Multi-objective MPPT Optimisation for PV System Using QHBM Algorithm in Madura Island - Nugraha, Agil Zaidan; Aripriharta; Handayani, Anik Nur (Universitas Muhammadiyah Malang, 2025)
This study presents the application of the Queen Honey Bee Migration (QHBM) algorithm, for Maximum Power Point Tracking (MPPT) in an off-grid photovoltaic (PV) system on Madura Island. Implemented in Python, QHBM optimizes a 3.3 kW PV array (six polycrystalline silicon panels, 550 W each, configured in 2-series and 3-parallel) under tropical conditions (irradiation: 860–970 W/m², temperature: 26–30°C) using data from the East Java BMKG Trunojoyo Meteorological Station. QHBM’s multi-objective optimization balances power conversion efficiency (95.0–99.1%), power quality (THD < 4.5%), and component longevity (current ripple: 3.1–3.2 A), outperforming Perturb and Observe (P&O: 78% efficiency under low irradiation and 34% under partial shading) and Particle Swarm Optimization (PSO: 85% and 88%). Trade-offs are managed by minimizing ripple-induced thermal stress (10–15% lower than P&O) and achieving rapid convergence (0–3 ms vs. 300–500 ms for PSO), ensuring reliability in Madura’s dynamic climate. The system, integrated with a single-phase full-bridge inverter (96% efficiency), delivers a consistent daily energy output of 14,941.87 Wh (SD ±267.45 Wh) and reduces CO2 emissions by 118.49 kgCO2e annually. QHBM was chosen over P&O and PSO for its superior efficiency, faster response, and robustness under partial shading and noisy irradiation (±10% variations), offering a scalable solution for sustainable electrification in Indonesia’s archipelagic regions.