Wind power generation control system and method with intelligent device integrated with base system

Abstract

The present disclosure relates to a wind power generation control system and method in which an intelligent device is integrated with a base system, the system comprising a base system and an intelligent device, wherein an intelligent operation model of the intelligent device is deployed in the base system through containerization, and the base system provides a callable base resource space to the intelligent device through an open standardized resource interface; the base system is configured to acquire running data of a wind turbine in real time and share the running data to the base resource space; the intelligent device is configured to acquire the running data from the base resource space through a fusion bus, analyze the running data through the intelligent operation model, determine a wind turbine control strategy, and control the wind turbine to generate wind power according to the wind turbine control strategy. The base system provides the callable base resource space to the intelligent device through the open standardized resource interface, avoids external interface coupling, and solves the problem of data fragmentation between the intelligent device and the base system.

Description

Technical Field

[0001] This disclosure relates to the field of digital and intelligent wind power technology, specifically to a wind power generation control system and method that integrates an intelligent device with a base system. Background Technology

[0002] Based on the application of Large Language Models (LLMs) in natural language understanding, text generation, and multimodal interaction, intelligent wind farm systems can rely on independent monitoring platforms or AI (Artificial Intelligence) modules to achieve data analysis and control decisions during wind power generation. However, in traditional wind power architectures, there is a hierarchical data interface between the platform-based control system and the AI-based computing intelligent devices. Therefore, the models are isolated, resulting in delayed control responses and an inability to act in real-time on the equipment control layer, making it difficult to achieve globally optimal operational control of the wind farm. Summary of the Invention

[0003] The purpose of this disclosure is to provide a wind power generation control system and method that integrates intelligent devices and a base system, aiming to achieve global optimal operation control of wind farms and improve the efficiency of wind power generation control and collaborative control.

[0004] To achieve the above objectives, a first aspect of this disclosure provides a wind power generation control system integrating an intelligent device and a base system, the system comprising: The system comprises a base system and a smart device, wherein the smart device's intelligent computing model is deployed on the base system in a containerized manner, and the base system provides the smart device with callable base resource space through an open, standardized resource interface; The base system is used to acquire the wind turbine's operating data in real time and share the operating data to the base resource space; The intelligent device is used to acquire the operating data from the base resource space, analyze the operating data through the intelligent computing model, determine the wind turbine control strategy, and control the wind turbine to generate wind power according to the wind turbine control strategy.

[0005] Optionally, the intelligent computing model includes: a wind power prediction model, a health diagnosis model, and an optimal scheduling model; The wind power prediction model is used to predict the wind turbine's power generation in the next time period based on the operating data. The health diagnosis model is used to determine the health status of the wind turbine based on the operating data, and to generate early warning information when the health status triggers an early warning, the early warning information being used to perform risk warning; The optimized scheduling model is used to determine the wind turbine control strategy based on the predicted power generation and the health status.

[0006] Optionally, the wind turbine control strategy includes one or more of the following: wind turbine speed regulation strategy and blade angle control strategy.

[0007] Optionally, the standardized resource interface includes a data interface, a model interface, and an execution interface; The data interface is used for the intelligent computing model to obtain the operating data from the base resource space; The model interface is used by the intelligent computing model to call the computing model deployed in the base system to analyze the running data; The execution interface is used by the intelligent device to output the wind turbine control strategy to control the wind turbine to generate wind power.

[0008] Optionally, the intelligent device is equipped with a scheduling engine; The scheduling engine is used to obtain the operating status information of the intelligent device, and when the operating status information indicates that the intelligent device is in an abnormal operation, switch the wind turbine to a redundancy strategy control mode and / or a manual control mode.

[0009] Optionally, the scheduling engine is further configured to obtain the task execution result for the wind turbine control strategy, and if the task execution result indicates that the task execution has failed, to revert the operating state of the wind turbine to before the execution of the wind turbine control strategy, and to re-execute the steps from obtaining the operating data to controlling the wind turbine to generate wind power.

[0010] Optionally, the smart device is also equipped with a semantic mapping module; The semantic mapping module is used to perform one or more of the following on the running data: field matching, unit unification, data preprocessing, outlier filtering, and data normalization, so as to map and align the fields of the running data with the features of the intelligent computing model. The intelligent computing model is used to analyze the mapped and aligned operating data to determine the wind turbine control strategy.

