A wind farm wind turbine energy management platform and method

Abstract

The present invention discloses an energy management platform and method for wind turbines in wind farms, and relates to the field of wind power generation technology, including data acquisition and monitoring systems, wind resource assessment and prediction, energy optimization and fault diagnosis, economic analysis and user interface, safety protection and system integration. The present invention, through the implementation of the data acquisition and monitoring system, makes the collection and monitoring of real-time data more efficient and accurate, enhances the quality of data and management efficiency, and improves the overall operation level of the wind farm. In terms of wind resource assessment and optimization, accurate wind speed and wind direction prediction can be achieved by combining historical data and real-time data. These prediction results provide energy optimization and fault diagnosis functions. The operating status of the wind turbine can be optimized according to the prediction results and grid demand, thereby maximizing the power generation and minimizing the cost. By reducing the downtime of the wind turbine, the economic benefits and operational efficiency of the wind farm are further improved.

Description

Technical Field

[0001] The present invention relates to the technical field of wind power generation, and in particular to an energy management platform and method for wind turbines in a wind farm. Background Art

[0002] The wind turbine energy management platform is the core component of wind farm operation. It is responsible for collecting, analyzing and processing wind turbine operating data to optimize the performance and efficiency of wind turbines. The energy management of wind turbines mainly relies on the experience of on-site operators and simple control strategies. With the advancement of computer technology, wind farms have begun to introduce automated control systems. These systems can monitor the operating status of wind turbines in real time and adjust the operating parameters of wind turbines according to environmental factors such as wind speed and wind direction to improve power generation efficiency.

[0003] The existing technology has the following problems:

[0004] Current wind farm wind turbine energy management platforms and methods often lack sufficient flexibility and adaptability, and are unable to cope with rapid changes in wind speed and direction. Secondly, these systems may not be able to efficiently integrate data from different wind turbines, resulting in insufficient resource utilization. In addition, traditional platforms may not have advanced forecasting capabilities and cannot accurately predict fluctuations in wind energy resources, thereby affecting power generation efficiency and grid stability. Summary of the invention

[0005] The present invention provides an energy management platform and method for wind turbines in a wind farm to solve the problems raised in the above background technology.

[0006] In order to solve the above technical problems, the technical solution adopted by the present invention is:

[0007] An energy management platform for wind turbines in wind farms, including data acquisition and monitoring systems, wind resource assessment and prediction, energy optimization and fault diagnosis, economic analysis and user interface, safety protection and system integration;

[0008] The data acquisition and monitoring system is responsible for collecting the operating data of the wind turbine in real time, including wind speed, wind direction, power generation, temperature and other information;

[0009] The wind resource assessment and prediction can analyze wind speed and wind direction data, evaluate the availability and quality of wind resources, and predict the power generation of wind turbines in the future based on historical and real-time data, providing a basis for scheduling and optimization;

[0010] The energy optimization and fault diagnosis can optimize the operation status of the wind turbine according to the prediction results and the grid demand, maximize the power generation and minimize the cost. At the same time, the operation status of the wind turbine can be monitored in real time, potential faults can be discovered in time and maintenance can be arranged to reduce downtime.

[0011] The economic analysis and user interface can evaluate the economic performance of the wind farm, including cost analysis, revenue forecast, return on investment, etc., and provide an intuitive user interface so that operators can easily monitor the operating status of the wind farm, conduct data analysis and management decisions;

[0012] The safety protection and system integration can ensure the safety of the wind farm under various operating conditions, including overload protection, short circuit protection, lightning protection, etc., ensure stable communication within the wind farm and with external systems, realize rapid data transmission and remote control, ensure that the platform is compatible with existing wind farm management systems and equipment, and support the integration of future technologies.

