtained pitch angle adjustment value until the wind turbine state change value of the next monitoring time period exceeds the preset wind turbine state assessment value change. When the wind speed of the wind turbine is less than the minimum value of the preset standard wind speed range in the meteorological and power database and the wind turbine state assessment value of the current monitoring time period is greater than or equal to the wind turbine state assessment value of the previous monitoring time period, the pitch angle of the wind turbine is gradually increased according to the obtained pitch angle adjustment value until the wind turbine state change value of the next monitoring time period exceeds the preset wind turbine state assessment value change threshold; when the wind speed of the wind turbine is less than the minimum value of the preset standard wind speed range and the wind turbine state assessment value of the current monitoring time period is less than the wind turbine state assessment value of the previous monitoring time period, the pitch angle of the wind turbine is gradually reduced according to the obtained pitch angle adjustment value until the next The wind turbine state change value during the monitoring time period exceeds the preset wind turbine state assessment value change threshold; when the wind speed of the wind turbine is within the standard wind speed range preset in the meteorological and power database, the yaw angle is dynamically adjusted according to the wind direction sensor data of the wind turbine. The wind direction sensor data includes the real-time wind direction angle and the wind direction change rate. When the yaw angle is dynamically adjusted, the difference between the wind direction angle and the current yaw angle of the wind turbine is recorded as the initial target yaw angle in real time, and then the wind direction change rate is multiplied by the prediction time window obtained by the wind direction prediction model such as Kalman filtering or neural network, and the target yaw angle is obtained by summing the wind direction change rate with the initial target yaw angle, and the wind turbine is adjusted to the target yaw angle in real time.

[0037] In this embodiment, when obtaining the pitch angle adjustment value, the state deviation range of each wind turbine in the meteorological and power database corresponds to the pitch angle adjustment value one by one to form a mapping relationship table, which records each wind turbine state deviation range and its corresponding pitch angle adjustment value. These relationships can be one-to-one or many-to-one. When obtaining the pitch angle adjustment value, it is only necessary to input the wind turbine state deviation value into the mapping relationship table, and the meteorological and power database can quickly locate and return the pitch angle adjustment value corresponding to the wind turbine state deviation value. The present invention realizes precise hierarchical control through the quantitative determination results of the wind turbine state, significantly improving the operating efficiency and safety of the wind farm. Unqualified wind turbines are marked in time and the preset personnel are notified to cut out in a hierarchical manner, avoiding the spread of faults and reducing downtime losses. For the wind turbine to be optimized, the pitch angle and yaw angle are dynamically adjusted in combination with the wind speed and the wind turbine state assessment value to balance the power generation efficiency and mechanical load, extend the equipment life, and through data-driven closed-loop control, both the data quality of high-impact meteorological-power event modeling is guaranteed and the coordinated optimization of wind farm power generation stability and unit safety is achieved.

[0038] Among them, the step of prompting preset personnel to cut out abnormal wind turbines in batches according to the priority parameters of each abnormal wind turbine includes: obtaining a priority estimation value of each abnormal wind turbine according to the priority parameters of each abnormal wind turbine, the priority estimation value represents quantitative data of the degree of influence of the priority parameters on the wind turbine operation priority, and the priority parameters include the power generation contribution, average wind speed and power generation efficiency in the previous monitoring time period; sorting each abnormal wind turbine in order of the priority estimation value from small to large, and notifying the preset personnel to cut out abnormal wind turbines with a threshold number of wind turbines according to the sorting order, and monitoring the wind farm power change rate in real time. When the wind farm power change rate is lower than the preset electric field power change threshold, continue to notify the preset personnel to cut out abnormal wind turbines with a threshold number of wind turbines until all abnormal wind turbines are shut down or repaired.

[0039] Specifically, the priority estimation value of each abnormal wind turbine is obtained as follows: priority parameter reference data is obtained from a preset meteorological power database, specifically including: critical power generation contribution, critical average wind speed and critical power generation efficiency; the power generation contribution, critical average wind speed and power generation efficiency of each abnormal wind turbine in the previous monitoring time period are respectively subjected to a proportion approximation operation with the corresponding critical power generation contribution, average wind speed and critical power generation efficiency, and then the proportion approximation operation results are weighted and coupled using the priority parameter weight ratio to obtain the priority estimation value of each abnormal wind turbine. The priority parameter weight ratio includes the power generation contribution weight ratio, the average wind speed weight ratio and the power generation efficiency weight ratio.