[0011] Optionally, the base system is used to call the intelligent computing module according to task priority, analyze the operating data, and determine the wind turbine control strategy.

[0012] Optionally, the base system is further configured to acquire the execution result corresponding to the wind turbine control strategy and the acquired environmental change data; The intelligent device is also used to perform model self-learning and parameter adaptive adjustment based on the execution results and the environmental change data.

[0013] A second aspect of this disclosure provides a wind power generation control method integrating a smart device and a base system, the method comprising: The system acquires the wind turbine's operating data in real time through the base system and shares the operating data to the base resource space. The operational data is obtained from the base resource space via a smart device; The intelligent computing model of the intelligent device analyzes the operating data to determine the wind turbine control strategy. The intelligent computing model is deployed in a containerized manner on the base system, and the base system provides the intelligent device with callable base resource space through an open standardized resource interface. According to the wind turbine control strategy, the wind turbine is controlled to generate wind power.

[0014] The above technical solution can achieve at least the following beneficial effects: By containerizing the intelligent computing model of smart devices onto a base system, the base system provides accessible base resource space to the smart devices through open, standardized resource interfaces. The base system acquires real-time wind turbine operating data and shares this data within the base resource space. The smart devices retrieve operating data from the base resource space via a converged bus, analyze the data using the intelligent computing model, determine wind turbine control strategies, and control the wind turbines for wind power generation based on these strategies. The base system's open, standardized resource interfaces prevent external interface coupling and resolve the data silo problem between the smart devices and the base system. This achieves globally optimal operation control of the wind farm and improves the efficiency of collaborative wind power generation control.

[0015] Other features and advantages of this disclosure will be described in detail in the following detailed description section. Attached Figure Description

[0016] The accompanying drawings are provided to further illustrate the present disclosure and form part of the specification. They are used together with the following detailed description to explain the present disclosure, but do not constitute a limitation thereof. In the drawings: Figure 1 This is a block diagram of a wind power generation control system based on an integrated architecture of intelligent device and base system provided according to an embodiment of this disclosure.

[0017] Figure 2 This is a block diagram of another wind power generation control system based on an integrated architecture of intelligent device and base system provided according to an embodiment of this disclosure.

[0018] Figure 3 This is a block diagram of another wind power generation control system based on an integrated architecture of intelligent device and base system provided according to an embodiment of this disclosure.

[0019] Figure 4 This is a flowchart of a wind power generation control method based on an integrated architecture of intelligent device and base system provided according to an embodiment of the present disclosure. Detailed Implementation

[0020] The specific embodiments of this disclosure will be described in detail below with reference to the accompanying drawings. It should be understood that the specific embodiments described herein are for illustration and explanation only and are not intended to limit this disclosure.

[0021] It should be noted that all actions involving the acquisition of signals, information, or data in this disclosure are carried out in compliance with the relevant data protection laws and policies of the country where the location is situated, and with authorization from the owner of the relevant device.

[0022] Before introducing the wind power generation control method based on the integrated architecture of intelligent device and base system provided by the embodiments of this disclosure, we will first introduce the technical means used in the relevant scenarios and the existing technical problems.

[0023] In existing intelligent wind power control systems, the Supervisory Control and Data Acquisition (SCADA) platform, data center, and artificial intelligence (AI) analysis module are deployed separately, achieving a layered architecture. The SCADA platform, located on the base platform, serves as the data acquisition and monitoring layer, primarily responsible for collecting operational data from equipment such as the wind turbine system, pitch system, weather tower, and power controller. The collected data can be transmitted to the data center via industrial protocols (such as Modbus and OPC-UA). The AI ​​analysis module, running on a dedicated server or cloud platform, serves as the data processing and analysis layer. It retrieves historical data in batches from the base platform through interfaces, performs offline intelligent analysis for tasks such as power prediction, fault diagnosis, and scheduling optimization, and outputs control optimization results or alarm information.

[0024] However, because the collaboration between the AI ​​analysis module and the monitoring platform mainly relies on API calls or command interactions, they are independent of each other in terms of data structure, command response, and control permissions. While this "loosely coupled" architecture can achieve a certain degree of intelligent optimization, it has the following shortcomings: 1. The artificial intelligence analysis module cannot directly access the real-time control bus, resulting in a high response latency; 2. Because the artificial intelligence analysis module and the monitoring platform are different systems, their interface compatibility is poor and their deployment complexity is high; 3. The artificial intelligence analysis module lacks dynamic awareness of the monitoring platform's operational status, leading to delayed decision-making; 4. It cannot achieve true adaptive control and continuous learning.