[0013] A method for managing energy of wind turbines in a wind farm, the method comprising the following steps:

[0014] Step 1: Data collection;

[0015] Step 2: Predictive analysis;

[0016] Step 3: Energy optimization;

[0017] Step 4: Load balancing and fault diagnosis;

[0018] Step 5: Energy storage;

[0019] Step 6: Control execution, monitoring and feedback

[0020] A further improvement of the technical solution of the present invention is that the step 1 further includes the following steps:

[0021] A1: According to the operation requirements of the wind farm and the characteristics of environmental changes, set a reasonable data collection frequency and deploy various sensors to ensure that the monitoring area of ​​the wind farm can be fully covered;

[0022] A2: The data collected by the sensor is transmitted to the central data processing system through wired or wireless communication to ensure the stability and real-time performance of data transmission for subsequent analysis and processing. The collected raw data is pre-processed, including data cleaning, format conversion, and outlier processing, to ensure data quality.

[0023] A3: Store the preprocessed data in the database to facilitate subsequent query, analysis and prediction, ensure that all relevant systems and equipment in the wind farm can synchronize data in real time, so that the energy management system of the entire wind farm can work together, conduct preliminary analysis of the collected data to evaluate the operating status and environmental conditions of the wind farm, and provide basic data support for subsequent energy management.

[0024] A further improvement of the technical solution of the present invention is that the step 2 further includes the following steps:

[0025] B1: First, clean and format the collected real-time and historical data to ensure data quality. This may include removing outliers, filling in missing data, and data standardization;

[0026] B2: Select features from the preprocessed data that are helpful for wind speed and direction prediction, which may include historical wind speed, wind direction data, meteorological factors such as temperature, humidity, air pressure, and time series features;

[0027] B3: According to the specific conditions of the wind farm and the characteristics of historical data, select appropriate meteorological forecast models and machine learning algorithms. Common models include time series analysis models (such as ARI MA) and machine learning models (such as random forest, support vector machine, neural network, etc.);

[0028] B4: Use historical data sets to train the selected model. During the training process, parameter tuning may be required to obtain the best prediction performance. Use a portion of the data set that was not involved in the training to verify the prediction accuracy of the model. By comparing the prediction results with the actual data, the performance of the model can be evaluated.

[0029] B4: Input real-time data into the trained model to predict the changing trend of wind speed and wind direction in the future. The prediction results usually include the numerical value and probability distribution of wind speed and wind direction in the next few hours or days. Analyze the prediction results to determine the possible impact of wind speed and wind direction changes on the power generation of the wind farm. This step may require combining the power curve and efficiency characteristics of the wind turbine to evaluate the power generation capacity of the wind turbine under different wind speeds and wind directions.

[0030] A further improvement of the technical solution of the present invention is that the step three comprises the following steps:

[0031] C1: Detailed analysis of the collected real-time environmental data such as wind speed, wind direction, temperature, humidity, and the operating status and power generation of wind turbines to understand the current operating status and environmental conditions of the wind farm;

[0032] C2: Based on the power curve of the wind turbine, combined with real-time wind speed data, calculate the maximum theoretical power generation of the wind turbine under the current wind speed, analyze the efficiency characteristics of the wind turbine, determine the optimal operating efficiency range of the wind turbine under different wind speeds and wind directions, and calculate the optimal wind turbine operating parameters, such as blade angle, speed, etc., to ensure that the wind turbine achieves the best power generation efficiency under the current environmental conditions;

[0033] C3: Based on the overall power generation of the wind farm and the needs of the power grid, formulate an operation strategy for the wind turbines to achieve smoothing of the wind farm output power and balance of the power grid load. Use simulation software to verify the calculated operating parameters to ensure that these parameters can achieve the expected optimization effect in actual operation. Based on the simulation results and real-time monitoring data, fine-tune the operating parameters of the wind turbines to adapt to environmental changes and real-time changes in power grid demand.

[0034] A further improvement of the technical solution of the present invention is that the step 4 further includes the following steps:

[0035] D1: The overall power generation of the wind farm and the demand of the power grid. By adjusting the operating status of each wind turbine, the wind farm output power can be smoothed and the power grid load can be balanced.