[0040] Specifically, the calculation formula for the priority estimation value of each abnormal wind turbine is:

[0041] ;

[0042] Where, represents the priority value of the jth abnormal wind turbine, Indicates the weight ratio of power generation contribution, represents the average wind speed weight ratio, represents the power generation efficiency weight ratio, It represents the contribution of the j-th abnormal wind turbine to the power generation, which is the proportion of the power generation of the abnormal wind turbine to the total power generation of the wind farm during the last monitoring period. represents the critical power generation contribution, The average wind speed of the jth abnormal wind turbine can be obtained by directly measuring the wind speed of each monitoring point in the previous monitoring period using an anemometer and calculating the average value. represents the critical mean wind speed, It represents the power generation efficiency of the jth abnormal wind turbine, which is the ratio of the actual power generation to the theoretical available wind energy in the last monitoring period. represents the critical power generation efficiency, where j is the number of each abnormal wind turbine, j = 1, 2, 3, ..., M, and M is the total number of abnormal wind turbines.

[0043] 、 and The weighted ratios for power generation contribution, average wind speed, and power generation efficiency, preset in the meteorological and power generation database, represent the degree of influence of power generation contribution, average wind speed, and power generation efficiency on the estimated priority value. These values ​​can be directly retrieved from the meteorological and power generation database. These relationships are organized into a "mapping set," a lookup table or corresponding rules. When a priority parameter value is actually monitored, the corresponding value can be looked up in the corresponding mapping set to obtain the corresponding weighted ratio. This weighted ratio is a number between 0 and 1, representing the degree to which the current priority parameter interferes with the wind turbine's priority. For example, for power generation contribution, a mapping set exists between power generation contribution and its corresponding weighted ratio. Entering the power generation contribution value yields the corresponding weighted ratio. For average wind speed, a mapping set exists between average wind speed and its corresponding weighted ratio. Entering the average wind speed value yields the corresponding weighted ratio. For power generation efficiency, a mapping set exists between power generation efficiency and its corresponding weighted ratio. Entering the power generation efficiency value yields the corresponding weighted ratio. These mappings can be many-to-one or one-to-one.

[0044] In this embodiment, the power generation contribution, average wind speed and power generation efficiency are interrelated. For example, under the same wind speed conditions, the higher the power generation efficiency, the closer the actual power generation is to the theoretical maximum value, and the greater the power generation contribution. On the contrary, if the power generation efficiency of the wind turbine is low, then even if the wind speed is high, its power generation contribution may be limited; when the wind speed is lower than the cut-in wind speed, the wind turbine captures less wind energy, and the wind turbine's power generation efficiency is close to zero; when the wind speed is between the cut-in wind speed and the rated wind speed, the power generation efficiency increases with the increase of wind speed; and when the wind speed exceeds the rated wind speed, the wind turbine reaches the rated power limit, resulting in the power generation efficiency no longer increasing or even decreasing. The priority estimated value obtained by comprehensive analysis can be used to determine the order of cutting out each abnormal wind turbine, so that the abnormal wind turbine can be cut out to the greatest extent under the premise of stable operation of the wind farm, reducing the impact of abnormal wind turbines on the accuracy of power generation, and maximizing the overall power generation efficiency of the wind farm.

[0045] like Figure 4 The figure shows a mind map for quantitative determination of wind speed fluctuations provided by an embodiment of the present application. The method includes: real-time monitoring of the wind speed fluctuation parameters of each normal wind turbine in each monitoring time period to obtain a wind speed fluctuation assessment value for each normal wind turbine in each monitoring time period, and performing quantitative determination of wind speed fluctuations: comparing the wind speed fluctuation assessment value for each normal wind turbine in each monitoring time period with a wind speed fluctuation threshold value; if the wind speed fluctuation assessment value for a normal wind turbine in a certain monitoring time period is less than or equal to the wind speed fluctuation threshold value, the corresponding wind speed fluctuation quantitative determination result is recorded as qualified wind speed fluctuation, and no additional processing is performed; if the wind speed fluctuation assessment value for a normal wind turbine in a certain monitoring time period is greater than the wind speed fluctuation threshold value, the corresponding wind speed fluctuation quantitative determination result is recorded as unqualified wind speed fluctuation, and the power generation is corrected and the ensemble empirical mode decomposition method is optimized based on the wind speed fluctuation assessment value for the normal wind turbine in the monitoring time period.