[0025] This disclosure provides a wind power generation control system based on an integrated architecture of intelligent devices and a base system. See also... Figure 1 As shown, the wind power generation control system based on the integrated architecture of intelligent device and base system includes: base system and intelligent device, wherein the intelligent computing model of the intelligent device is deployed in the base system through containerization, and the base system provides the intelligent device with callable base resource space through open standardized resource interfaces; The intelligent computing model is deployed in a containerized manner within the underlying system, rather than the underlying system simply calling the intelligent computing model externally. This achieves system-level integration with shared resources, unified scheduling, and closed-loop feedback. The intelligent computing model is embedded into the underlying system using container or plug-in mechanisms, enabling shared memory, real-time calls, and efficient communication. Containerized deployment achieves resource sharing and deep integration with the control channel of the underlying system.

[0026] The base system is used to acquire the wind turbine's operating data in real time and share the operating data to the base resource space; The base system provides fundamental capabilities for wind power system operation, such as data acquisition, equipment communication, model library management, task scheduling, and digital twin support. For example, it can acquire real-time operational data such as wind speed, wind direction, turbine power, temperature, and current.

[0027] The intelligent device is used to acquire the operating data from the base resource space, analyze the operating data through the intelligent computing model, determine the wind turbine control strategy, and control the wind turbine to generate wind power according to the wind turbine control strategy.

[0028] See also Figure 1 As shown, the intelligent device communicates with the base system via a bus. In this way, the intelligent device can obtain operating data through the bus, and can also feed back control commands corresponding to the fan control strategy to the base system through the bus.

[0029] The intelligent device can be embedded in the base system and can consist of an intelligent agent container, a semantic mapping module, a converged bus, and a scheduling engine. The intelligent computing model in the intelligent device can be configured as the wind power business layer to perform specific tasks such as power prediction, health diagnosis, and optimized scheduling, and will operate collaboratively with the base resources through the converged layer.

[0030] In this embodiment, by deeply integrating the intelligent device into the base system via an embedded approach, real-time collaboration between the intelligent device and the base system is achieved at the data flow, model operation, and control decision-making levels, thereby constructing a self-learning and self-optimizing wind power operation system. For example, the intelligent device can deploy its intelligent computing model on the base system using lightweight container technology (such as Docker or base plugin mechanisms), sharing memory and control channels with the base system. The data channel employs encrypted transmission, and the base interface supports access control. This solves the problems of data fragmentation, complex interfaces, and low collaboration efficiency between the intelligent device and the base system in wind power intelligent systems.

[0031] In this embodiment of the disclosure, the intelligent device and the control interface can be seamlessly connected, thereby directly controlling the wind turbine to generate wind power according to the wind turbine control strategy. In this way, the control strategy is executed in real time, improving the efficiency of wind turbine control.

[0032] The aforementioned technical solution deploys the intelligent computing model of the smart device within a containerized base system. The base system provides the smart device with callable base resource space through open, standardized resource interfaces. The base system acquires real-time wind turbine operating data and shares this data with the base resource space. The smart device retrieves operating data from the base resource space via a fusion bus, analyzes the data using the intelligent computing model, determines the wind turbine control strategy, and controls the wind turbine to generate wind power based on this strategy. By providing callable base resource space to the smart device through open, standardized resource interfaces, the base system avoids external interface coupling, solves the data silo problem between the smart device and the base system, achieves globally optimal operation control of the wind farm, and improves the efficiency of collaborative wind power generation control.

[0033] Optionally, see Figure 2 As shown, the intelligent computing model includes: a wind power prediction model, a health diagnosis model, and an optimal scheduling model; The wind power prediction model is used to predict the wind turbine's power generation in the next time period based on the operating data. In this embodiment of the disclosure, the wind power prediction model can be trained based on historical operating data, including wind speed, wind direction, temperature, etc., using machine learning algorithms, such as neural networks. Once the operating data is acquired in real time, the wind power prediction model, based on the learned patterns and combined with current meteorological conditions and other factors, predicts the power generation of the wind turbine in the next time period.

