INDIVIDUAL BLADE PITCH CONTROLLER BASED ON FUZZY LOGIC CONTROL (FLC) AND ARTIFICIAL NEURAL NETWORKS (ANNs) FOR A SMALL H-DARRIEUS VERTICAL AXIS WIND TURBINE

Abstract

Wind turbines are mainly divided into horizontal and vertical axis wind turbines. The quality and performance of wind turbines have been extensively investigated by researchers. A pitch angle controller is one of the most common techniques used to improve wind turbines’ aerodynamic performance. Unlike horizontal axis wind turbines, only a few studies being conducted recently to improve the self-starting capability and aerodynamic performance of H-type Vertical Axis Wind Turbines with straight blades (Darrieus VAWT). This study aims to process the issue of VAWT performance using the pitch angle controller technique. Due to the mathematical complexity associated with addressing the behavior of VAWT, numerical results extracted from a Computational Fluid Dynamics (CFD) model are used to design a system identification model, neural networks (ANNs) based, that can identify the behavior of the VAWT model. In addition, two controllers based on both neural networks (MLP-network) and fuzzy logic (FLC) techniques were designed to control Darrieus VAWT pitch angle. Moreover, comparisons between the two intelligent controllers were provided. Results show that both controllers (ANNs and FLC) can achieve better control performance in terms of VAWT power regulations