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 various 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% tracking efficiency. A detailed comparison table is provided by comparing with other popular existing MPPT methodologies.
Highlights
Photovoltaic (PV)-based power generation has gained immense attention over the past few decades
This paper proposes a Bayesian fusion technique (BFT) for tracking global maximum power point (GMPP) under partial shading conditions (PSCs)
There is almost no oscillatory behaviour observed while tracking MPP as compared to incremental conductance, particle swarm optimization (PSO), and Jaya technique indicating the superior performance of the proposed Bayesian fusion technique
Summary
Photovoltaic (PV)-based power generation has gained immense attention over the past few decades. The power extracted from the PV panel is dependent on its output voltage for a given irradiance and temperature. For full utilization of the installed PV panel, a proper maximum power point tracking (MPPT) technique that extracts maximum possible power from PV systems for a particular irradiance and temperature is needed. The PV power plant under partially shaded conditions may exhibit multiple local peaks (MLPs) characteristics which include a global peak (GP) [6]. The conventional MPPT techniques based on the “hill-climbing” algorithm may not guarantee a global peak under these conditions and may be stuck at the local MPP. The multiple global maximum power point tracking (GMPPT) algorithms available in literature can be broadly categorized as (a).
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