[0034] The health diagnosis model is used to determine the health status of the wind turbine based on the operating data, and to generate early warning information when the health status triggers an early warning, the early warning information being used to perform risk warning; In this embodiment, the running data is compared with a preset health standard threshold, and data analysis algorithms are used to identify abnormal data patterns. If the data exceeds the threshold or exhibits an abnormal pattern, a health status is determined, triggering an alert and generating alert information including the problem type and location.

[0035] The optimized scheduling model is used to determine the wind turbine control strategy based on the predicted power generation and the health status.

[0036] In this embodiment of the disclosure, the optimal wind turbine control strategy is determined by comprehensively predicting power generation and health status and using optimization algorithms, while meeting constraints such as safety and efficiency.

[0037] In this embodiment of the disclosure, see Figure 3 As shown, the wind power prediction model and health diagnosis model can be configured as the wind power business layer, thereby performing parallel operations to predict power generation and determine health status. The optimized scheduling model is configured as the intelligent fusion layer, which integrates the predicted power generation and health status to determine the wind turbine control strategy and feeds the wind turbine control strategy back to the base system, which serves as the platform base layer.

[0038] In this embodiment, the power generation strategy is adjusted based on real-time wind speed and other operational data, while health diagnostics are performed to provide early warnings of faults. The synergistic effect of power prediction and optimized scheduling improves power generation efficiency and reduces the rate of abnormal outages, thereby enhancing the level of intelligence and operational efficiency.

[0039] In this embodiment, the wind power prediction model, health diagnosis model, and optimal scheduling model can be implemented in parallel, improving the scalability and portability of the intelligent device.

[0040] Optionally, the wind turbine control strategy includes one or more of the following: wind turbine speed regulation strategy and blade angle control strategy.

[0041] In this embodiment, the system follows instructions from an optimized scheduling model, taking into account current wind speed and predicted power generation. When wind speed changes, the inverter adjusts the motor input frequency, altering the motor speed and consequently adjusting the wind turbine rotor speed. If increased power generation is required and wind speed permits, the speed is increased; otherwise, the speed is decreased, ensuring efficient operation of the wind turbine under different wind speeds while preventing overspeed damage.

[0042] In this embodiment, the blade angle is changed using an electric pitch control system based on wind speed, wind direction, and wind health status. At low wind speeds, the angle is increased to enhance lift; at high wind speeds, the angle is decreased to reduce load, ensuring the wind turbine's safety and stability and achieving optimal power generation.

[0043] Optionally, the standardized resource interface includes a data interface, a model interface, and an execution interface; The data interface is used for the intelligent computing model to obtain the operating data from the base resource space; The data interface supports access to both historical and real-time operational data. In this embodiment, the intelligent computing model extracts historical or real-time operational data from the base resource space by calling the data interface.

[0044] The model interface is used by the intelligent computing model to call the computing model deployed in the base system to analyze the running data; The execution interface is used by the intelligent device to output the wind turbine control strategy to control the wind turbine to generate wind power.

[0045] Optionally, the intelligent device is equipped with a scheduling engine; The scheduling engine is used to obtain the operating status information of the intelligent device, and when the operating status information indicates that the intelligent device is in an abnormal operation, switch the wind turbine to a redundancy strategy control mode and / or a manual control mode.

[0046] In this embodiment, a scheduling engine manages tasks within the intelligent device, and the intelligent device's output directly drives wind turbine control, achieving real-time closed-loop control. Furthermore, the scheduling engine supports task prioritization and real-time invocation, enabling the intelligent device to directly execute wind turbine control (such as speed adjustment and blade angle adjustment) according to the wind turbine control strategy. This improves collaborative efficiency and real-time performance by unifying semantics and data channels.

[0047] Optionally, the scheduling engine is further configured to obtain the task execution result for the wind turbine control strategy, and if the task execution result indicates that the task execution has failed, to revert the operating state of the wind turbine to before the execution of the wind turbine control strategy, and to re-execute the steps from obtaining the operating data to controlling the wind turbine to generate wind power.

[0048] In this way, the scheduling engine supports a rollback mechanism for task failures, ensuring the safe operation of wind turbine units.

