A Bayesian fusion technique for maximum power point tracking under partial shading condition

Abstract

In this paper, a Bayesian fusion technique (BFT) based on maximum power point tracking (MPPT) is developed for the photovoltaic (PV) system that can exhibit faster and accurate tracking under partially shaded conditions (PSCs). Although the conventional hill-climbing algorithms have fast tracking capabilities, they are prone to steady-state oscillations and may not guarantee global peak under partially shaded conditions. Contrarily, the meta-heuristic-based techniques may promise a global peak solution, but they are computationally inefficient and take significant time for tracking. To address this problem, a BFT is proposed which combines the solutions obtained from conventional incremental conductance algorithm and Jaya optimization algorithm to produce better responses under various PSCs. The effectiveness of the proposed BFT-based MPPT is evaluated by comparing it with various MPPT methods, viz. incremental conductance, particle swarm optimization (PSO), and Jaya optimization algorithms in MATLAB/Simulink environment. From the vari ous case studies carried, the overall average tracking speed with more than 99% accuracy is less than 0.25 s and having minimum steady-state oscillations. Even under the wide range of partially shaded conditions, the proposed method exhibited superior MPPT compared to the existing methods with tracking speed less than 0.1 s to achieve 99.8% track ing efficiency. A detailed comparison table is provided by comparing with other popular existing MPPT methodologies.