A Fuzzy Logic–MPC Driven Multi-Objective PSO Optimization Approach for Coordinated Energy Management and Maximum Power Point Tracking in Integrated Wave–wind Conversion System
Adel Elgammal *
Utilities and Sustainable Engineering, The University of Trinidad & Tobago UTT, Trinidad and Tobago.
*Author to whom correspondence should be addressed.
Abstract
Wave–wind energy conversion systems integrated have become an interesting alternative for enhancing the reliability of renewable power generation, mainly at maritime/coastal sites with severe resourcevariation. However, the interconnection of two very fluctuating energy sources impose great power quality challenges, dynamic stability and coordinated control. In this study, a new hybrid wave–wind system aimed at real time energy management and MPPT has been developed based on Fuzzy Logic –MPC driven MOPSO optimization algorithm is proposed. The proposed structure combines: (i) a Fuzzy Logic Controller (FLC) for adaptive MPPT control in non-linear and fast changing sea states; (ii) a Model Predictive Controller (MPC) for short-horizon optimisation of active/reactive power flows and converter dynamics; and, (iii), a MOPSO supervisory layer that continuouslymonitors the efficiency, power smoothness and mechanical stress system-wide.Numerical results indicate that the proposed FLC–MPC control is truly responsive to fast variations of wave height and wind turbulence, up to 22% improvement in MPPT tracking accuracy with respect to conventional approaches being reported. By allowing the system to adjust the number of optimal control parameters off grid the MOPSO algorithm achieves a 17% decrease in power oscillations, 28% improved energy capture ratio as well as significant decreases on turbine and point absorber mechanical loading. Moreover, the hierarchical mode guarantees that both the storage use and grid-connected operation are implemented in a coordinated manner, such that with complex ocean–wind disturbances, the voltage and frequency can be stably regulated.In conclusion, it can be noted that the multi-layer intelligent control approach satisfied energy capture maximum effort and system reliability, as well as improved power quality for in integrated wave–wind energy systems. This work provides a scalable platform for the next-generation marine renewable hybridization and enables the transition to sustainable offshore energy infrastructure and secure coastal microgrids.
Keywords: Fuzzy logic control, Model Predictive Control (MPC), Multi-Objective Particle Swarm Optimization (MOPSO), Wave Energy Converter (WEC), wind energy system, hybrid renewable energy systems, Maximum Power Point Tracking (MPPT), energy management strategy