An adaptive neuro-fuzzy inertia controller for variable-speed wind turbines

Abstract

A Variable-Speed Wind Turbine (VSWT) can serve as a good reservoir of Kinetic Energy (KE) for few seconds owing to wide operating rotor speed. Based on this fact, several approaches have been proposed to introduce synthetic inertial response in VSWT. Usually, this is accomplished by introducing an additional control loop at the outer most level of the control hierarchy. However, several key issues including the selection of control parameters and the effects of wind speed variations on the synthetic inertial support are not addressed. As a result, the KE reserve is severely under utilized. To address these concerns, in this work, a simple approach is proposed to control parameter selection which ensures the optimal use of available KE reserve. Further, to tackle the variable KE reserve, a comprehensive inertia controller using intelligent learning paradigm is designed. The proposed inertia controller can adapt to wind speed variations while providing optimum inertial response. Efficacy of the proposed approach is evaluated over the entire operating range of the VSWT. For further evaluation, wind speed data from NREL western wind integration is utilized. The results indicate that the proposed system is quite effective and can maintain an adequate performance over the entire operating range.