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A High Performance PSO-Based Global MPP Tracker for a PV Power Generation System

Author

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  • Kuei-Hsiang Chao

    (Department of Electrical Engineering, National Chin-Yi University of Technology, Taichung 41170, Taiwan)

Abstract

This paper aims to present an improved version of a typical particle swarm optimization (PSO) algorithm, such that the global maximum power point (MPP) on a P-V characteristic curve with multiple peaks can be located in an efficient and precise manner for a photovoltaic module array. A series of instrumental measurements are conducted on variously configured arrays built with SANYO HIP2717 PV modules, either unshaded, partially shaded, or malfunctioning, as the building blocks. There appear two, triple and quadruple peaks on the corresponding P-V characteristic curves. Subsequently, the tracking performance comparisons, made by some practical experiments, indicate the superiority of this improved MPP tracking algorithm over the typical one.

Suggested Citation

  • Kuei-Hsiang Chao, 2015. "A High Performance PSO-Based Global MPP Tracker for a PV Power Generation System," Energies, MDPI, vol. 8(7), pages 1-18, July.
    • Handle: RePEc:gam:jeners:v:8:y:2015:i:7:p:6841-6858:d:52204
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    References listed on IDEAS

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    1. Héctor Zazo & Esteban Del Castillo & Jean François Reynaud & Ramon Leyva, 2012. "MPPT for Photovoltaic Modules via Newton-Like Extremum Seeking Control," Energies, MDPI, vol. 5(8), pages 1-15, July.
    2. Her-Terng Yau & Chen-Han Wu, 2011. "Comparison of Extremum-Seeking Control Techniques for Maximum Power Point Tracking in Photovoltaic Systems," Energies, MDPI, vol. 4(12), pages 1-16, December.
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    Cited by:

    1. Sergio Ignacio Serna-Garcés & Daniel Gonzalez Montoya & Carlos Andres Ramos-Paja, 2016. "Sliding-Mode Control of a Charger/Discharger DC/DC Converter for DC-Bus Regulation in Renewable Power Systems," Energies, MDPI, vol. 9(4), pages 1-27, March.
    2. Chendi Li & Yuanrui Chen & Dongbao Zhou & Junfeng Liu & Jun Zeng, 2016. "A High-Performance Adaptive Incremental Conductance MPPT Algorithm for Photovoltaic Systems," Energies, MDPI, vol. 9(4), pages 1-17, April.
    3. Sheik Mohammed, S. & Devaraj, D. & Imthias Ahamed, T.P., 2016. "A novel hybrid Maximum Power Point Tracking Technique using Perturb & Observe algorithm and Learning Automata for solar PV system," Energy, Elsevier, vol. 112(C), pages 1096-1106.

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