[0046] Specifically, the steps of quantitatively determining the wind speed fluctuation according to the wind speed fluctuation parameters of each normal wind turbine in each monitoring time period in the wind farm include: obtaining wind speed fluctuation parameter reference data from a preset meteorological power database, specifically including: critical wind speed variation coefficient, critical wind speed variability and critical maximum wind speed fluctuation amplitude; performing a proportion approach calculation on the wind speed fluctuation parameters of each normal wind turbine in each monitoring time period and the corresponding wind speed fluctuation parameter reference data, and then using the wind speed fluctuation parameter weight ratio to perform weighted processing on the proportion approach calculation and then couple it to obtain the wind speed fluctuation assessment value of each normal wind turbine in each monitoring time period, and the wind speed fluctuation parameter weight ratio includes the wind speed variation coefficient weight ratio, the average wind speed variability weight ratio and the maximum wind speed fluctuation weight ratio. Amplitude weight ratio, wind speed fluctuation assessment value represents the quantitative data of the degree of influence of wind speed fluctuation parameters on the accuracy of power generation of normal wind turbines, wind speed fluctuation parameters include wind speed variation coefficient, average wind speed variability and maximum wind speed fluctuation amplitude; wind speed fluctuation threshold value is obtained from the preset meteorological power database; the wind speed fluctuation assessment value of each normal wind turbine in each monitoring time period is compared with the wind speed fluctuation threshold value respectively, if the wind speed fluctuation assessment value of a normal wind turbine in a certain monitoring time period is less than or equal to the wind speed fluctuation threshold value, the corresponding wind speed fluctuation quantitative judgment result is recorded as qualified wind speed fluctuation; if the wind speed fluctuation assessment value of a normal wind turbine in a certain monitoring time period is greater than the wind speed fluctuation threshold value, the corresponding wind speed fluctuation quantitative judgment result is recorded as unqualified wind speed fluctuation.

[0047] The wind speed fluctuation assessment value of each normal wind turbine in each monitoring time period is obtained as follows:

[0048] ;

[0049] Where, represents the wind speed fluctuation assessment value of the ath normal wind turbine in the kth monitoring period, represents the weight ratio of wind speed variation coefficient, represents the weight ratio of the average wind speed variability, Indicates the weight ratio of the maximum wind speed fluctuation amplitude, It represents the wind speed variation coefficient of the ath normal wind turbine in the kth monitoring period. The wind speed can be measured in real time using an anemometer. The ratio of the wind speed standard deviation to the average wind speed in the monitoring period is the wind speed variation coefficient. represents the critical wind speed variation coefficient, It represents the average wind speed variability of the ath normal wind turbine in the kth monitoring period. The absolute value of the wind speed difference between each adjacent monitoring point in the monitoring period is compared with the time interval, and then the calculated results are averaged. represents the critical mean wind speed variability, It represents the maximum wind speed fluctuation amplitude of the ath normal wind turbine in the kth monitoring period, which is the difference between the maximum wind speed and the minimum wind speed in the monitoring period. represents the critical maximum wind speed fluctuation amplitude, where k is the number of each monitoring time period, k = 1, 2, 3, ..., L, L is the total number of monitoring time periods, a is the number of each normal wind turbine, a = 1, 2, 3, ..., A, A is the total number of normal wind turbines.

[0050] 、 and These are the weight ratios corresponding to the wind speed variation coefficient, average wind speed variability, and maximum wind speed fluctuation amplitude preset in the meteorological and power database, respectively, representing the numerical values ​​of the degree of influence of the wind speed variation coefficient, average wind speed variability, and maximum wind speed fluctuation amplitude on the wind speed fluctuation assessment value, which can be directly obtained from the meteorological and power database when used. These relationships are organized into a "mapping set," that is, a lookup table or corresponding rules. When the value of a certain wind speed fluctuation parameter is actually monitored, this value can be looked up in the corresponding mapping set, and then the corresponding weight ratio can be obtained. This weight ratio is a number between 0 and 1, which represents the evaluation result of the current wind speed fluctuation parameter on the power generation and the accuracy of the ensemble empirical mode decomposition method. For example, for the wind speed variation coefficient, there is a mapping set of the wind speed variation coefficient and the corresponding weight ratio of the wind speed variation coefficient. By inputting the value of the wind speed variation coefficient, the corresponding weight ratio of the wind speed variation coefficient can be obtained. For the average wind speed variability, there is a mapping set of the average wind speed variability and the corresponding weight ratio of the average wind speed variability. By inputting the value of the average wind speed variability, the corresponding weight ratio of the average wind speed variability can be obtained. For the maximum wind speed fluctuation amplitude, there is a mapping set of the maximum wind speed fluctuation amplitude and the corresponding weight ratio of the maximum wind speed fluctuation amplitude. By inputting the value of the maximum wind speed fluctuation amplitude, the corresponding weight ratio of the maximum wind speed fluctuation amplitude can be obtained. These mapping relationships can be many-to-one or one-to-one.

