Arc Fault Detection and Classification in DC Microgrid Using Deep Neural Network

International Journal of Electronics and Communication Engineering

© 2024 by SSRG - IJECE Journal

Volume 11 Issue 8

Year of Publication : 2024

Authors : Dipti Patil, Bindu S, Sushil Thale

10.14445/23488549/IJECE-V11I8P114

How to Cite?

Dipti Patil, Bindu S, Sushil Thale, "Arc Fault Detection and Classification in DC Microgrid Using Deep Neural Network," SSRG International Journal of Electronics and Communication Engineering, vol. 11,  no. 8, pp. 131-139, 2024. Crossref, https://doi.org/10.14445/23488549/IJECE-V11I8P114

Abstract:

High resistance DC arc faults in the DC microgrid can create serious damage to the microgrid and put the operator's safety in danger. If it is not quickly found and eliminated. Available pattern-based fault identification approaches do not perform as expected due to the nonperiodic nature of arc fault and the presence of multiple switching converters. This research suggests using Wavelet Transform (WT) in conjunction with deep neural networks to detect arc faults in DC microgrids. Multi-Layer Perceptron (MLP)/Dense neural networks and Convolution Neural Networks (CNN) have been employed in this proposed methodology. The MATLAB simulation using the Cassie arc model is developed, and simulation results. According to the simulation results, MLP and CNN have respective arc fault detection accuracies of 95.5% and 96.4%. The result also shows that CNN performs better in various degrees of noisy signal conditions.

Keywords:

Deep neural networks, Fully connected networks, Multi-Layer Perceptron, Convolutional Neural Networks.

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