Optimization-Based Maximum Power Point Tracking of Photovoltaic Systems Under Partial Shading: Methods, Boost-Converter Pwm Implementation, And Research Directions

Partial shading conditions (PSCs) remain a major challenge in photovoltaic (PV) energy conversion because non-uniform irradiance across modules or substrings can produce multiple local maxima on the power–voltage (P–V) characteristic. Conventional maximum power point tracking (MPPT) methods, including perturb and observe (P&O), hill climbing, and incremental conductance, are attractive because of their simplicity, but their local decision rules may converge to a local maximum rather than the global maximum power point (GMPP). This has motivated the development of optimization-based MPPT methods, including genetic algorithms, differential evolution, particle swarm optimization (PSO), artificial bee colony, cuckoo search, firefly algorithms, grey wolf optimization, whale optimization, and hybrid or learning-based approaches. This review examines these methods from an implementation-oriented perspective, with particular emphasis on PV systems interfaced through a DC–DC boost converter whose pulse-width modulation (PWM) duty cycle is commanded by the MPPT optimizer. A qualitative comparison framework is developed around GMPP identification, MPPT efficiency, convergence time, steady-state oscillation, repeatability, computational cost, dynamic-shading response, and experimental validation. Existing reviews demonstrate substantial algorithmic diversity, but reported results are difficult to rank when studies use different PV arrays, shading patterns, converter parameters, and performance definitions. The review therefore identifies reproducible benchmarking, realistic dynamic-shading evaluation, controller–converter co-design, and hardware validation as priorities for future research. Rather than claiming a universally superior optimizer, the paper provides criteria for selecting and implementing algorithms for reliable global MPPT in boost-converter-based PV systems.