[0051] In this embodiment, the wind speed variation coefficient, the average wind speed variability and the maximum wind speed fluctuation amplitude all reflect the intensity and amplitude of wind speed fluctuations. The wind speed variation coefficient and the wind speed variability can measure the instantaneous fluctuation degree of wind speed, while the maximum wind speed fluctuation amplitude reflects the maximum variation range of wind speed. The three are interrelated. For example, the higher the wind speed variation coefficient, the larger the wind speed standard deviation, which means that the wind speed is more likely to change significantly per unit time, and the average wind speed variability may be higher. At the same time, a high wind speed variation coefficient indicates that the wind speed distribution is dispersed, and the probability of extreme conditions such as gusts and calm winds is higher, which may be accompanied by a larger wind speed fluctuation amplitude. If the maximum wind speed fluctuation amplitude is larger in a short period of time, the wind speed variability per unit time will inevitably increase. If the wind speed repeatedly crosses a large range for a long time, it means that the wind speed fluctuation range is wide, and the average wind speed variability will also be larger. The wind speed fluctuation assessment value obtained through comprehensive analysis reflects the stability and efficiency of the wind turbine under different wind speed conditions, and can help identify the impact of wind speed fluctuations on the accuracy of wind turbine power generation. Dynamic correction of power generation and optimization of the ensemble empirical mode decomposition method based on the wind speed fluctuation assessment value can improve the accuracy of power generation and enhance the accuracy of the ensemble empirical mode decomposition method in analyzing high-impact meteorological-power event modeling data.

[0052] Furthermore, the steps of determining whether to perform dynamic correction of power generation and optimization of the ensemble empirical mode decomposition method based on the quantitative judgment result of wind speed fluctuation include: if the quantitative judgment result of wind speed fluctuation of a normal wind turbine in a certain monitoring time period is that the wind speed fluctuation is qualified, no additional processing is performed; if the quantitative judgment result of wind speed fluctuation of a normal wind turbine in a certain monitoring time period is that the wind speed fluctuation is unqualified, an abnormal fluctuation prompt is issued, and the difference between the wind speed fluctuation assessment value of the normal wind turbine in the monitoring time period and the wind speed fluctuation threshold is recorded as the wind speed fluctuation deviation value; the white noise data is dynamically corrected according to the wind speed fluctuation deviation value, and the white noise data includes the white noise standard deviation of high-frequency data and the white noise standard deviation of low-frequency data; the wind speed fluctuation deviation value is matched with the power generation correction value corresponding to each wind speed fluctuation deviation value interval preset in the meteorological power database to obtain the power generation correction value, and the sum of the power generation correction value and the power generation of the wind turbine is the corrected power generation.

[0053] Among them, the step of dynamically correcting white noise data according to the wind speed fluctuation deviation value includes: matching the wind speed fluctuation deviation value with the proportional factor data corresponding to each wind speed fluctuation deviation value interval preset in the meteorological and power database to obtain the corresponding proportional factor data, and the proportional factor data includes a high-frequency proportional factor and a low-frequency proportional factor; obtaining the initial white noise standard deviation and the white noise standard deviation multiple range from the preset meteorological and power database, and the white noise standard deviation multiple range includes a high-frequency multiple range and a low-frequency multiple range; obtaining the white noise standard deviation of the corrected high-frequency data according to the initial white noise standard deviation, the high-frequency multiple range, the high-frequency proportional factor and the wind speed fluctuation deviation value; obtaining the white noise standard deviation of the corrected low-frequency data according to the initial white noise standard deviation, the low-frequency multiple range, the low-frequency proportional factor and the wind speed fluctuation deviation value.

