Fault Classification in GCPV Microgrid System Using Wavelet Transform

Authors

  • Mohamad Hakimi Zullkuffli Universiti Tun Hussein Onn Malaysia Author
  • Faridah Hanim Mohd Nor Universiti Tun Hussein Onn Malaysia Author

Keywords:

Grid-connected photovoltaic (GCPV) microgrids, Maximum power extraction, Fault Detection, Wavelet transform techniques, 3-phase voltage analysis, Solar PV array voltage, Fault classification and identification

Abstract

Grid-connected photovoltaic (GCPV) microgrids have emerged as a promising alternative to conventional energy sources. To ensure efficient operation, maximum power needs to be extracted from the PV system and 3-phase grid system. PV panels are often connected in series and parallel combinations, forming an array to meet load demands. However, the system can experience various faults, such as grid faults, PV system faults, and grid faults with the PV system in normal conditions, leading to significant reductions in maximum power generation and affecting the load. Wavelet transform techniques are employed to detect damage events by analyzing 3-phase voltage and solar PV array voltage. Wavelet decomposition using 'wavedec' and detailed coefficient extraction using 'detcoef' enable fault classification and identification. The wavelet coefficients can identify all faults, allowing for early isolation of damage locations.

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Published

25-11-2024

Issue

Section

Electrical, Electronics, and Energy

How to Cite

ZULLKUFFLI, M. H., & binti Mohd Noh, F. H. (2024). Fault Classification in GCPV Microgrid System Using Wavelet Transform. Progress in Engineering Application and Technology, 5(2), 178-187. https://publisher.uthm.edu.my/periodicals/index.php/peat/article/view/17185