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Hybrid pitch angle controller approaches for stable wind turbine power under variable wind speed

Sarkar, Md.R., Julai, S., Tong, C.W., Uddin, M., Romlie, M.F. and Shafiullah, GMORCID: 0000-0002-2211-184X (2020) Hybrid pitch angle controller approaches for stable wind turbine power under variable wind speed. Energies, 13 (14). Article 3622.

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The production of maximum wind energy requires controlling various parts of medium to large-scale wind turbines (WTs). This paper presents a robust pitch angle control system for the rated wind turbine power at a wide range of simulated wind speeds by means of a proportional–integral–derivative (PID) controller. In addition, ant colony optimization (ACO), particle swarm optimization (PSO), and classical Ziegler–Nichols (Z-N) algorithms have been used for tuning the PID controller parameters to obtain within rated stable output power of WTs from fluctuating wind speeds. The proposed system is simulated under fast wind speed variation, and its results are compared with those of the PID-ZN controller and PID-PSO to verify its effeteness. The proposed approach contains several benefits including simple implementation, as well as tolerance of turbine parameters and several nonparametric uncertainties. Robust control of the generator output power with wind-speed variations can also be considered a significant advantage of this strategy. Theoretical analyses, as well as simulation results, indicate that the proposed controller can perform better in a wide range of wind speed compared with the PID-ZN and PID-PSO controllers. The WT model and hybrid controllers (PID-ACO and PID-PSO) have been developed in MATLAB/Simulink with validated controller models. The hybrid PID-ACO controller was found to be the most suitable in comparison to the PID-PSO and conventional PID. The root mean square (RMS) error calculated between the desired power and the WT’s output power with PID-ACO is found to be 0.00036, which is the smallest result among the studied controllers.

Item Type: Journal Article
Murdoch Affiliation: Engineering and Energy
Publisher: MDPI
Copyright: © 2020 by the authors
United Nations SDGs: Goal 7: Affordable and Clean Energy
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