Maximum power point (MPP) tracking (MPPT) is a crucial aspect of photovoltaic (PV) technology for operating in an optimum location throughout the day. The bypass diodes are connected across series connected PV modules to avoid the hotspot phenomenon resulting in multiple peaks in the power-voltage (P-V) curve during partial shading conditions. Under this situation, tracking of global MPP (GMPP) for PV system by conventional approach is incompetent. Thus, a global MPPT controller is designed based on a novel population based algorithm, student psychology based optimization (SPBO), to enhance the overall performance of the 4S and the 3S configurations of the PV array. The effectiveness and the feasibility of SPBO algorithm for catching the GMPP are verified under several shadow arrangements. For proving effectiveness, the simulation results of SPBO are compared with human behaviour based optimization, improved chaotic particle swarm optimization (PSO), PSO, fuzzy logic control and teaching–learning based optimization. The proposed SPBO algorithm is able to successfully catch the GMPP under different weather scenarios and exhibits superior performance in terms of iteration, tracking time and efficiency. However, the temperature variation marginally affects the tracking efficiency of the SPBO method. This analysis is performed on the 4S configuration of the PV array. For better justification of the concept of stability, statistical analysis is also conducted on the 3S configuration of PV array separately.

最大功率点 (MPP) 跟踪 (MPPT) 是光伏 (PV) 技术的一个关键方面，可全天在最佳位置运行。旁路二极管连接在串联的光伏组件之间，以避免热点现象导致电源电压（PV) 部分阴影条件下的曲线。在这种情况下，传统方法对光伏系统的全局MPP（GMPP）进行跟踪是无能为力的。因此，基于新的基于人口的算法、基于学生心理的优化 (SPBO) 设计了全局 MPPT 控制器，以提高光伏阵列 4S 和 3S 配置的整体性能。在几种影子安排下验证了SPBO算法捕获GMPP的有效性和可行性。为了证明有效性，将 SPBO 的仿真结果与基于人类行为的优化、改进的混沌粒子群优化 (PSO)、PSO、模糊逻辑控制和基于教学的优化进行了比较。所提出的SPBO算法能够在不同天气场景下成功捕获GMPP，并在迭代、跟踪时间和效率方面表现出优异的性能。然而，温度变化对 SPBO 方法的跟踪效率影响很小。该分析是在光伏阵列的 4S 配置上进行的。为了更好地论证稳定性概念，还分别对光伏阵列的3S配置进行了统计分析。