[0036] D2: Monitor the operating status of wind turbines in real time, identify potential failures and performance degradation through data analysis, and perform maintenance and adjustments in a timely manner.

[0037] A further improvement of the technical solution of the present invention is that the step five comprises the following steps:

[0038] E1: In the comprehensive planning and design phase, the selection and design of energy storage systems are combined, and cost, storage requirements and wind resource volatility are considered. Economical and efficient energy storage solutions are selected and designed, and the type of energy storage technology, system capacity, power level, configuration and control strategy are determined to meet the specific needs of the wind farm.

[0039] E2: The implementation and integration phase involves the deployment and monitoring of energy storage systems. After the energy storage equipment is installed in the wind farm, it is connected to the wind turbines and the grid. The real-time monitoring system tracks the equipment's operating status, including key parameters such as battery charging and discharging, temperature, and voltage, to ensure the system is safe and efficient, and to handle problems in a timely manner;

[0040] E3: The continuous optimization and maintenance phase involves the continuous adjustment of the energy storage system control strategy and the optimization of operating parameters, with the goal of improving energy storage efficiency and wind farm performance, and regular inspections and maintenance, such as battery health assessments and cooling system inspections, to prevent failures and extend system life. This ensures that the energy storage system can stably support wind farm operations in the long term.

[0041] A further improvement of the technical solution of the present invention is that the step six further includes the following steps:

[0042] F1: Send the optimized operating parameters and instructions to the control system of the wind turbine to execute the energy management strategy to ensure efficient and stable operation of the wind turbine;

[0043] F2: Monitor the operating status of wind farms and the response of the power grid in real time, collect feedback information, and continuously adjust and optimize energy management strategies.

[0044] Due to the adoption of the above technical solution, the present invention has the following technical advances compared with the prior art:

[0045] 1. The present invention provides an energy management platform and method for wind turbines in wind farms. The implementation of the data acquisition and monitoring system makes the collection and monitoring of real-time data more efficient and accurate. Through this system, the accuracy of data collection and analysis is significantly improved, thereby greatly enhancing the quality of data and the efficiency of management, which not only helps to improve the overall operation level of wind farms, but also provides more reliable data support for decision makers.

[0046] 2. The present invention provides an energy management platform and method for wind turbines in wind farms. In terms of wind resource assessment and optimization, by combining historical data and real-time data, more accurate wind speed and wind direction forecasts can be made. These forecast results provide a solid foundation for energy optimization and fault diagnosis functions. Based on these functions, the operating status of wind turbines can be optimized according to the forecast results and grid demand, thereby maximizing power generation and minimizing costs. In addition, by reducing the downtime of wind turbines, the economic benefits and operational efficiency of wind farms are further improved.

[0047] 3. The present invention provides an energy management platform and method for wind turbines in wind farms. In terms of user interface and system safety, the introduction of economic analysis and user interface functions provides operators with a more intuitive and convenient user interface, which not only facilitates monitoring and management, but also improves work efficiency. At the same time, the implementation of safety protection and system integration functions ensures the safe and stable operation of wind farms. These functions support the compatibility of the system and further improve the operating efficiency and economic benefits of wind farms. Through these measures, we ensure the safety and reliability of the system, providing a solid guarantee for the long-term stable operation of wind farms. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] Figure 1 This is a schematic diagram of the framework of the wind farm wind turbine energy management platform of the present invention;

[0049] Figure 2 This is a flow chart of the energy management method for wind turbines in a wind farm according to the present invention. DETAILED DESCRIPTION

[0050] The present invention is further described in detail below in conjunction with embodiments:

[0051] Example 1

[0052] like Figure 1-2 As shown, the present invention provides an energy management platform and method for wind turbines in wind farms, including data acquisition and monitoring systems, wind resource assessment and prediction, energy optimization and fault diagnosis, economic analysis and user interface, safety protection and system integration;

[0053] The data acquisition and monitoring system is responsible for collecting the operating data of wind turbines in real time, including wind speed, wind direction, power generation, temperature and other information;

[0054] Wind resource assessment and forecasting can analyze wind speed and direction data, evaluate the availability and quality of wind resources, and predict the power generation of wind turbines in the future based on historical and real-time data, providing a basis for scheduling and optimization;

[0055] Energy optimization and fault diagnosis can optimize the operating status of wind turbines according to the prediction results and grid demand, maximize power generation and minimize costs. At the same time, it can monitor the operating status of wind turbines in real time, detect potential faults in time and arrange maintenance to reduce downtime.

