Solar Photovoltaic (PV) systems are playing a major role in the present electrical energy systems. The solar PV gives nonlinear I–V and P–V characteristics. As a result, it is difficult to extract the maximum power of the solar PV. Under Partial Shading Conditions (PSCs), the solar PV characteristics consist of multiple local Maximum Power Points (MPPs) and one global MPP. The classical Maximum Power Point Tracking (MPPT) techniques cannot track the global MPP under PSCs. Accordingly, this work aims to study the performance of five soft computing MPPT techniques. The studied five soft computing MPPT techniques are Modified Variable Step Size-Radial Basis Functional Network (MVSS-RBFN), Modified Hill-Climb with Fuzzy Logic Controller (MHC-FLC), Artificial Neuro-Fuzzy Inference System (ANFIS), Perturb and Observe with Practical Swarm Optimization (P&O-PSO), and Adaptive Cuckoo Search (ACS). The comparative performance analysis of five soft computing techniques has been carried out against the Variable Step Size-Incremental Resistance (VSS-INR), and Variable Step Size-Feedback Controller (VSS-FC)-based MPPT techniques. The performance analysis of seven MPPT techniques has been done by considering the parameters are steady-state settling time, MPP tracking speed, algorithm complexity, PV array dependency, handling of partial shading, and efficiency.

太阳能光伏（PV）系统在当前的电能系统中扮演着重要角色。太阳能PV具有非线性的I–V和P–V特性。结果，难以提取太阳能PV的最大功率。在部分阴影条件（PSC）下，太阳能PV特性由多个局部最大功率点（MPP）和一个全局MPP组成。经典的最大功率点跟踪（MPPT）技术无法在PSC下跟踪全局MPP。因此，这项工作旨在研究五种软计算MPPT技术的性能。研究了五种软计算MPPT技术：变步长径向基函数网络（MVSS-RBFN），带模糊逻辑控制器的爬山（MHC-FLC），人工神经模糊推理系统（ANFIS），利用实用群优化（P＆O-PSO）和自适应杜鹃搜索（ACS）进行摄动和观察。针对可变步长增量电阻（VSS-INR）和基于可变步长反馈控制器（VSS-FC）的MPPT技术，对五种软计算技术进行了比较性能分析。通过考虑以下参数进行了7种MPPT技术的性能分析：稳态建立时间，MPP跟踪速度，算法复杂性，PV阵列依赖性，部分阴影处理和效率。