基于多元宇宙优化算法的混合光伏-热电系统MPPT设计

Multi-verse Optimization-based MPPT Design for PV-TEG System

混合光伏-热电(centralized hybrid photovoltaic thermoelectric generator,PV-TEG)系统在部分遮蔽(partial shading condition,PSC)条件下呈现多个局部最大功率点(local maximum power point,LMPP)。采用多元宇宙优化算法(multi-verse optimization,MVO),用于PV-TEG系统在PSC下的最大功率点跟踪(maximum power point tracking,MPPT)。MVO通过平衡全局搜索和局部搜索,有效识别多个LMPPs中唯一的全局最大功率点(global maximum power point,GMPP),避免搜索结果陷入LMPP,以提高发电效率和能源利用率。算例仿真结果表明:基于MVO的MPPT可以在更短的时间内收集到更高的功率,实现功率波动最小。

A centralized hybrid photovoltaic thermoelectric generator(PV-TEG)system shows multiple local maximum power points(LMPPs)under a partial shading condition(PSC).The multi-verse optimization(MVO)is used for the maximum power point tracking(MPPT)of the PV-TEG system under PSC.MVO can effectively identify the unique global maximum power point(GMPP)among multiple LMPPs by balancing global search and local search,avoiding search results trapped in LMPP and improving generation efficiency and energy utilization.The simulation results of the example show that the MPPT based on the MVO algorithm can collect higher power in a shorter time and achieve the minimum power fluctuation.