Wind Turbine Control Using Predictive Wind Data
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Solution Overview
Problem
Current wind turbine control systems face challenges in accurately predicting wind speed and direction, leading to unstable rotational speeds and significant losses in power generation due to lag in control responses, especially in large wind farms with varying turbine heights and turbulent conditions.
Innovation Solution
A method for autonomously controlling wind turbines by using real-time wind condition data shared between turbines in a wind farm, employing polar coordinates and virtual radar to select a forward-direction turbine for predictive control, thereby reducing lag and inaccuracy in wind condition prediction and enhancing power generation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If a main control system performs control strategy adjustment and yawing passively based on detected wind speed variation or wind direction variation, then the control system structure is simple, but the pitch variation and yawing action lag behind the wind variation, resulting in unstable rotational speed and loss of power generation
Solution Approach 1:
The patent applies preliminary action by using wind direction sensors and anemometers to detect wind variations before they significantly impact the turbine. The control system calculates predicted wind direction and speed, then proactively adjusts the yawing system and pitch control system in advance, eliminating the lag experienced by passive systems while maintaining reasonable structural complexity.
Solution Approach 2:
The patent implements feedback by continuously monitoring wind direction and speed through sensors, comparing detected values with predicted values, and using this information to dynamically adjust control strategies. The system feeds back rotational speed measurements to fine-tune pitch variations, creating a closed-loop control system that responds accurately to wind changes without excessive complexity.
2Measurement precision
If anemometer towers are used to measure wind speed and direction, then the measurement coverage is limited, but the measured values are only reference data and difficult to use for accurate control timing
Solution Approach 1:
The patent divides the wind farm into multiple monitoring zones with distributed sensors on individual turbines rather than relying on a single anemometer tower. Each turbine has its own wind direction sensor and anemometer, segmenting the measurement system to provide localized, actionable data for each turbine's control system, improving both measurement precision and operational ease.
Solution Approach 2:
The patent introduces a wind direction sensor and anemometer as intermediary devices that directly measure wind conditions at each turbine location. These intermediaries convert physical wind parameters into electrical signals that the control system can process, enabling accurate determination of control timing without relying on distant tower measurements.
3Device complexity
If weather forecast is used to predict wind speed value, then the prediction method is simple, but the predicted value is quite inaccurate and aimless
Solution Approach 1:
The patent uses real-time feedback from wind direction sensors and anemometers to continuously update and refine wind speed predictions. The control system compares forecasted values with actual measured values, adjusting prediction algorithms dynamically to maintain high accuracy without requiring complex external forecasting systems.
Solution Approach 2:
The patent performs preliminary wind condition assessment by detecting wind direction and speed trends before they fully develop. The system calculates predicted wind parameters in advance based on sensor data, enabling accurate predictions without relying solely on simple weather forecasts or complex big data systems.
4Measurement precision
If big data is used to predict wind speed and direction values, then the data quantity and quality requirements are high, but the prediction has certain lag for controlling the wind turbine
Solution Approach 1:
The patent applies preliminary action by using locally installed wind direction sensors and anemometers to detect wind variations as they begin to affect each turbine. The control system processes this real-time data immediately to predict upcoming wind changes and adjusts control parameters in advance, eliminating the lag inherent in big data systems that process historical information.
Solution Approach 2:
The patent enables each wind turbine to serve itself by equipping it with its own wind measurement sensors and control system. Each turbine independently detects local wind conditions, predicts wind variations, and adjusts its own operation without relying on centralized big data processing, thereby eliminating prediction lag while maintaining high accuracy through localized real-time measurements.
Data Source
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AI summary
A wind turbine and an operational control method therefor are disclosed. The method comprises: obtaining current directional data from the nacelle of the wind turbine; and, according to the directional data, positional data of each wind turbine of a wind farm, as well as wind condition data as measured by each wind turbine, and controlling operational equipment of the wind turbine, so as to increase power generated by the wind turbine. According to the current direction of the nacelle of the wind turbine, positional data of each wind turbine of a wind farm, and wind condition data as measured by each wind turbine, accurate control policy adjustment is performed in advance on operational equipment of a wind turbine, thereby increasing the power generated by the wind turbine. A device for implementing the control method is also disclosed.