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OA

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.