Data-driven predictive control for floating offshore wind turbines based on deep learning and multi-objective optimization

Abstract

In order to deal with the problems of complicated modeling and the conflicts of control objectives in the control of floating offshore wind turbines (FOWTs), a multi-objective predictive control strategy for FOWTs based on a deep learning model and multi-objective optimization is proposed. In this strategy, the dynamic prediction model of the FOWT based on a gated recurrent neural network is established. Based on the dynamic prediction model, a multi-objective predictive control algorithm for individual pitch control of FOWTs is developed. The multi-objective distribution estimation algorithm is used to optimize the pitch angle considering the constraints of the pitch angle setting. The proposed method is simulated in different wind conditions by FAST. The results show that, compared with the collective pitch control and the gain scheduling PI individual pitch control, the output power of the proposed method is closer to the rated power, and the proposed method significantly reduces the mechanical load of the blade root. Moreover, compared with the gain scheduling PI individual pitch control, the proposed method effectively restrains platform pitching motion.