This article introduces a novel strategy for determining the optimal control parameters of particle swarm optimization (PSO) for the shortest convergence time and lowest failure rate of photovoltaic (PV) maximum power point tracker (MPPT) systems. This strategy is used offline to determine these parameters and then the control system uses them in the online MPPT. The strategy uses two nested particle swarm optimization (NESTPSO) search loops: the inner one involves the PV system and the outer one uses the inner PSO as a fitness function. The control parameters and swarm size of the inner PSO loop are used as optimization variables in the outer PSO loop. This strategy can be used not only for PSO but also for all other optimization techniques. The simulation and experimental results obtained using the NESTPSO strategy show a great reduction of 77–681% in convergence time and failure rate compared to 10 benchmark strategies, proving the superiority of this technique.

本文介绍了一种新的策略，用于确定粒子群优化 (PSO) 的最优控制参数，以实现光伏 (PV) 最大功率点跟踪器 (MPPT) 系统的最短收敛时间和最低故障率。该策略用于离线确定这些参数，然后控制系统在在线 MPPT 中使用它们。该策略使用两个嵌套粒子群优化 (NESTPSO) 搜索循环：内部一个涉及 PV 系统，外部一个使用内部 PSO 作为适应度函数。内部 PSO 循环的控制参数和群体大小用作外部 PSO 循环中的优化变量。该策略不仅可以用于 PSO，还可以用于所有其他优化技术。