Robust Design of ANFIS-Based Blade Pitch Controller for Wind Energy Conversion Systems Against Wind Speed Fluctuations

Wind speed fluctuations and load demand variations represent the big challenges against wind energy conversion systems (WECS). Besides, inefficient measuring devices environmental impacts (e.g. temperature, humidity, noise signals) affect system equipment, leading to increased uncertainty issues. In addition, time delay due communication channels can make a gap between transmitted control signal WECS that causes instability for operation. To tackle these issues, this paper proposes an adaptive neuro-fuzzy inference (ANFIS) as effective technique blade pitch of instead conventional controllers. However, ANFIS requires suitable dataset training testing adjust its membership functions in order provide performance. regard, also suggests strategy prepare sufficient controller. Specifically, new optimization algorithm named mayfly (MOA) is developed find optimal parameters proportional integral derivative (PID) controller demonstrate advantages proposed technique, it compared with different three algorithms literature. Another contribution time-domain figure demerit established confirm minimization settling maximum overshoot simultaneous manner. A lot test scenarios are performed effectiveness robustness based technique. The method verified on frequency domain conditions driven from Hermite-Biehler theorem. results emphases provides superior performance fluctuations, variations, uncertainties, channels.