超小型衛星における最適化手法を用いた電力制御の検討

抄録

This study aims to develop a high-power acquisition system for nano-satellites. Microcomputers are utilized for power control of nano-satellites, considering size and power consumption. In this study, Perturbation & Observation (P & O), Particle Swarm Optimization (PSO), and Genetic Algorithm (GA) were implemented on Arduino UNO for Maximum Power Point Tracking (MPPT) control. The control processes were compared under conditions where the power characteristics exhibited both unimodal and multimodal functions. In MPPT control, key considerations include generated power, time to reach maximum power, and power fluctuations, all of which were focal points of our investigation. From experimental results, it was confirmed that PSO achieves maximum power even under conditions with a limited number of particles due to memory constraints. Additionally, it was observed that the operating point accurately tracked the maximum power for both unimodal and multimodal characteristics. The inference down from the experimental results suggested that PSO, where the Global Best is shared by all particles, proved to be an effective algorithm. These findings suggested the potential for achieving efficient MPPT control by using nano-satellites using PSO.