PREDICTION OF PERFORMANCE FOR SAVONIUS-DARRIEUS WIND ROTOR BY HYBRID NEURO-FUZZY CONTROLLER

Abstract

Vertical Axis Wind Turbine (VAWT) is a viable proposition for small-scale uses like, rural electrification, pumping, desalinating, household applications etc in many developing countries including India. In this paper, a hybrid neuro-fuzzy controller has been developed using gradient-based training algorithm to evaluate the performance of a combined three-bladed Savonius-Darrieus rotor. The objective of the study is to design a controller that causes more uniform loading on the generator by minimizing fluctuations in output parameters with change of input and also that improves rotor performance. A two-input-single-output controller has been designed. The tip speed ratio and overlap have been taken as input parameters, and output parameters are power coefficients and torque coefficients. At the first step, the input data are fuzzified by assigning fuzzy levels to the input data sets, and then trained outputs are obtained by back propagation learning algorithm. The controller results are in good agreement with the experimental results both qualitatively and quantitatively. For power coefficient (Cp), the agreement is within 4.5%, and for torque coefficient (Ct) it is within 2%. Moreover, the performance of the hybrid neuro-fuzzy controller has also been compared with Fuzzy Logic Controller (FLC) & ANN controller. The present controller predicts smoother values of performance parameters compared with other controllers.