A hybrid global maximum power point tracking method for photovoltaic arrays under partial shading conditions

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

16 Scopus Citations
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Detail(s)

Original languageEnglish
Pages (from-to)665-674
Journal / PublicationOptik
Volume180
Online published1 Dec 2018
Publication statusPublished - Feb 2019

Abstract

A novel global maximum power point tracking (GMPPT) method based on hybrid Gaussian process regression-Jaya (GPR-Jaya) algorithm is proposed for photovoltaic (PV) arrays under partial shading conditions. The newly developed swarm-based Jaya algorithm does not require algorithm-specific controlling parameters. This character makes Jaya an attractive stochastic optimization tool. To improve the MPPT performance with Jaya algorithm, a GPR model is introduced into the iterative updates of candidate solutions (operating voltages for the PV system). The GPR model serves as a predictor of PV power generations. Candidate solutions failing to improve PV power generations judged by the GPR model will be discarded during the iterative updates. Such extension can reduce worse updates. The effectiveness and efficiency of the proposed method is validated by comprehensive simulation study. Simulation results demonstrate that the GPR-Jaya based MPPT method outperforms benchmarking methods including standard Jaya algorithm and particle swarm optimization (PSO) algorithm based MPPT methods in terms of convergence speed and dynamical efficiency.

Research Area(s)

  • Gaussian process regression, Global maximum power point tracking, Jaya algorithm, Photovoltaic array