An adaptive and energy-maximizing control of wave energy converters using extremum-seeking approach

TL;DR

This paper evaluates various extremum-seeking control schemes to enhance wave energy converter efficiency, demonstrating their convergence, adaptability, and effectiveness in optimizing power extraction under different wave conditions.

Contribution

It systematically compares multiple ES control schemes for WECs, highlighting their convergence properties, robustness, and adaptive capabilities in real-time power optimization.

Findings

Most ES schemes reliably converge for two-parameter optimization.

Sliding mode and perturbation-based ES perform better in irregular sea states.

ES controllers adapt to changing wave conditions effectively.

Abstract

In this paper, we systematically investigate the feasibility of different extremum-seeking (ES) control schemes to improve the conversion efficiency of wave energy converters (WECs). Continuous-time and model-free ES schemes based on the sliding mode, relay, least-squares gradient, self-driving, and perturbation-based methods are used to improve the mean extracted power of a heaving point absorber subject to regular and irregular waves. This objective is achieved by optimizing the resistive and reactive coefficients of the power take-off (PTO) mechanism using the ES approach. The optimization results are verified against analytical solutions and the extremum of reference-to-output maps. The numerical results demonstrate that except for the self-driving ES algorithm, the other four ES schemes reliably converge for the two-parameter optimization problem, whereas the former is more suitable for optimizing a single-parameter. The results also show that for an irregular sea state, the sliding mode and perturbation-based ES schemes have better convergence to the optimum, in comparison to other ES schemes considered here. The convergence of PTO coefficients towards the performance-optimal values are tested for widely different initial values, in order to avoid bias towards the extremum. We also demonstrate the adaptive capability of ES control by considering a case in which the ES controller adapts to the new extremum automatically amidst changes in the simulated wave conditions.