[0056] Economic analysis and user interface can evaluate the economic performance of wind farms, including cost analysis, revenue forecast, return on investment, etc. It provides an intuitive user interface so that operators can easily monitor the operating status of wind farms, conduct data analysis and management decisions;

[0057] Safety protection and system integration can ensure the safety of wind farms under various operating conditions, including overload protection, short circuit protection, lightning protection, etc., ensure stable communication within the wind farm and with external systems, achieve rapid data transmission and remote control, ensure that the platform is compatible with existing wind farm management systems and equipment, and support the integration of future technologies.

[0058] In this embodiment, the data acquisition and monitoring system collects wind turbine operating data in real time, such as wind speed, wind direction, power generation and temperature. The wind resource assessment and prediction module analyzes wind speed and direction data, assesses resource availability, and predicts power generation to guide scheduling optimization. The energy optimization and fault diagnosis module optimizes the operating status of wind turbines to maximize power generation and minimize costs, while monitoring potential faults. The economic analysis and user interface module evaluates the economic performance of the wind farm and provides a user-friendly interface to support operation monitoring and management decisions. The safety protection and system integration module ensures the safe operation of the wind farm, including a variety of protection measures, and supports system compatibility and technical integration.

[0059] Example 2

[0060] like Figure 1-2 As shown, based on Example 1, the present invention provides a technical solution: Preferably, the energy management method for wind turbines in a wind farm comprises the following steps:

[0061] Step 1: Data collection;

[0062] Step 2: Predictive analysis;

[0063] Step 3: Energy optimization;

[0064] Step 4: Load balancing and fault diagnosis;

[0065] Step 5: Energy storage;

[0066] Step 6: Control execution, monitoring and feedback.

[0067] Step one also includes the following steps:

[0068] A1: According to the operation requirements of the wind farm and the characteristics of environmental changes, set a reasonable data collection frequency and deploy various sensors to ensure that the monitoring area of ​​the wind farm can be fully covered;

[0069] A2: The data collected by the sensor is transmitted to the central data processing system through wired or wireless communication to ensure the stability and real-time performance of data transmission for subsequent analysis and processing. The collected raw data is pre-processed, including data cleaning, format conversion, and outlier processing, to ensure data quality.

[0070] A3: Store the preprocessed data in the database to facilitate subsequent query, analysis and prediction, ensure that all relevant systems and equipment in the wind farm can synchronize data in real time, so that the energy management system of the entire wind farm can work together, conduct preliminary analysis of the collected data to evaluate the operating status and environmental conditions of the wind farm, and provide basic data support for subsequent energy management.

[0071] Step 2 also includes the following steps:

[0072] B1: First, clean and format the collected real-time and historical data to ensure data quality, which may include removing outliers, filling missing data, and data standardization;

[0073] B2: Select features from the preprocessed data that are helpful for wind speed and direction prediction, which may include historical wind speed, wind direction data, meteorological factors such as temperature, humidity, air pressure, and time series features;

[0074] B3: According to the specific conditions of the wind farm and the characteristics of historical data, select appropriate meteorological forecast models and machine learning algorithms. Common models include time series analysis models (such as ARI MA) and machine learning models (such as random forest, support vector machine, neural network, etc.);

[0075] B4: Use historical data sets to train the selected model. During the training process, parameter tuning may be required to obtain the best prediction performance. Use a portion of the data set that was not involved in the training to verify the prediction accuracy of the model. By comparing the prediction results with the actual data, the performance of the model can be evaluated.

