Universiti Teknologi Malaysia Institutional Repository

Grey wolf optimizer tuned PI controller for enhancing output parameters of fixed speed wind turbine

Sule, A. H. and Mokhtar, A. S. and Jamian, J. J. and Sheikh, U. U. and Khidrani, A. (2020) Grey wolf optimizer tuned PI controller for enhancing output parameters of fixed speed wind turbine. In: 2020 IEEE International Conference on Automatic Control and Intelligent Systems, I2CACIS 2020, 20 June 2020, Shah Alam, Selangor.

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Official URL: http://dx.doi.org/10.1109/I2CACIS49202.2020.914017...

Abstract

This paper proposes optimal tuning of Proportional Integral (PI) controller using Grey Wolf Optimizer (GWO) to overcome the slow convergence into local optimum associated with the Particle Swarm Optimization (PSO), and Genetic Algorithm (GA) tuning methods. The optimal gains of the PI controller obtained from tuning using GWO, PSO and GA were implemented in the pitch angle control of the fixed speed Wind Turbine integrated into the distribution system. The objectives are to enhance the efficiency and reduced the fluctuation in the Wind Turbine output. Similarly, the Ziegler Nichols (ZN) tuned PI controller, the PI-fuzzy logic and Fuzzy Logic controllers were implemented in the same distribution system. The implementation of the tuned PI controllers has enhanced the output power of the Wind Turbine compared to the value obtained using the Hybrid or Fuzzy controllers. Furthermore, the GWO tuned PI controller recorded the high average output power of the Wind Turbine with low mean power error compared to other controllers. Also, it recorded the second-position in smoothing the Wind Turbine output power. The significance of the research is it has enhanced the efficiency and smoothing the output power of the fixed speed Wind Turbine.

Item Type:Conference or Workshop Item (Paper)
Uncontrolled Keywords:tuning PI controller, wind turbine output power
Subjects:T Technology > TK Electrical engineering. Electronics Nuclear engineering
Divisions:Electrical Engineering
ID Code:93579
Deposited By: Yanti Mohd Shah
Deposited On:30 Nov 2021 08:33
Last Modified:30 Nov 2021 08:33

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