[0054] In this embodiment, the white noise standard deviation of the corrected high-frequency data and the white noise standard deviation of the corrected low-frequency data are obtained as follows: the high-frequency multiple adjustment value and the low-frequency multiple adjustment value are obtained by multiplying the high-frequency multiple adjustment value and the low-frequency multiple adjustment value by the product of the wind speed fluctuation deviation value, and then the high-frequency multiple adjustment value and the low-frequency multiple adjustment value are multiplied by the sum of the corresponding high-frequency multiple range minimum value and the low-frequency multiple range minimum value and the initial white noise standard deviation to obtain the white noise standard deviation of the corrected high-frequency data and the white noise standard deviation of the corrected low-frequency data. The present invention dynamically matches the power generation correction value based on the wind speed fluctuation deviation value, effectively reduces the interference of abnormal wind speed on the power generation data, ensures the accuracy of the output power, and not only reduces the negative impact of abnormal wind speed fluctuations on high-impact meteorological-power event modeling by dynamically correcting the power generation and optimizing the set empirical mode decomposition parameters, but also provides a high-quality and highly consistent data foundation for subsequent AI-driven LLM Agent analysis.

[0055] like Figure 5As shown, it is a structural diagram of the analysis system based on high-impact meteorological-electricity event modeling data provided by an embodiment of the present application. The analysis system based on high-impact meteorological-electricity event modeling data provided by an embodiment of the present application includes: a wind turbine processing module, a power generation correction module, a data analysis module and a meteorological-electricity database; wherein the wind turbine processing module is used to perform quantitative determination of the wind turbine state according to the wind turbine state parameters of each wind turbine in the wind farm, obtain the wind turbine state quantitative determination result, determine whether to perform wind turbine dynamic processing based on the wind turbine state quantitative determination result, and obtain each normal wind turbine based on the wind turbine dynamic processing result; the power generation correction module is used to perform quantitative determination of wind speed fluctuation according to the wind speed fluctuation parameters of each normal wind turbine in each monitoring time period in the wind farm, obtain the wind speed fluctuation quantitative determination result, determine whether to perform power generation dynamic correction and set empirical mode decomposition method optimization based on the wind speed fluctuation quantitative determination result; the data analysis module is used to perform meteorological-electricity data analysis using the optimized set empirical mode decomposition method, and the meteorological-electricity data analysis represents the use of AI-driven LLM The Agent large model performs parametric modeling and analysis on the wind power generation data processed by the optimized ensemble empirical mode decomposition method, and constructs a multi-dimensional coupling indicator system.

[0056] In summary, the embodiment of the present application first performs a quantitative judgment based on the operating status parameters of each wind turbine in the wind farm, generates a wind turbine status level, and performs dynamic processing on the wind turbine according to the judgment result, and screens out normal wind turbines to eliminate the interference of low-quality data on modeling; then, the wind speed fluctuation parameters of normal wind turbines in each monitoring time period are quantitatively evaluated. If the fluctuation exceeds the threshold, the dynamic correction of power generation and the optimization of the ensemble empirical mode decomposition method are triggered, thereby reducing the impact of wind speed anomalies on data stability; finally, the optimized ensemble empirical mode decomposition method is used to decompose the meteorological-power coupling data, and the AI-driven LLM Agent is used to perform parameterized feature extraction and multi-dimensional analysis of the IMF components to construct a coupling indicator system covering meteorological sensitivity, equipment status and power grid response capability, thereby achieving improved accuracy in data analysis of high-impact meteorological-power event modeling.

[0057] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Thus, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0058] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowcharts and / or block diagrams, as well as combinations of processes and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowcharts and / or block diagrams. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0059] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0060] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0061] Although the preferred embodiments of the present invention have been described, those skilled in the art may make additional changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present invention.

[0062] Obviously, those skilled in the art may make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if such changes and modifications fall within the scope of the claims and their equivalents, the present invention is intended to include such changes and modifications.