[0076] B4: Input real-time data into the trained model to predict the changing trend of wind speed and wind direction in the future. The prediction results usually include the numerical value and probability distribution of wind speed and wind direction in the next few hours or days. Analyze the prediction results to determine the possible impact of wind speed and wind direction changes on the power generation of the wind farm. This step may require combining the power curve and efficiency characteristics of the wind turbine to evaluate the power generation capacity of the wind turbine under different wind speeds and wind directions.

[0077] Step three also includes the following steps:

[0078] C1: Detailed analysis of the collected real-time environmental data such as wind speed, wind direction, temperature, humidity, and the operating status and power generation of wind turbines to understand the current operating status and environmental conditions of the wind farm;

[0079] C2: Based on the power curve of the wind turbine, combined with real-time wind speed data, calculate the maximum theoretical power generation of the wind turbine under the current wind speed, analyze the efficiency characteristics of the wind turbine, determine the optimal operating efficiency range of the wind turbine under different wind speeds and wind directions, and calculate the optimal wind turbine operating parameters, such as blade angle, speed, etc., to ensure that the wind turbine achieves the best power generation efficiency under the current environmental conditions;

[0080] C3: Based on the overall power generation of the wind farm and the needs of the power grid, formulate an operation strategy for the wind turbines to achieve smoothing of the wind farm output power and balance of the power grid load. Use simulation software to verify the calculated operating parameters to ensure that these parameters can achieve the expected optimization effect in actual operation. Based on the simulation results and real-time monitoring data, fine-tune the operating parameters of the wind turbines to adapt to environmental changes and real-time changes in power grid demand.

[0081] Step 4 also includes the following steps:

[0082] D1: The overall power generation of the wind farm and the demand of the power grid. By adjusting the operating status of each wind turbine, the wind farm output power can be smoothed and the power grid load can be balanced.

[0083] D2: Monitor the operating status of wind turbines in real time, identify potential failures and performance degradation through data analysis, and perform maintenance and adjustments in a timely manner.

[0084] Step five includes the following steps:

[0085] E1: In the comprehensive planning and design phase, the selection and design of energy storage systems are combined, and cost, storage requirements and wind resource volatility are considered. Economical and efficient energy storage solutions are selected and designed, and the type of energy storage technology, system capacity, power level, configuration and control strategy are determined to meet the specific needs of the wind farm.

[0086] E2: The implementation and integration phase involves the deployment and monitoring of energy storage systems. After the energy storage equipment is installed in the wind farm, it is connected to the wind turbines and the grid. The real-time monitoring system tracks the equipment's operating status, including key parameters such as battery charging and discharging, temperature, and voltage, to ensure the system is safe and efficient, and to handle problems in a timely manner.

[0087] E3: The continuous optimization and maintenance phase involves the continuous adjustment of the energy storage system control strategy and the optimization of operating parameters, with the aim of improving energy storage efficiency and wind farm performance. Regular inspections and maintenance, such as battery health assessments and cooling system inspections, are aimed at preventing failures and extending system life, thus ensuring that the energy storage system can stably support wind farm operations in the long term.

[0088] Step six also includes the following steps:

[0089] F1: Send the optimized operating parameters and instructions to the control system of the wind turbine to execute the energy management strategy to ensure efficient and stable operation of the wind turbine;

[0090] F2: Monitor the operating status of wind farms and the response of the power grid in real time, collect feedback information, and continuously adjust and optimize energy management strategies.

