基于萤火虫优化模糊P&O的微生物燃料电池最大功率点跟踪
MAXIMUM POWER POINT TRACKING OF MICROBIAL FUEL CELL BASED ON FIREFLY OPTIMIZATION FUZZY P&O
樊立萍 1陈其鹏1
作者信息
- 1. 沈阳化工大学信息工程学院,沈阳 110142;工业环境-资源协同控制与优化技术辽宁省高校重点实验室,沈阳 110142
- 折叠
摘要
针对微生物燃料电池产电量低、输出不稳定等问题,提出一种混合萤火虫优化和模糊改进扰动观测的最大功率点跟踪算法,利用双种群萤火虫算法的全局优化能力和模糊控制的逻辑推理能力产生更精确的控制信号,通过Boost变换器对等效负载进行动态调节.结果表明:基于双种群萤火虫优化-模糊扰动观测的最大功率点跟踪算法能以更快的速度追踪到并稳定在最大功率点,降低了稳态误差,抑制了功率振荡,提高了抗干扰能力,显著改善了微生物燃料电池的产电能力和供电质量.
Abstract
Aiming at the problems of low power and unstable output of microbial fuel cells,a maximum power point tracking control algorithm with a mixture of firefly optimization and fuzzy modified perturb&observe(P&O)is proposed.Using the global optimization ability of dual-species firefly optimization algorithm and the logic reasoning ability of fuzzy control,more accurate control signals are generated,and the equivalent load is dynamically adjusted through a Boost converter.The results show that the maximum power point tracking algorithm based on dual species firefly optimization mixed with fuzzy modified perturb&observe can track and stabilize at the maximum power point at a faster speed,reduce the steady-state error,suppress power oscillation,enhance the anti-interference ability,and significantly improve the power generation capacity and power supply quality of microbial fuel cells.
关键词
微生物燃料电池/最大功率跟踪/模糊控制/萤火虫算法Key words
microbial fuel cell/maximum power point tracking/fuzzy control/firefly algorithm引用本文复制引用
基金项目
国家自然科学基金(61143007)
辽宁省教育厅重点攻关项目(LJKZZ20220057)
中国-北马其顿政府间科技合作项目(国科外[2019]22:6-8)
出版年
2024
NETL
NSTL
万方数据