A Review of Microgrid Power Quality Disturbance Identification Studies

Junzhuo Jiang; Hong Song

doi:10.62051/ijcsit.v3n2.11

Authors

Junzhuo Jiang
Hong Song

DOI:

https://doi.org/10.62051/ijcsit.v3n2.11

Keywords:

Microgrid, Power quality disturbance, Time-frequency analysis, Artificial intelligence, Deep learning

Abstract

Microgrid is an efficient large-scale distributed generation technology, which is an important component of smart grid gradually replacing the traditional power system. With the wide application of power electronic devices in various fields, the problems of harmonic distortion, voltage sag, and voltage swell of power grid have also arisen, seriously endangering the safety, stability, and normal operation of the power grid. This article provides a comprehensive review of the identification techniques for power quality disturbances in microgrids, covering the methods based on time-frequency domain and image coding with deep learning. The background and current situation of power quality disturbances in microgrids are analyzed in a more comprehensive way, and the advantages and disadvantages of various commonly used analysis methods are compared.

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