Ocean Wave Energy Control Using Aquila Optimization Technique

Sunil Kumar Mishra, Amitkumar V. Jha, Bhargav Appasani (), Nicu Bizon (), Phatiphat Thounthong and Pongsiri Mungporn
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Sunil Kumar Mishra: School of Electrical Engineering, Kalinga Institute of Industrial Technology, Bhubaneswar 751024, India
Amitkumar V. Jha: School of Electrical Engineering, Kalinga Institute of Industrial Technology, Bhubaneswar 751024, India
Bhargav Appasani: School of Electrical Engineering, Kalinga Institute of Industrial Technology, Bhubaneswar 751024, India
Nicu Bizon: Faculty of Electronics, Communication and Computers, University of Pitesti, 110040 Pitesti, Romania
Phatiphat Thounthong: Renewable Energy Research Centre (RERC), Faculty of Technical Education, King Mongkut’s University of Technology North Bangkok, Bangkok 10800, Thailand
Pongsiri Mungporn: Renewable Energy Research Centre (RERC), Faculty of Technical Education, King Mongkut’s University of Technology North Bangkok, Bangkok 10800, Thailand

Energies, 2023, vol. 16, issue 11, 1-21

Abstract: This paper presents ocean wave energy control using the Aquila optimization (AO) technique. An oscillating water column (OWC)-type wave energy converter has been considered that is fitted with a Wells turbine and doubly fed induction generator (DFIG). To achieve maximum power point tracking (MPPT), the rotor speed of the DFIG must be controlled as per the MPPT law. The MPPT law is designed in such a way that the Wells turbine flow coefficient remains within the threshold limit. It avoids the turbine from stalling which generates the maximum power. The MPPT law provides the reference rotor speed which is followed by the actual rotor speed. For this, a backstepping controller (BSC)-based rotational speed control strategy has been designed using the Lyapunov stability theory. The BSC has unknown control parameters which should be selected such that tracking errors are minimum. Hence, the objective of this work is to find the unknown control parameters using an optimization approach. The optimization approach of selecting BSC control parameters for an OWC plant has not been explored yet. To achieve this, an integral square error (ISE)-type fitness function has been defined and minimized using the AO technique. The results achieved using the AO technique have been compared with particle swarm optimization (PSO) and a genetic algorithm (GA), validating its superior performance. The rotor speed error maximum peak overshoot is least for AO-BSC as compared to PSO-BSC and GA-BSC. The fitness function value for AO comes out to be least among all the optimization methods applied. However, all tested methods provide satisfactory results in terms of turbine flow coefficient, rotor speed and output power. The approach paves the way for future research on ocean wave energy control.

Keywords: aquila optimizer; backstepping control; maximum power point tracking; oscillating water column; particle swarm optimization (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
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