Probabilistic Wavelet Fuzzy Neural Network based reactive power control for grid-connected three-phase PV system during grid faults

Faa Jeng Lin, Kuang Chin Lu, Ting Han Ke

Research output: Contribution to journalArticlepeer-review

37 Scopus citations

Abstract

This study presents a reactive power controller using Probabilistic Wavelet Fuzzy Neural Network (PWFNN) for grid-connected three-phase PhotoVoltaic (PV) system during grid faults. The controller also considers the ratio of the injected reactive current to meet the Low Voltage Ride Through (LVRT) regulation. Moreover, the balance of the active power between the PV panel and the grid-connected inverter during grid faults is controlled by the dc-link bus voltage. Furthermore, to reduce the risk of over-current during LVRT operation, a current limit is predefined for the injection of reactive current. The main contribution of this study is the introduction of the PWFNN controller for reactive and active power control that provides LVRT operation with power balance under various grid fault conditions. Finally, some experimental tests are realized to validate the effectiveness of the proposed controller.

Original languageEnglish
Pages (from-to)437-449
Number of pages13
JournalRenewable Energy
Volume92
DOIs
StatePublished - 1 Jul 2016

Keywords

  • Grid faults
  • Low voltage ride through
  • Maximum power point tracking
  • Photovoltaic system
  • Probabilistic wavelet fuzzy neural network

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