Claims

1. An analysis method based on high-impact meteorological-electricity event modeling data, characterized in that: The following steps are involved: Quantitatively determine the status of each wind turbine in the wind farm based on its status parameters to obtain a quantitative determination result of the status of the wind turbine. Based on the determination result, determine whether to perform dynamic processing of the wind turbines, and obtain the status of each normal wind turbine based on the processing result. Dynamic processing of the wind turbines is to process the wind turbines based on the determination result to reduce the impact of wind turbine efficiency on the accuracy of data analysis of high-impact meteorological-power event modeling; A quantitative determination of wind speed fluctuations is performed based on the wind speed fluctuation parameters of each normal wind turbine in each monitoring time period in the wind farm, and a quantitative determination result of wind speed fluctuations is obtained. Based on the determination result, it is determined whether to perform dynamic correction of power generation and optimization of the ensemble empirical mode decomposition method. The dynamic correction of power generation and optimization of the ensemble empirical mode decomposition method are to correct the power generation of normal wind turbines and optimize the white noise standard deviation in the ensemble empirical mode decomposition method based on the quantitative determination result of wind speed fluctuations, so as to reduce the impact of abnormal wind speed fluctuations on the accuracy of data analysis of high-impact meteorological-power event modeling; The quantitative determination of wind speed fluctuation includes: obtaining wind speed fluctuation parameter reference data from a preset meteorological and power database: critical wind speed variation coefficient, critical wind speed variability and critical maximum wind speed fluctuation amplitude; performing a proportion approach calculation on the wind speed fluctuation parameters of each normal wind turbine in each monitoring time period and the corresponding wind speed fluctuation parameter reference data, and then using the wind speed fluctuation parameter weight ratio to perform weighted processing on the proportion approach calculation and then couple it to obtain the wind speed fluctuation assessment value of each normal wind turbine in each monitoring time period, the wind speed fluctuation parameters include wind speed variation coefficient, average wind speed variability and maximum wind speed fluctuation amplitude; obtaining the wind speed fluctuation threshold value from a preset meteorological and power database; performing a proportion approach calculation on the wind speed fluctuation parameters of each normal wind turbine in each monitoring time period and the corresponding wind speed fluctuation parameter reference data; and then performing a weighted processing on the proportion approach calculation and then coupling the weighted ratio of the wind speed fluctuation parameters to obtain the wind speed fluctuation assessment value of each normal wind turbine in each monitoring time period. The wind speed fluctuation parameters include wind speed variation coefficient, average wind speed variability and maximum wind speed fluctuation amplitude; and The wind speed fluctuation assessment value of each normal wind turbine in the monitoring period is compared with the wind speed fluctuation threshold value. If it is greater than the wind speed fluctuation threshold value, it is recorded as unqualified wind speed fluctuation, and an abnormal fluctuation prompt is issued. The difference between the wind speed fluctuation assessment value of the normal wind turbine in the monitoring period and the wind speed fluctuation threshold value is recorded as the wind speed fluctuation deviation value; the white noise data is dynamically corrected according to the wind speed fluctuation deviation value, and the white noise data includes the white noise standard deviation of the high-frequency data and the white noise standard deviation of the low-frequency data; the wind speed fluctuation deviation value is matched with the power generation correction value corresponding to each wind speed fluctuation deviation value interval preset in the meteorological power database to obtain the power generation correction value, and the power generation of the wind turbine is corrected according to the power generation correction value; The optimized ensemble empirical mode decomposition method is used to decompose meteorological-power data. The AI-driven LLM Agent large model is used to perform parametric feature extraction and multi-dimensional analysis on the wind power generation data processed by the optimized ensemble empirical mode decomposition method, and to construct a coupling indicator system covering meteorological sensitivity, equipment status and grid response capability.

2. The analysis method based on high-impact meteorological-electricity event modeling data according to claim 1, characterized in that: The step of quantitatively determining the status of each wind turbine in the wind farm according to the status parameters of the wind turbines includes: Obtain wind turbine status parameter reference data from a preset meteorological and power database, including: critical wind energy utilization rate, critical wind speed variation coefficient, and critical active power fluctuation rate; The wind energy utilization rate, wind speed variation coefficient and critical active power fluctuation rate of each wind turbine are respectively subjected to a proportion approaching degree calculation with the corresponding critical wind energy utilization rate, critical wind speed variation coefficient and active power fluctuation rate, and then the proportion approaching degree calculation results are weighted and coupled using the weight ratio of the wind turbine state parameters to obtain a wind turbine state assessment value of each wind turbine, wherein the weight ratio of the wind turbine state parameters includes the weight ratio of the wind energy utilization rate, the weight ratio of the wind speed variation coefficient and the weight ratio of the active power fluctuation rate, and the wind turbine state assessment value represents quantitative data of the degree of influence of the wind turbine state parameters on the accuracy of the wind turbine data, and the wind turbine state parameters include the wind energy utilization rate, the wind speed variation coefficient and the active power fluctuation rate; Obtaining a first threshold value of a wind turbine state and a second threshold value of a wind turbine state from a preset meteorological and power database; The fan status assessment value of each fan is compared with the first fan status threshold and the second fan status threshold respectively. If the fan status assessment value of a fan is less than the first fan status threshold, the corresponding fan status quantitative determination result is recorded as unqualified fan status; If the fan status assessment value of a certain fan is greater than or equal to the first fan status threshold and less than the second fan status threshold, the corresponding fan status quantitative determination result is recorded as the fan status to be optimized; If the fan status assessment value of a certain fan is greater than or equal to the second fan status threshold, the corresponding fan status quantitative determination result is recorded as the fan status being qualified.