[0049] Optionally, the smart device is also equipped with a semantic mapping module; The semantic mapping module is used to perform one or more of the following on the running data: field matching, unit unification, data preprocessing, outlier filtering, and data normalization, so as to map and align the fields of the running data with the features of the intelligent computing model. In this embodiment, a semantic mapping module is used to achieve adaptive alignment between the data fields of the base system and the model features in the smart device. Semantic alignment ensures that the smart device and the base system use the same language, ensuring that the model in the smart device can correctly understand the operational data collected by the base system.

[0050] The intelligent computing model is used to analyze the mapped and aligned operating data to determine the wind turbine control strategy.

[0051] In this embodiment, a semantic mapping module and a fusion bus are used to achieve a unified understanding and retrieval of operational data collected by the base system and the characteristics of intelligent devices. This enables the collaborative operation of multiple intelligent computing models, forming a unified decision-making chain.

[0052] In this way, a collaborative architecture between the base system and the intelligent device can be established, enabling the AI ​​model in the intelligent device to achieve unified semantics, unified data channels, and unified scheduling mechanisms with the core services of the base system, such as data acquisition, control execution, and digital twins, thereby realizing system-level intelligent control.

[0053] Optionally, the base system is used to call the intelligent computing module according to task priority, analyze the operating data, and determine the wind turbine control strategy.

[0054] Optionally, the base system is further configured to acquire the execution result corresponding to the wind turbine control strategy and the acquired environmental change data; The intelligent device is also used to perform model self-learning and parameter adaptive adjustment based on the execution results and the environmental change data.

[0055] In this embodiment, the execution results and environmental change data are fed back to the intelligent device. The model in the intelligent device can perform incremental training or online learning, enabling model self-learning and adaptive adjustment. This ensures the continuous improvement of the system's intelligence level, not only enhancing prediction accuracy and scheduling strategy accuracy to achieve system-level intelligence, but also allowing the integration to go beyond mere collaboration and achieve self-evolution to meet the ever-changing wind turbine power generation control requirements.

[0056] In this way, the execution results of wind turbine control strategy and environmental change data can be fed back in real time, realizing the adaptive adjustment and optimization of intelligent computing model, transforming wind turbine control from single intelligent control to continuous optimization control, and achieving system-level self-evolution.

[0057] This disclosure also provides a wind power generation control method integrating a smart device and a base system, see [link to relevant documentation]. Figure 4 As shown, the method includes: In step S41, the operating data of the wind turbine is acquired in real time through the base system, and the operating data is shared to the base resource space; In this embodiment, the base system uses a sensor network to collect real-time operating parameters of various components of the fan, such as speed, temperature, and power. After data cleaning and preprocessing, the data is transmitted to the base resource space through an internal communication protocol and stored in a unified format to ensure data integrity and accuracy.

[0058] In step S42, the operating data is obtained from the base resource space via a smart device; In this embodiment of the disclosure, the smart device sends a data request to the base resource space through a standardized data interface. After verifying the legality of the request, the resource space feeds back the required operating data to the smart device.

[0059] In step S43, the operating data is analyzed by the intelligent computing model of the intelligent device to determine the wind turbine control strategy. The intelligent computing model is deployed in the base system in a containerized manner, and the base system provides the intelligent device with callable base resource space through an open standardized resource interface. In this embodiment of the disclosure, the intelligent computing model, within the base system container, utilizes the model interface to call resources, analyzes operational data, and generates control strategies.

[0060] In step S44, the wind turbine is controlled to generate wind power according to the wind turbine control strategy.

[0061] In this embodiment of the disclosure, the intelligent device converts the strategy into instructions and controls the wind turbine to generate electricity through the execution interface.

[0062] The aforementioned technical solution involves containerizing the intelligent computing model of the smart device onto the base system. The base system provides the smart device with callable base resource space through open, standardized resource interfaces. The base system acquires real-time wind turbine operating data and shares this data with the base resource space. The smart device retrieves operating data from the base resource space via a fusion bus, analyzes the data using the intelligent computing model, determines the wind turbine control strategy, and controls the wind turbine to generate wind power based on this strategy. By providing callable base resource space to the smart device through open, standardized resource interfaces, the base system avoids external interface coupling, solves the data silo problem between the smart device and the base system, achieves globally optimal operation control of the wind farm, and improves the efficiency of collaborative wind power generation control.

