GA-optimized inverse fuzzy model control of OWC wave power plants

Oscillating water columns (OWCs) are widely regarded as the simplest and most reliable type of wave energy converter. An economically viable OWC wave power plant requires maximizing the performance of the power take-off system, composed of an air turbine coupled to an electrical generator. Besides, the OWC power plant's inherent nonlinearities and modeling uncertainties must be dealt with. One solution is the use of fuzzy controllers since they capture both nonlinearities and uncertainties of the system, which can largely contribute to improving the overall efficiency. This paper shows how an inverse fuzzy model optimized by a genetic algorithm (GA) can improve the control of an OWC and, consequently, its performance. As a case study, the Mutriku power plant with a biradial turbine was addressed, considering 11 different sea states of the characteristic wave climate. Two optimization objectives were considered: maximizing turbine and generator output average powers. Compared with a well-known baseline control strategy, the GA-fuzzy control did not significantly contribute to increasing average turbine power. However, when maximizing generator yearly-accumulated power, there were improvements of over 9%. Ultimately, these research findings may play an essential role in control strategies for wave power plants.