Confidence-driven adaptive operating-point optimization for photovoltaic systems under partial shading conditions

Abstract

Partial shading conditions (PSC) generate highly nonlinear multi-peak photo voltaic (PV) characteristics, complicating reliable global maximum power point tracking (GMPP). Although numerous intelligent optimization techniques exist, most rely on extensive exploration mechanisms that limit their applicability in embedded real-time controllers. This paper introduces a confidence-driven adaptive maximum power point tracking (MPPT) framework in which the optimization search space is dynamically regulated according to the reliability of a predictive operating-region estimator. Unlike standard artificial neural network (ANN)-assisted MPPT strategies that offer only power prediction, the proposed approach exploits a confidence index to continuously contract or expand the exploration domain, minimizing search effort while preserving global tracking capability. The framework was developed using real measurements from the PV DAQ database and validated through a nonlinear two-diode thermal-electrical PV model incorporating irradiance mismatch and temperature-dependent effects. Static and dynamic PSC scenarios were investigated to evaluate convergence behavior and computational performance. Experimental results demonstrate that the proposed confidence-governed strategy achieves an average tracking efficiency of 90.89%, reduces convergence effort through adaptive search-space contraction, and matches real measurements with an R2 value of 0.8664, offering a low-complexity solution for real-time embedded PV energy management.