Prediction of aerodynamics performance of continuously variable-speed wind turbine by adaptive neuro-fuzzy methodology

Abstract

Horizontal-axis wind turbines (HAWT) have the constant rotor speed, while the blade tip speed changes continuously. This could reduce power performance of the wind turbine. In this paper, the accuracy of soft-computing technique was employed for aerodynamics performance prediction based on continuously variable-speed horizontal-axis wind turbine with optimal blades. The process, which simulates the $$\varphi$$ (relative wind angle), BEP (blade element parameter), SP (solidity parameter), CPtot (total power coefficient), CPl (local power coefficient), and CT (local thrust coefficient), with adaptive neuro-fuzzy inference system (ANFIS) was constructed. The inputs were local speed ratios λr and different values of drag-to-lift ratio e. The performance of proposed system is confirmed by the simulation results. The ANFIS results are compared with the experimental results using root-mean-square error and coefficient of determination and Pearson’s coefficient. The experimental results show that an improvement in predictive accuracy and capability of generalization can be achieved by the ANFIS approach. The effectiveness of the proposed strategies is verified based on the simulation results.