Intelligent Control of Microgrid with Virtual Inertia Using Recurrent Probabilistic Wavelet Fuzzy Neural Network

Kuang Hsiung Tan, Faa Jeng Lin, Cheng Ming Shih, Che Nan Kuo

Research output: Contribution to journalArticlepeer-review

47 Scopus citations

Abstract

A microgrid with virtual inertia using master-slave control is proposed in this article to overcome the drawbacks of traditional inverter-based distributed generators for lack of inertia and without grid-forming capability. The microgrid using master-slave control is composed of a storage system, a photovoltaic (PV) system and a varying resistive three-phase load. The storage system and PV system are regarded as the master unit and the slave unit, respectively, in the microgrid. Moreover, in order to improve the reactive power control in grid-connected mode and the transient response of microgrid during the switching between the grid-connected mode and islanding mode, an online trained recurrent probabilistic wavelet fuzzy neural network (RPWFNN) is proposed to replace the conventional proportional-integral (PI) controller in the storage system. Furthermore, when the microgrid is operated in islanding mode, the load variation will have serious influence on the voltage control of the microgrid. Thus, the RPWFNN control is also proposed to improve the transient and steady-state responses of voltage control in the microgrid. Finally, according to some experimental results, excellent control performance of the microgrid with virtual inertia using the proposed intelligent controller can be achieved.

Original languageEnglish
Article number8907405
Pages (from-to)7451-7464
Number of pages14
JournalIEEE Transactions on Power Electronics
Volume35
Issue number7
DOIs
StatePublished - Jul 2020

Keywords

  • Fuzzy neural network
  • grid-forming
  • microgrid
  • virtual inertia

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