MPPT Control in an Offshore Wind Turbine Optimized with Genetic Algorithms and Unsupervised Neural Networks

Abstract

In this work, a control operation of a 1.5 MW offshore wind turbine (WT) for maximum power point tracking (MPPT) when wind speed is below rated, is studied. The implemented controller is designed using the general Direct Speed Control (DSC) scheme in which artificial neural networks (ANN) are incorporated to close the control loop. The neural controller acts in an unsupervised mode updating its weights with the incorporation of a learning algorithm. The optimal configuration parameters of the controller are determined by genetic algorithms. With this intelligent control strategy, the generator speed is regulated by varying the electromagnetic torque while adapting to the external phenomena in real time. Then, the output power, through the power coefficient ( $${C}_{p}$$ ), reaches the maximum wind power generation in that region. The offshore WT model is subjected to external loads due to wind and waves, which increase the system complexity and produce tower vibrations, negatively impacting the control efficiency. Despite that, it is shown that the proposed controller is able to operate with satisfactory results in terms of power generation and even reducing vibration, and it has been compared to the OpenFAST embedded torque control for the same WT providing better results.