山东电力技术2026，Vol.53Issue(1)：75-87,13.DOI:10.20097/j.cnki.issn1007-9904.250053
基于人类进化优化算法的混合光伏-温差系统最大功率点跟踪
Maximum Power Point Tracking of Hybrid PV-TEG System via Human Evolutionary Optimization Algorithm
摘要
Abstract
Hybrid photovoltaic-thermoelectric generator(PV-TEG)system realizes the dual utilization of two different energy sources,representing a significant innovation in the advancement of renewable energy technologies..In order to enable the hybrid PV-TEG system to effectively cope with the negative impacts of partial shading condition(PSC)and non-uniform temperature distribution(NTD),and to improve the energy conversion efficiency and utilization of the hybrid system,a hybrid system MPPT method based on human evolutionary optimization(HEOA)is proposed.HEOA divides the global search process into two distinct phases:human exploration and human development.It employs logical chaotic mapping to enhance the quality of the initial solution.Notably,the jumping strategy utilized in the human exploration phase effectively integrates both global and local features,thereby improving search efficiency while minimizing the risk of converging on local optima.The effectiveness and applicability of the HEOA-based MPPT method for hybrid systems are evaluated in four arithmetic scenarios.A comparison with two heuristic algorithms—beluga whale optimization(BWO)and subtraction-average-based optimizer(SABO)—demonstrates that the HEOA method outperforms the other algorithms in all critical aspects,including power output stability,solution quality,and responsiveness to rapid changes in irradiance.关键词
人类进化优化算法/混合光伏-温差系统/最大功率跟踪/部分遮蔽/SimuNPSKey words
human evolutionary optimization/hybrid photovoltaic-thermoelectric generator system/maximum power point tracking/partial shading/SimuNPS分类
信息技术与安全科学引用本文复制引用
李鸿彪,郜登科,杨博..基于人类进化优化算法的混合光伏-温差系统最大功率点跟踪[J].山东电力技术,2026,53(1):75-87,13.基金项目
国家自然科学基金项目(62263014) （62263014）
云南省基础研究专项(202401AT070344).National Natural Science Foundation of China(62263014) （202401AT070344）
Natural Science Foundation of Yunnan Province(202401AT070344). （202401AT070344）
