A Prediction Model-Guided Jaya Algorithm for the PV System Maximum Power Point Tracking

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

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Detail(s)

Original languageEnglish
Pages (from-to)45-55
Journal / PublicationIEEE Transactions on Sustainable Energy
Volume9
Issue number1
Online published12 Jun 2017
Publication statusPublished - Jan 2018

Abstract

This paper proposes a novel model-free solution algorithm, the natural cubic spline guided Jaya algorithm (S-Jaya), for efficiently solving the maximum power point tracking (MPPT) problem of PV systems under partial shading conditions. A PV system which controls the power generation with its operating voltage is considered. As the same as the generic Jaya algorithm, the S-Jaya is free of algorithm-specific parameters. A natural cubic spline based prediction model is incorporated into the iterative search process to guide the update of candidate solutions (operating voltage settings) in the S-Jaya and such extension is capable of improving the tracking performance. Simulation studies and experiments are conducted to validate the effectiveness of the proposed S-Jaya algorithm for better addressing PV MPPT problems considering a variety of partial shading conditions. The performance of the proposed algorithm is benchmarked against the generic Jaya and the particle swarm optimization (PSO), which has been widely considered in the model-free MPPT, to demonstrate its advantages. Results of simulation studies and experiments demonstrate that the S-Jaya algorithm converges faster and provides a higher overall tracking efficiency.

Research Area(s)

  • Algorithm design and analysis, Heuristic search, Jaya algorithm, Maximum power point trackers, maximum power point tracking, Modeling, partial shading conditions, photovoltaic system, Prediction algorithms, Predictive models, Splines (mathematics)

Citation Format(s)