Enhancing Offshore Wind Turbines Performance with Hybrid Control Strategies Using Neural Networks and Conventional Controllers

Abstract

A wind turbine is a complex energy converter that requires a robust control solution to overcome performance limitations caused by its mechatronics and external disturbances. Within the turbine operation regions, in the area below the nominal power, the challenge of achieving a precise and highly efficient response must be addressed using multi-objective control strategies that can also contribute to the stability of the structure, reducing vibrations. In this work, a control architecture to maximize the power generation and minimize the vibrations is proposed. Based on this architecture four different hybrid control strategies exploiting radial basis function neural networks and conventional regulators are proposed to cover this twofold objective. The controllers calculate the appropriate electromagnetic torque to track the maximum power point and thus achieve the generation of maximum power, while reducing the acceleration of the tower movement. The neural controllers use a non-supervised learning algorithm to adaptively adjust the weights of the neuros to better couple to the wind turbine dynamics. These combined control methods are tested on a 5 MW floating offshore wind turbine under the influence of winds and waves. The results show that these hybrid strategies are valid for achieving a more efficient response and decreasing turbine fatigue, thus extending their lifetime.