Optimization of Wind Power Generation in Ambon City Using the Queen Honey Bee Migration (QHBM) Method
DOI:
10.29303/jppipa.v12i9.14640Published:
2026-09-30Downloads
Abstract
The increasing demand for sustainable electricity generation highlights the importance of efficient wind power optimization, particularly in island regions with variable wind resources such as Ambon City, Indonesia. This study proposes an intelligent optimization framework based on the Queen Honey Bee Migration (QHBM) algorithm to improve wind power generation performance. A simulation model of a grid-connected wind energy conversion system was developed, integrating aerodynamic turbine modeling, generator dynamics, and power conversion stages. The optimization process aims to determine optimal operating conditions that maintain the turbine at an optimal tip speed ratio (λ) by adjusting controllable operational parameters, including blade pitch angle (β) and generator operating conditions. The proposed QHBM approach is evaluated using representative wind speed profiles ranging from 6–14 m/s derived from seasonal coastal wind conditions in Ambon. For benchmarking purposes, the algorithm performance is compared with Particle Swarm Optimization (PSO) under identical simulation settings. Results show that QHBM achieves higher power extraction performance, with an average improvement of 15.90% at a representative wind speed of 10 m/s. Statistical evaluation across 30 simulation runs indicates that QHBM also exhibits greater robustness, with significantly lower output variability. Furthermore, the algorithm achieves MPPT tracking efficiency of up to 96.80% under dynamic wind conditions. These results demonstrate that the migration-based search mechanism of QHBM enhances the ability to maintain optimal turbine operating conditions, leading to improved energy yield and operational stability for wind power systems in island environments.
Keywords:
Ambon City Metaheuristic algorithm Particle Swarm Optimization (PSO) Queen Honey Bee Migration (QHBM) Renewable energy Wind power optimizationReferences
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