Quasi-Z-source inverter-based photovoltaic generation system with maximum power tracking control using ANFIS

Haitham Abu-Rub, Atif Iqbal, Sk Moin Ahmed, Fang Z. Peng, Yuan Li, Ge Baoming

Research output: Contribution to journalArticle

147 Citations (Scopus)

Abstract

The paper proposes an artificial-intelligence-based solution to interface and deliver maximum power from a photovoltaic (PV) power generating system in standalone operation. The interface between the PV dc source and the load is accomplished by a quasi-Z-source inverter (qZSI). The maximum power delivery to the load is ensured by an adaptive neuro-fuzzy inference system (ANFIS) based on maximum power point tracking (MPPT). The proposed ANFIS-based MPPT offers an extremely fast dynamic response with high accuracy. The closed-loop control of the qZSI regulates the shoot through duty ratio and the modulation index to effectively control the injected power and maintain the stringent voltage, current, and frequency conditions. The proposed technique is tested for isolated load conditions. Simulation and experimental approaches are used to validate the proposed scheme.

Original languageEnglish
Article number6205354
Pages (from-to)11-20
Number of pages10
JournalIEEE Transactions on Sustainable Energy
Volume4
Issue number1
DOIs
Publication statusPublished - 1 Jan 2013

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Keywords

  • DC-AC power conversion
  • nonshoot-through state
  • pulsewidth modulated inverters
  • quasi-Z-source inverter (qZSI)
  • shoot-through state
  • solar power generation

ASJC Scopus subject areas

  • Renewable Energy, Sustainability and the Environment

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