Ocean Wave Energy Control Using Aquila Optimization Technique

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.

Automated summary

This paper presents a method for ocean wave energy control using the Aquila optimization (AO) technique. The method focuses on an oscillating water column (OWC)-type wave energy converter fitted with a Wells turbine and doubly fed induction generator (DFIG). A backstepping controller (BSC)-based rotational speed control strategy is designed using the Lyapunov stability theory to achieve maximum power point tracking (MPPT) by controlling the rotor speed of the DFIG. An integral square error (ISE)-type fitness function is defined and minimized using the AO technique to find the optimal control parameters for the BSC. The results show that the AO technique outperforms particle swarm optimization (PSO) and a genetic algorithm (GA) in terms of rotor speed error and fitness function value.