Optimal PTO passive control of a floating two-buoy wave energy converter based on multi-level optimization

A fully coupled dynamic multi-level optimization framework combining parameter-sensitivity with interval-constrained global optimization is developed to design the optimal passive power take-off control strategy for a floating two-buoy wave energy converter. Linear, quadratic, and hybrid power take-off configurations are optimized using two hybrid optimization strategies, genetic algorithm–pattern search and particle swarm optimization–pattern search. The coupled numerical model is validated against experimental data from 1:9 scale physical model tests. The three-level optimization framework is implemented as follows. First, power take-off parameter sensitivity analysis determines effective parameter ranges as constraints for use in the next level. Second, two global–local hybrid algorithms iteratively optimize three power take-off passive control models. In the third level, the optimal power take-off coefficients are integrated into the coupled model, and the dynamic responses compared for different optimization strategies to identify the most efficient power take-off passive control configuration. Compared with direct optimization, our proposed multi-level optimization framework significantly enhances both optimization efficiency and solution robustness. The optimization results indicate that a genetic-algorithm-pattern-search optimization framework achieves faster convergence and superior solution optimality for all three power take-off configurations. The hybrid power take-off passive control system achieves both optimal energy conversion and a broad power take-off response bandwidth. This study provides a generalized, efficient, robust approach for power take-off passive control of complex wave energy converter systems and offers a promising pathway towards multi-level optimization design for advanced wave energy technologies.