Optimal control for a variable speed wind turbine based on extreme learning machine and adaptive Particle Swarm Optimization

Abstract

This paper presents a new optimal controller design for the sensorless variable speed wind turbine (SVSWT) based on the extreme learning machine (ELM) and the adaptive particle swarm optimisation (APSO) algorithms. The two main objectives of this command are to maximize the conversion of wind energy below the rated wind speed and to maintain the safety of the wind turbine system (WT) by minimizing stress on the drive train shafts. The proposed technique is based on the efficiency of the ELM for single hidden layer feed forward neural networks (SLFN) combined to sliding mode control (SMC) to respectively, improve the used model and stabilize the operation of the WT. ELM algorithm with high learning speed is used to approximate the nonlinear unmodelled dynamics while SMC is used to compensate the external disturbances and modelling errors. APSO algorithm is introduced to adapt and optimize the gain of the SMC. The efficiency of the proposed method is illustrated in simulations by the comparison with traditional SMC.