Reactive power control of three-phase grid-connected PV system during grid faults using takagi-sugeno-kang probabilistic fuzzy neural network control

Faa Jeng Lin, Kuang Chin Lu, Ting Han Ke, Bo Hui Yang, Yung Ruei Chang

Research output: Contribution to journalArticlepeer-review

105 Scopus citations

Abstract

An intelligent controller based on the Takagi-Sugeno-Kang-type probabilistic fuzzy neural network with an asymmetric membership function (TSKPFNN-AMF) is developed in this paper for the reactive and active power control of a three-phase grid-connected photovoltaic (PV) system during grid faults. The inverter of the three-phase grid-connected PV system should provide a proper ratio of reactive power to meet the low-voltage ride through (LVRT) regulations and control the output current without exceeding the maximum current limit simultaneously during grid faults. Therefore, the proposed intelligent controller regulates the value of reactive power to a new reference value, which complies with the regulations of LVRT under grid faults. Moreover, a dual-mode operation control method of the converter and inverter of the three-phase grid-connected PV system is designed to eliminate the fluctuation of dc-link bus voltage under grid faults. Furthermore, the network structure, the online learning algorithm, and the convergence analysis of the TSKPFNN-AMF are described in detail. Finally, some experimental results are illustrated to show the effectiveness of the proposed control for the three-phase grid-connected PV system.

Original languageEnglish
Article number7051250
Pages (from-to)5516-5528
Number of pages13
JournalIEEE Transactions on Industrial Electronics
Volume62
Issue number9
DOIs
StatePublished - 1 Sep 2015

Keywords

  • Asymmetric membership function (AMF)
  • Takagi-Sugeno-Kang type probabilistic fuzzy neural network (TSKPFNN)
  • grid faults
  • low voltage ride through (LVRT)
  • photovoltaic system
  • reactive power control

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