[0063] The preferred embodiments of this disclosure have been described in detail above with reference to the accompanying drawings. However, this disclosure is not limited to the specific details of the above embodiments. Within the scope of the technical concept of this disclosure, various simple modifications can be made to the technical solutions of this disclosure, and these simple modifications all fall within the protection scope of this disclosure.

[0064] It should also be noted that the various specific technical features described in the above specific embodiments can be combined in any suitable manner without contradiction. In order to avoid unnecessary repetition, this disclosure will not describe the various possible combinations separately.

[0065] Furthermore, various different embodiments of this disclosure can be combined in any way, as long as they do not violate the spirit of this disclosure, they should also be regarded as the content disclosed in this disclosure.

Claims

1. A wind power generation control system integrating an intelligent device and a base system, characterized in that, The system includes: The system comprises a base system and a smart device, wherein the smart device's intelligent computing model is deployed on the base system in a containerized manner, and the base system provides the smart device with callable base resource space through an open, standardized resource interface; The base system is used to acquire the wind turbine's operating data in real time and share the operating data to the base resource space; The intelligent device is used to acquire the operating data from the base resource space, analyze the operating data through the intelligent computing model, determine the wind turbine control strategy, and control the wind turbine to generate wind power according to the wind turbine control strategy.

2. The wind power generation system according to claim 1, characterized in that, The intelligent computing model includes: a wind power prediction model, a health diagnosis model, and an optimal scheduling model; The wind power prediction model is used to predict the wind turbine's power generation in the next time period based on the operating data. The health diagnosis model is used to determine the health status of the wind turbine based on the operating data, and to generate early warning information when the health status triggers an early warning, the early warning information being used to perform risk warning; The optimized scheduling model is used to determine the wind turbine control strategy based on the predicted power generation and the health status.

3. The wind power generation system according to claim 2, characterized in that, The wind turbine control strategy includes one or more of the following: wind turbine speed regulation strategy and blade angle control strategy.

4. The wind power generation system according to claim 1, characterized in that, The standardized resource interfaces include data interfaces, model interfaces, and execution interfaces; The data interface is used for the intelligent computing model to obtain the operating data from the base resource space; The model interface is used by the intelligent computing model to call the computing model deployed in the base system to analyze the running data; The execution interface is used by the intelligent device to output the wind turbine control strategy to control the wind turbine to generate wind power.

5. The wind power generation system according to claim 1, characterized in that, The intelligent device is equipped with a scheduling engine; The scheduling engine is used to obtain the operating status information of the intelligent device, and when the operating status information indicates that the intelligent device is in an abnormal operation, switch the wind turbine to a redundancy strategy control mode and / or a manual control mode.

6. The wind power generation system according to claim 5, characterized in that, The scheduling engine is also used to obtain the task execution result for the wind turbine control strategy, and if the task execution result indicates that the task execution has failed, to roll back the operating state of the wind turbine to before the execution of the wind turbine control strategy, and to re-execute the steps from obtaining the operating data to controlling the wind turbine to generate wind power.

7. The wind power generation system according to any one of claims 1-6, characterized in that, The intelligent device is also equipped with a semantic mapping module; The semantic mapping module is used to perform one or more of the following on the running data: field matching, unit unification, data preprocessing, outlier filtering, and data normalization, so as to map and align the fields of the running data with the features of the intelligent computing model. The intelligent computing model is used to analyze the mapped and aligned operating data to determine the wind turbine control strategy.

8. The wind power generation system according to any one of claims 1-6, characterized in that, The base system is used to call the intelligent computing module according to task priority, analyze the operating data, and determine the wind turbine control strategy.

9. The wind power generation system according to any one of claims 1-6, characterized in that, The base system is also used to obtain the execution result corresponding to the wind turbine control strategy and the obtained environmental change data; The intelligent device is also used to perform model self-learning and parameter adaptive adjustment based on the execution results and the environmental change data.

10. A wind power generation control method integrating an intelligent device and a base system, characterized in that, The method includes: The system acquires the wind turbine's operating data in real time through the base system and shares the operating data to the base resource space. The operational data is obtained from the base resource space via a smart device; The intelligent computing model of the intelligent device analyzes the operating data to determine the wind turbine control strategy. The intelligent computing model is deployed in a containerized manner on the base system, and the base system provides the intelligent device with callable base resource space through an open standardized resource interface. According to the wind turbine control strategy, the wind turbine is controlled to generate wind power.

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