Robust Fuzzy Neural Network Sliding Mode Control For Wind Turbine with a Permanent Magnet Synchronous Generator - Alaoui, K. Belamfedel; Sefriti, S.; Boumhidi, I. (Transactions on Engineering and Computing Sciences, 2017)
Abstract—In the present paper, we are interested in the contribution of wind power to the electricity supply in power systems of small sized isolated communities. A robust fuzzy neural sliding control (FNSC) is proposed to track the maximum power point for an isolated wind energy conversion system using a permanent magnet synchronous generator (PMSG) with a hysteresis rectifier connected to a DC load. The turbine is controlled by a sliding mode controller (SMC) to reach the maximum power level. The main objective of the control is to adjust the rectifier voltage to provide it to the DC load as well as to maintain the maximum power extraction. In presence of large uncertainties and wind speed variations, the traditional SMC produces the chattering phenomenon due to the higher needed gain. In order to reduce this gain, FNSC is used for the estimation of the unknown part, thus provide lower gain. The stability of the proposed FNSC is analyzed by Lyapunov theory, simulations results are presented and the proposed control performance is shown by the comparison with the conventional SMC.