Adaptive hybrid PSO-ORB maximum power point tracking for tidal energy conversion systems

Tidal energy conversion systems (TECS) provide a predictable and sustainable renewable energy source, yet nonlinear turbine characteristics and rapidly varying flow conditions pose significant challenges for effective maximum power point tracking (MPPT). Conventional MPPT techniques often face a trade-off between fast dynamic response and steady-state stability, limiting energy capture and operational reliability. To address this challenge, this study proposes an adaptive hybrid MPPT strategy that integrates Particle Swarm Optimization (PSO) with an Optimum Relation-Based (ORB) method for tidal energy applications. The proposed PSO-ORB MPPT algorithm employs a bidirectional switching mechanism, where PSO is selectively activated during transient conditions for global search, while ORB provides deterministic local refinement under steady-state operation. The approach is evaluated using MATLAB/Simulink-based simulations of a TECS model under dynamic tidal velocity profiles. Key performance metrics, including response time, steady-state oscillation, overshoot, and tracking efficiency, are quantitatively analysed and compared with standalone PSO and modified ORB (M-ORB) methods. Simulation results show that the hybrid PSO-ORB strategy reduces response time by up to 36.81 %, suppresses steady-state oscillations by more than 51 %, and lowers transient overshoot by 16.5 % compared to conventional approaches, while maintaining tracking efficiencies above 99 %. These improvements reduce power fluctuations and component stress, thereby enhancing operational reliability and supporting more efficient and sustainable long-term deployment of TECS.