Inverse Optimal Adaptive Neural Power Regulation Control for Variable-Speed Wind Turbines With Load Mitigation

Traditional optimization control algorithms need to solve the Hamilton-Jacobi-Bellman (HJB) equation in real time, which requires a time-consuming training procedure and is difficult to apply in actual engineering facilities. To break this limitation, an inverse optimal adaptive neural pitch control scheme is first presented for variable-speed wind turbines (VSWTs) to achieve a comprehensive performance of power regulation and load mitigation in this paper. First of all, to facilitate the design of the controller, the non-affine VSWT model is converted into an affine model by employing the filter regulation error technology. Subsequently, the stability of the overall control system is demonstrated via the Lyapunov theory and the regulation error ultimately converges to an adjustable region near zero. Then, for a meaningful cost functional, the practical inverse optimality of the control system is realized. Finally, the OpenFAST (Fatigue, Aerodynamics, Structure and Turbulence) platform is utilized to compare the proposed scheme with existing pitch controllers on an NREL WP 1.5-MW three-blade VSWT to verify the feasibility of the presented scheme. Note to Practitioners—VSWTs are receiving increasing research attention due to their performance in maximizing power generation and mitigating effects of wind fluctuations. Under the turbulent wind condition, it has great practical significance to achieve stable power quality and alleviate fatigue loads on valuable components of VSWTs. How to improve the power quality and reduce the operation and maintenance cost of the turbine is a challenging problem. This paper presents an inverse optimal adaptive neural network control method for VSWTs under the high-speed turbulent wind condition.