Pitch angle control for a small-scale Darrieus vertical axis wind turbine with straight blades (H-Type VAWT)

• Published in: Renewable energy Vol. 114; pp. 1353 - 1362
• Main Authors: Abdalrahman, Gebreel, Melek, William, Lien, Fue-Sang
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.12.2017
• Subjects: blades; Computational fluid dynamics (CFD); computer software; Darrieus vertical axis wind turbine (H-type VAWT); fluid mechanics; Multilayer perceptron artificial neural network (MLP-ANN); neural networks; renewable energy sources; Variable pitch angle control; wind turbines; Darrieus vertical axis wind turbine (H-type VAWT); Variable pitch angle control; Multilayer perceptron artificial neural network (MLP-ANN); Computational fluid dynamics (CFD)
• ISSN: 0960-1481; 1879-0682
• DOI: 10.1016/j.renene.2017.07.068

Unlike horizontal axis wind turbines (HAWTs), the Darrieus vertical axis wind turbine (H-type VAWT) has been the subject of only a few recent studies directed at improving its self-starting capability and/or aerodynamic performance. The technique currently used for improving the performance of this type of turbine is pitch angle control. This paper presents intelligent blade pitch control for enhancing the performance of H-type VAWTs with respect to power output. To determine the optimum pitch angles, ANSYS Fluent Computational Fluid Dynamics (CFD) software was used for a study of the aerodynamic performance of a 2D variable pitch angle H-type VAWT at a variety of tip speed ratios (TSRs). For each case examined, the power coefficient (Cp) was calculated and compared to published experimental and CFD findings. The results obtained from the CFD model were then applied for the construction of an aerodynamic model of an H-type VAWT rotor, which constituted a prerequisite for designing an intelligent pitch angle controller using a multilayer perceptron artificial neural network (MLP-ANN) method. The performance of the MLP-ANN blade pitch controller was compared to that of a conventional controller (PID). The findings demonstrate that for an H-type VAWT, compared to a conventional PID controller, an MLP-ANN results in superior power output. •Study effect of blade pitching on performance of straight-bladed Darrieus vertical axis wind turbine (H-type VAWT) in terms of power output.•Investigate the aerodynamic performance of a 2D variable and fixed pitch angle H-type VAWT numerically using a commercial CFD package ANSYS Fluent.•Use CFD results such as optimum pitch angles to design intelligent active pitch angle control based on artificial neural networks (ANNs) strategies for a small-scale H-type VAWT.•Develop a conventional PID controller to control the blade pitch of an H-type VAWT and compare its performance with a MLP-ANN controller in terms of power output.