Enhanced stability and performance of the tidal energy conversion system using adaptive optimum relation-based MPPT algorithms - Noor Lina, Ramli; Mohd Rusllim, Mohamed; Wan Ismail, Ibrahim; Kurukuri, Peddakapu (Springer, 2025)
Tidal energy is a highly predictable and sustainable resource with significant potential to meet global energy demands. This study proposes an adaptive optimum relation-based (A-ORB) maximum power point tracking algorithm to enhance the efficiency, stability, and adaptability of tidal energy conversion systems. The A-ORB algorithm integrates the optimum relation-based (ORB) approach with Hill Climb Search (HCS), along with an adaptive gain adjustment mechanism that dynamically tunes the parameter K based on power variation (ΔP). This hybrid strategy enables faster convergence, improved responsiveness to tidal fluctuations, and reduced power oscillations. The novelty of the proposed method lies in the combination of ORB and HCS with adaptive gain tuning, which collectively improves MPPT performance under variable tidal conditions. Simulation results show that A-ORB outperforms conventional techniques such as small step perturb and observe (SS-PO), small step incremental conductance (SS-InC), and bio-inspired particle swarm optimization (BI-PSO) in both tracking accuracy and power output. Specifically, A-ORB achieves a convergence time of 0.32 s and a maximum power output of 4833 W, compared to 4658 W (0.41 s) for SS-PO, 4561 W (0.5 s) for SS-InC, and 4699 W (0.37 s) for BI-PSO. Moreover, A-ORB exhibits significantly lower power oscillations (3.9 W) compared to 17.88 W (SS-PO), 21.96 W (SS-InC), and 10.4 W (BI-PSO). These findings demonstrate the potential of A-ORB to enhance MPPT efficiency, reduce transient response time, and improve adaptability in dynamic tidal energy environments.