Fault Classification with Convolutional Neural Networks for Microgrid Systems

The microgrid (MG) networks require adaptive and rapid fault classification mechanisms due to their insufficient kinetic energy reserve and dynamic response of power electronic converters of distributed generation (DG) systems. To achieve this requirement, this study explores the issues in standalon...

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Bibliographic Details
Published in:International transactions on electrical energy systems 2022-04, Vol.2022, p.1-21
Main Authors: Pan, Prateem, Mandal, Rajib Kumar, Rahman Redoy Akanda, Md. Mojibur
Format: Article
Language:English
Subjects:
Accuracy
Algorithms
Artificial neural networks
Classification
Distributed generation
Dynamic response
Energy reserves
Fourier transforms
Gaussian process
Kinetic energy
Machine learning
Neural networks
Random noise
Real time operation
Signal processing
Wavelet transforms
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Summary:The microgrid (MG) networks require adaptive and rapid fault classification mechanisms due to their insufficient kinetic energy reserve and dynamic response of power electronic converters of distributed generation (DG) systems. To achieve this requirement, this study explores the issues in standalone (SA) and grid-connected (GC) operating modes of MG and develops a near-real-time intelligent disturbance detection and protective solutions for their stable operation. In the proposed approach, an intelligent fault classification mechanism is developed using the advantages of wavelet transform and convolutional neural networks (CNNs). Initially, the voltage and current outcomes for each and every possible fault in the MG network are identified and the wavelet transforms are applied for preprocessing and image conversion. The converted images are identified as scalograms which are further trained with the CNNs. To assess the development of the proposed approach, the IEEE 13 bus system is considered for data gathering. To replicate the real-time behavior of the MG network, the additive white Gaussian noise (AWGN) and additive impulsive Gaussian noise (AIGN) are injected at various levels during the classifier development process. The trained classifier has an average training accuracy of 99.1% for SA MG and 97.7% for GC MG, and the average testing accuracies are 98.9% for SA MG and 97.1% for GC MG.
ISSN:2050-7038
2050-7038
DOI:10.1155/2022/8431450