Abstract
Due to the influence of wind speed disturbance, there are some uncertain phenomena in the parameters of the nonlinear wind turbine model with time in an actual working environment. In order to mitigate the side effects of uncertainties in speed models of wind turbines, researchers have designed a variety of controllers in recent years. However, traditional control methods require more knowledge of dynamics. Therefore, based on reinforcement learning and system state data, a robust wind turbine controller that adopts adaptive dynamic programming (ADP) is proposed. The ADP algorithm is a combination of Temporal-Difference (TD) algorithm and actor-critic structure, which can guarantee the rotor speed is stable around the rated value to indirectly adjust the wind energy utilization coefficient by changing the pitch angle in the area of high wind speed and achieve online learning in real-time. In addition, the variation of the pitch angle command of the proposed controller is relatively gradual, which can reduce the energy consumption of the variable pitch actuator, and extend the service life of the equipment. Finally, the wind speed model is simulated by combined wind speed based on Weibull distribution, the comprehensive simulation results show that the proposed controller has better control effect than some existing ones.
Highlights
With the growth of the energy demand in the world, environmental problems are becoming more and more serious; attracting a lot of attention from renewable energies
The adaptive dynamic programming (ADP) algorithm is a combination of Temporal-Difference (TD) algorithm and actor-critic structure, it can guarantee the rotor speed is stable around the rated value to indirectly improve the efficiency of use of wind energy by changing the pitch angle in the area of high wind speed and achieve online learning in real-time
Based on the principle of reinforcement learning and the control objective of the controller, a robust variable pitch controller based on reinforcement learning is proposed, and intended to be applied to the variable pitch control of the wind turbines in this paper
Summary
With the growth of the energy demand in the world, environmental problems are becoming more and more serious; attracting a lot of attention from renewable energies. The variable pitch control method adjusts the blade pitch angle according to the change of wind speed to control the stability of the rotor speed of the wind turbines. Based on the above problems and combined with the analysis of the wind turbine models in [5], [6], this paper designs a pitch angle controller based on reinforcement learning with stable pitch angle change, which stabilizes the rotor speed at the rated value in an environment with higher wind speed than the rated speed. The ADP algorithm is a combination of Temporal-Difference (TD) algorithm and actor-critic structure, it can guarantee the rotor speed is stable around the rated value to indirectly improve the efficiency of use of wind energy by changing the pitch angle in the area of high wind speed and achieve online learning in real-time. 3) Compared with the previous work, the method proposed in this paper only needs to set the control objective and does not need to know how to reach the objective, for example, it does not need to adjust the control parameters
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