3. The analysis method based on high-impact meteorological-electricity event modeling data according to claim 2, characterized in that: The step of determining whether to perform fan dynamic processing based on the determination result and obtaining each normal fan according to the processing result includes: If the fan status quantification determination result of a certain fan is that the fan status is qualified, no additional processing is performed; If the quantitative determination result of the wind turbine state of a certain wind turbine is that the wind turbine state needs to be optimized, the pitch angle and yaw angle are dynamically processed according to the wind turbine state evaluation value and wind speed of the wind turbine; If the fan status quantification result of a fan is unqualified, the fan will be marked as abnormal and a fan abnormality prompt will be issued; Determine whether the number of fans whose fan status is unqualified according to the fan status quantitative determination result exceeds the preset fan number threshold. If so, prompt the preset personnel to cut off the abnormal fans in batches according to the priority parameters of each abnormal fan. Otherwise, directly prompt the preset personnel to cut off the abnormal fans. The fans that are not recorded as abnormal fans are recorded as normal fans.

4. The analysis method based on high-impact meteorological-electricity event modeling data according to claim 3, characterized in that: The step of dynamically processing the pitch angle and the yaw angle according to the wind turbine state evaluation value and the wind speed of the wind turbine comprises: When the wind speed of the wind turbine is greater than the maximum value of the preset standard wind speed range and the wind turbine state assessment value of the current monitoring time period is greater than or equal to the wind turbine state assessment value of the previous monitoring time period, the pitch angle of the wind turbine is gradually increased according to the obtained pitch angle adjustment value until the wind turbine state change value of the next monitoring time period exceeds the preset wind turbine state assessment value change threshold value, the pitch angle adjustment value is obtained by matching the current wind turbine state deviation value with the pitch angle adjustment value corresponding to each wind turbine state deviation range preset in the meteorological and power database, the current wind turbine state deviation value represents the difference between the wind turbine state assessment value of the current monitoring time period and the wind turbine state assessment value of the previous monitoring time period; When the wind speed of the wind turbine is greater than the maximum value of the preset standard wind speed range and the wind turbine status assessment value of the current monitoring time period is less than the wind turbine status assessment value of the previous monitoring time period, the pitch angle of the wind turbine is gradually reduced according to the obtained pitch angle adjustment value until the wind turbine status change value of the next monitoring time period exceeds the preset wind turbine status assessment value change threshold; When the wind speed of the wind turbine is less than the minimum value of the preset standard wind speed range and the wind turbine status assessment value of the current monitoring time period is greater than or equal to the wind turbine status assessment value of the previous monitoring time period, the pitch angle of the wind turbine is gradually increased according to the obtained pitch angle adjustment value until the wind turbine status change value of the next monitoring time period exceeds the preset wind turbine status assessment value change threshold; When the wind speed of the wind turbine is less than the minimum value of the preset standard wind speed range and the wind turbine status assessment value of the current monitoring time period is less than the wind turbine status assessment value of the previous monitoring time period, the pitch angle of the wind turbine is gradually reduced according to the obtained pitch angle adjustment value until the wind turbine status change value of the next monitoring time period exceeds the preset wind turbine status assessment value change threshold; When the wind speed of the wind turbine is within a preset standard wind speed range, the yaw angle is dynamically adjusted according to the wind direction sensor data of the wind turbine.

5. The analysis method based on high-impact meteorological-electricity event modeling data according to claim 3, characterized in that: The step of prompting preset personnel to cut out abnormal fans in batches according to the priority parameters of each abnormal fan includes: Obtaining a priority estimate value for each abnormal wind turbine based on priority parameters of each abnormal wind turbine, the priority estimate value representing quantitative data on the degree of influence of the priority parameters on the wind turbine operation priority, the priority parameters including power generation contribution, average wind speed, and power generation efficiency during the previous monitoring period; Sort the abnormal fans in ascending order according to the estimated priority values, and notify the preset personnel to cut out the abnormal fans according to the sorting order.