[0091] In this embodiment, data collection involves setting the collection frequency, deploying sensors, data transmission, preprocessing and storage to ensure data quality and real-time performance. Predictive analysis includes data cleaning, selecting prediction features, selecting prediction models and algorithms, model training and verification, and predicting wind speed and wind direction change trends. Energy optimization involves analyzing environmental data and wind turbine status, calculating optimal operating parameters, and formulating operating strategies to balance wind farm output and grid demand. Load balancing and fault diagnosis include adjusting the operating status of wind turbines to smooth output power, and real-time monitoring and maintenance of wind turbines. Energy storage involves the selection, design, deployment, monitoring and optimization of energy storage systems to improve energy storage efficiency and wind farm performance. Control execution includes executing optimized operating parameters, real-time monitoring of wind farm and grid status, and adjusting energy management strategies based on feedback information.

[0092] The above generally describes the present invention in detail, but it is obvious to a person skilled in the art that some modifications or improvements can be made to the present invention. Therefore, modifications or improvements that do not depart from the spirit of the present invention are within the scope of protection of the present invention.

Claims

1. An energy management platform for wind turbines in a wind farm, characterized by: Including data acquisition and monitoring systems, wind resource assessment and prediction, energy optimization and fault diagnosis, economic analysis and user interface, safety protection and system integration; The data acquisition and monitoring system is responsible for collecting the operating data of the wind turbine in real time, including wind speed, wind direction, power generation, temperature and other information; The wind resource assessment and prediction can analyze wind speed and direction data, evaluate the availability and quality of wind resources, and predict the power generation of wind turbines in the future based on historical and real-time data, providing a basis for scheduling and optimization; The energy optimization and fault diagnosis can optimize the operation status of the wind turbine according to the prediction results and the grid demand, maximize the power generation and minimize the cost. At the same time, the operation status of the wind turbine can be monitored in real time, potential faults can be discovered in time and maintenance can be arranged to reduce downtime. The economic analysis and user interface can evaluate the economic performance of the wind farm, including cost analysis, revenue forecast, return on investment, etc., and provide an intuitive user interface so that operators can easily monitor the operating status of the wind farm, conduct data analysis and management decisions; The safety protection and system integration can ensure the safety of the wind farm under various operating conditions, including overload protection, short circuit protection, lightning protection, etc., ensure stable communication within the wind farm and with external systems, realize rapid data transmission and remote control, ensure that the platform is compatible with existing wind farm management systems and equipment, and support the integration of future technologies.

2. A method for managing wind turbine energy in a wind farm, characterized in that: The energy management method for wind turbines in a wind farm comprises the following steps: Step 1: Data collection; Step 2: Predictive analysis; Step 3: Energy optimization; Step 4: Load balancing and fault diagnosis; Step 5: Energy storage; Step 6: Control execution, monitoring and feedback.

3. The method for managing wind turbine energy in a wind farm according to claim 2, characterized in that: The step 1 also includes the following steps: A1: According to the operation requirements of the wind farm and the characteristics of environmental changes, set a reasonable data collection frequency and deploy various sensors to ensure that the monitoring area of ​​the wind farm can be fully covered; A2: The data collected by the sensor is transmitted to the central data processing system through wired or wireless communication to ensure the stability and real-time performance of data transmission for subsequent analysis and processing. The collected raw data is pre-processed, including data cleaning, format conversion, and outlier processing, to ensure data quality. A3: Store the preprocessed data in the database to facilitate subsequent query, analysis and prediction, ensure that all relevant systems and equipment in the wind farm can synchronize data in real time, so that the energy management system of the entire wind farm can work together, conduct preliminary analysis of the collected data to evaluate the operating status and environmental conditions of the wind farm, and provide basic data support for subsequent energy management.

