To get the maximum power point (MPP), the algorithms developed in the literature fail for the most part when the atmospheric conditions vary rapidly. This paper presents the optimization by the swarm particles (PSO) of a sliding mode controller (SMC) to optimize photovoltaic systems (PV) evolving under the conditions of a rapid variation of the sunshine. The stochastic PSO algorithm is used in particular to determine the optimal gains of the sliding mode controller, and this algorithm guarantees the stability and global fast convergence of the system. Efficiency of the proposed hybrid algorithm is proved by using a Solarex MSX-60 module connected to a step-up DC-DC converter supplying a resistive load. The performances are compared to the conventional perturb and observe technique (P&O), PSO, CUCKOO, grey wolf optimization (GWO), and Whale Optimization Algorithm (WOA) in terms of convergence speed, i.e., 0.05 s, tracking speed after extreme change in irradiance 0.03 s, high Ability to track large changes in power before 1 s. Simulation results show that this technique is effective in conditions of a rapid variation of the irradiance.

中文翻译：

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在快速变化的大气条件下改进基于鲁棒滑模 MPPT 控制器的 PSO 算法的光伏系统动态响应

为了获得最大功率点 (MPP)，当大气条件快速变化时，文献中开发的算法大部分都失败了。本文介绍了通过滑模控制器 (SMC) 的群粒子 (PSO) 进行优化，以优化在阳光快速变化条件下演化的光伏系统 (PV)。特别使用随机PSO算法来确定滑模控制器的最优增益，该算法保证了系统的稳定性和全局快速收敛。通过使用 Solarex MSX-60 模块连接到提供电阻负载的升压 DC-DC 转换器，证明了所提出的混合算法的效率。将性能与传统的扰动观察技术 (P&O)、PSO、CUCKOO、灰狼优化 (GWO)、而Whale Optimization Algorithm (WOA)在收敛速度方面，即0.05 s，辐照度极端变化后的跟踪速度为0.03 s，在1 s前能够跟踪功率大的变化。仿真结果表明，该技术在辐照度快速变化的条件下是有效的。