Power drop guided incremental conductance MPPT algorithm for ladder configured PV systems to enhance maximum power and tracking efficiency

Abstract Partial shading conditions (PSCs) introduce multiple local maximum power points (LMPPs) in photovoltaic (PV) arrays, causing conventional maximum power point tracking (MPPT) methods to converge to suboptimal operating points and reduce energy harvesting. This paper proposes a power drop guided incremental conductance (PDG-INC) MPPT algorithm that combines event-triggered stochastic duty-cycle relocation with deterministic incremental conductance (INC) refinement for improved maximum power point tracking under partial shading conditions. The proposed controller is evaluated through MATLAB/Simulink simulations using a 3.97 kW 8 × 8 ladder-configured PV array under nine representative PSCs and experimentally evaluated under individually applied PSCs using a laboratory-scale hardware prototype comprising a Chroma 62050H-600S PV simulator, TI DSP (F28379D), and a boost DC-DC converter. Performance is assessed using tracking efficiency, energy efficiency, transient response, ripple characteristics, and duty-cycle stability. For comparative evaluation, the proposed PDG-INC algorithm is compared with the step modified incremental conductance (SM-INC) and trap escape (TE) algorithms, while simulated annealing (SA) and the conventional perturb and observe (P&O) algorithm are employed as benchmark MPPT methods under the same investigated PSCs within the respective simulation and experimental evaluations. The proposed PDG-INC algorithm achieved a 99.89% average tracking efficiency and 98.97% average energy efficiency in simulation, while the experimental prototype achieves an average tracking efficiency of 99.37%. Overall, the simulation results demonstrate high tracking performance across the investigated PSCs, while the individual hardware tests support stable real-time operation and high average experimental tracking efficiency under laboratory-scale conditions.