Abstract
A novel data-driven robust approximate optimal Maximum Power Point Tracking (MPPT) control method is proposed for the wind power generation system by using the adaptive dynamic programming (ADP) algorithm. First, a data-driven model is established by a recurrent neural network (NN) to reconstruct the wind power system dynamics using available input-output data. Then, in the design of the controller, based on the obtained data-driven model, the ADP algorithm is utilized to design the approximate optimal tracking controller, which consists of the steady-state controller and the optimal feedback controller. Ulteriorly, developing a robustifying term to compensate for the NN approximation errors introduced by implementing the ADP method. Based on the Lyapunov approach, it proves the stability of the designed model and controller to show that the proposed controller guarantees the system power asymptotically tracking the maximum power. Finally, the simulation results demonstrate that the control method stabilizes the tip speed ratio near the optimal value when the wind speed is lower than the rated wind speed. Moreover, the tracking response speed of the proposed method is fast, which enhances the stability and robustness of the system.
Highlights
With the rapid development of the social economy, traditional energy sources are gradually depleted
In this paper, we propose a datadriven robust adaptive control (DRAC) method for maximum power tracking of the wind power generation system, which changes the wind energy utilization coefficient by controlling the rotation speed of the wind turbine to keep the wind energy utilization coefficient at the maximum value, so as to guarantees the system to asymptotically track the maximum power when the wind speed is under the rated wind speed
RESEARCH CONTRUBUTIONS 1) Based on the nonlinear equations of wind power generation and adaptive dynamic programming algorithm, combined with recurrent neural network (RNN), we propose a data-driven robust adaptive control method for maximum power tracking of the wind power generation system
Summary
With the rapid development of the social economy, traditional energy sources are gradually depleted. When it is difficult to obtain an accurate wind turbine power curve, a method that combines sliding mode control and mountain climbing search algorithm is proposed in [6]. A. RESEARCH CONTRUBUTIONS 1) Based on the nonlinear equations of wind power generation and adaptive dynamic programming algorithm, combined with recurrent neural network (RNN), we propose a data-driven robust adaptive control method for maximum power tracking of the wind power generation system. 3) we design a robust compensation term to make the control input error converge to zero, reduce the influence of uncertain disturbance terms on the tracking effect of the wind power generation system, and enhance the robustness and performance of the controller The adaptive dynamic programming algorithm is used to approximate the optimal feedback control input value so that the control input error converges to a tiny range, which proves the tracking effectiveness of the controller. 3) we design a robust compensation term to make the control input error converge to zero, reduce the influence of uncertain disturbance terms on the tracking effect of the wind power generation system, and enhance the robustness and performance of the controller
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