Maximum Power Point Tracking Techniques for Solar PV Systems: Performance Analysis of Different Control Strategies

Solar photovoltaic output is nonlinear and changes with irradiance, temperature, load, and partial shading, so a maximum power point tracking controller must balance speed, energy capture, ripple, and implementation burden. An evidence-weighted performance analysis of 16 documents supplied, from 15 different studies, after duplicate screening, is presented in this paper. Without mixing incompatible power ratings or test profiles, conventional and adaptive methods, global-search and hybrid optimizers, prediction-based controllers and reinforcement learning are compared. The evidence clearly demonstrates that under uniform conditions simple local procedures are still appealing, while partial shading needs to be explicitly addressed by including the global-search or regime-switching features in the procedure. Hybrid controllers, which divide fast local regulation and global search, reported the best hardware supported results and methods that learned were promising with respect to adaptation but had to deal with training and validation costs and with computational costs. The following information is introduced: a control-evidence matrix, an all-source synthesis table, a validation-maturity assessment, and a minimum reporting framework. The main results are that controller selection should be condition and evidence aware: there is no single best controller and only numerical performance makes sense in a defined array, converter, disturbance profile, metric definition and validation platform.