6. The analysis method based on high-impact meteorological-electricity event modeling data according to claim 5, characterized in that: The priority estimation value of each abnormal wind turbine is obtained as follows: Obtaining priority parameter reference data from a preset meteorological and power database, specifically including: critical power generation contribution, critical average wind speed, and critical power generation efficiency; The power generation contribution, critical average wind speed and power generation efficiency of each abnormal wind turbine in the previous monitoring time period are respectively subjected to a proportion approach calculation with the corresponding critical power generation contribution, average wind speed and critical power generation efficiency. The proportion approach calculation results are then weighted and coupled using the priority parameter weight ratio to obtain the priority estimated value of each abnormal wind turbine. The priority parameter weight ratio includes the power generation contribution weight ratio, the average wind speed weight ratio and the power generation efficiency weight ratio.

7. The analysis method based on high-impact meteorological-electricity event modeling data according to claim 1, characterized in that: The step of quantitatively determining the wind speed fluctuation according to the wind speed fluctuation parameters of each normal wind turbine in each monitoring time period in the wind farm includes: The wind speed fluctuation parameter weight ratio includes the wind speed variation coefficient weight ratio, the average wind speed variability weight ratio and the maximum wind speed fluctuation amplitude weight ratio, and the wind speed fluctuation assessment value represents the quantitative data of the degree of influence of the wind speed fluctuation parameters on the accuracy of power generation of a normal wind turbine; Among them, if the wind speed fluctuation assessment value of a normal wind turbine in a certain monitoring time period is less than or equal to the wind speed fluctuation threshold, the corresponding wind speed fluctuation quantitative judgment result is recorded as the wind speed fluctuation is qualified.

8. The analysis method based on high-impact meteorological-electricity event modeling data according to claim 1, characterized in that: The step of determining whether to perform dynamic power generation correction and ensemble empirical mode decomposition method optimization based on the determination result includes: If the wind speed fluctuation quantification determination result of a normal wind turbine in a certain monitoring period is that the wind speed fluctuation is qualified, no additional processing is performed.

9. The analysis method based on high-impact meteorological-electricity event modeling data according to claim 1, characterized in that: The step of dynamically correcting white noise data according to the wind speed fluctuation deviation value includes: Matching the wind speed fluctuation deviation value with the proportional factor data corresponding to each wind speed fluctuation deviation value interval preset in the meteorological and power database to obtain corresponding proportional factor data, wherein the proportional factor data includes a high-frequency proportional factor and a low-frequency proportional factor; Obtaining an initial white noise standard deviation and a white noise standard deviation multiple range from a preset meteorological and power database, wherein the white noise standard deviation multiple range includes a high frequency multiple range and a low frequency multiple range; The white noise standard deviation of the corrected high-frequency data is obtained according to the initial white noise standard deviation, high-frequency multiple range, high-frequency scale factor and wind speed fluctuation deviation value; The white noise standard deviation of the corrected low-frequency data is obtained based on the initial white noise standard deviation, low-frequency multiple range, low-frequency scale factor and wind speed fluctuation deviation.

10. A system using the analysis method based on high-impact meteorological-electricity event modeling data according to any one of claims 1 to 9, characterized in that: It includes wind turbine processing module, power generation correction module, data analysis module and meteorological power database; The wind turbine processing module is configured to perform a quantitative determination of the wind turbine status based on the wind turbine status parameters of each wind turbine in the wind farm, obtain a quantitative determination result of the wind turbine status, determine whether to perform wind turbine dynamic processing based on the quantitative determination result of the wind turbine status, and obtain each normal wind turbine according to the wind turbine dynamic processing result; The power generation correction module is used to perform a quantitative determination of wind speed fluctuations based on the wind speed fluctuation parameters of each normal wind turbine in each monitoring time period in the wind farm, obtain a quantitative determination result of the wind speed fluctuation, and determine whether to perform dynamic power generation correction and ensemble empirical mode decomposition method optimization based on the quantitative determination result of the wind speed fluctuation; The data analysis module is used to decompose meteorological and power data using the optimized ensemble empirical mode decomposition method, and perform parameterized feature extraction and multi-dimensional analysis on the wind power generation data processed by the optimized ensemble empirical mode decomposition method through the AI-driven LLM Agent large model, to build a coupling indicator system covering meteorological sensitivity, equipment status and grid response capability.

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