4. The method for managing wind turbine energy in a wind farm according to claim 2, characterized in that: The step 2 also includes the following steps: B1: First, clean and format the collected real-time and historical data to ensure data quality, which may include removing outliers, filling missing data, and data standardization; B2: Select features from the preprocessed data that are helpful for wind speed and direction prediction, which may include historical wind speed, wind direction data, meteorological factors such as temperature, humidity, air pressure, and time series features; B3: Select appropriate meteorological forecasting models and machine learning algorithms based on the specific conditions of the wind farm and the characteristics of historical data. Common models include time series analysis models (such as ARIMA) and machine learning models (such as random forest, support vector machine, neural network, etc.); B4: Use historical data sets to train the selected model. During the training process, parameter tuning may be required to obtain the best prediction performance. Use a portion of the data set that was not involved in the training to verify the prediction accuracy of the model. By comparing the prediction results with the actual data, the performance of the model can be evaluated. B4: Input real-time data into the trained model to predict the changing trend of wind speed and wind direction in the future. The prediction results usually include the numerical value and probability distribution of wind speed and wind direction in the next few hours or days. Analyze the prediction results to determine the possible impact of wind speed and wind direction changes on the power generation of the wind farm. This step may require combining the power curve and efficiency characteristics of the wind turbine to evaluate the power generation capacity of the wind turbine under different wind speeds and wind directions.

5. The method for energy management of wind turbines in a wind farm according to claim 2, characterized in that: The step three also includes the following steps: C1: Detailed analysis of the collected real-time environmental data such as wind speed, wind direction, temperature, humidity, and the operating status and power generation of wind turbines to understand the current operating status and environmental conditions of the wind farm; C2: Based on the power curve of the wind turbine, combined with real-time wind speed data, calculate the maximum theoretical power generation of the wind turbine under the current wind speed, analyze the efficiency characteristics of the wind turbine, determine the optimal operating efficiency range of the wind turbine under different wind speeds and wind directions, and calculate the optimal wind turbine operating parameters, such as blade angle, speed, etc., to ensure that the wind turbine achieves the best power generation efficiency under the current environmental conditions; C3: Based on the overall power generation of the wind farm and the needs of the power grid, formulate an operation strategy for the wind turbines to achieve smoothing of the wind farm output power and balance of the power grid load. Use simulation software to verify the calculated operating parameters to ensure that these parameters can achieve the expected optimization effect in actual operation. Based on the simulation results and real-time monitoring data, fine-tune the operating parameters of the wind turbines to adapt to environmental changes and real-time changes in power grid demand.

6. The method for wind turbine energy management in a wind farm according to claim 2, characterized in that: The step 4 also includes the following steps: D1: The overall power generation of the wind farm and the demand of the power grid. By adjusting the operating status of each wind turbine, the wind farm output power can be smoothed and the power grid load can be balanced. D2: Monitor the operating status of wind turbines in real time, identify potential failures and performance degradation through data analysis, and perform maintenance and adjustments in a timely manner.

7. The method for managing wind turbine energy in a wind farm according to claim 2, characterized in that: The step five comprises the following steps: E1: In the comprehensive planning and design phase, the selection and design of energy storage systems are combined, and cost, storage requirements and wind resource volatility are considered. Economical and efficient energy storage solutions are selected and designed, and the type of energy storage technology, system capacity, power level, configuration and control strategy are determined to meet the specific needs of the wind farm. E2: The implementation and integration phase involves the deployment and monitoring of energy storage systems. After the energy storage equipment is installed in the wind farm, it is connected to the wind turbines and the grid. The real-time monitoring system tracks the equipment's operating status, including key parameters such as battery charging and discharging, temperature, and voltage, to ensure the system is safe and efficient, and to handle problems in a timely manner. E3: The continuous optimization and maintenance phase involves the continuous adjustment of the energy storage system control strategy and the optimization of operating parameters, with the aim of improving energy storage efficiency and wind farm performance. Regular inspections and maintenance, such as battery health assessments and cooling system inspections, are aimed at preventing failures and extending system life, thus ensuring that the energy storage system can stably support wind farm operations in the long term.

8. The method for energy management of wind turbines in a wind farm according to claim 2, characterized in that: The step six also includes the following steps: F1: Send the optimized operating parameters and instructions to the control system of the wind turbine to execute the energy management strategy to ensure efficient and stable operation of the wind turbine; F2: Monitor the operating status of wind farms and the response of the power grid in real time, collect feedback information, and continuously adjust and optimize energy management strategies.

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