Fuzzy adaptive power optimization control of wind turbine with improved whale optimization algorithm and kernel extreme learning machine

Since the short-term effective wind speed of wind turbine (WT) cannot be accurately measured, this brings great challenges to the power optimization control of WT. In this article, a power optimization control is proposed to increase the power generation of WT. Firstly, a novel improved whale optimization algorithm (NIWOA) is proposed, which has faster convergence speed and higher optimization precision. Secondly, a multi-goal optimization model (MGOM) is established to design effective wind speed soft sensor (EWSSS), which is based on kernel extreme learning machine (KELM) and the data-driven approach, and find the tracking target of power optimization control. The parameters of the KELM are optimized by the NIWOA to enhance the forecast precision of the EWSSS. Finally, based on the EWSSS, a fuzzy adaptive control (FAC) and a variable pitch control of WT are designed to ensure that the actual output power of the generator can better track the optimal output power, and enhance the power generation of WT. The results of the simulated experiments not only prove that the effectiveness and robustness of the FAC, but also demonstrate the EWSSS has higher estimation accuracy.