Deep-Learning Based Fault Events Analysis in Power Systems

The identification of fault types and their locations is crucial for power system protection/operation when a fault occurs in the lines. In general, this involves a human-in-the-loop analysis to capture the transient voltage and current signals using a common format for transient data exchange for p...

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Bibliographic Details
Published in:Energies (Basel) 2022-08, Vol.15 (15), p.5539
Main Authors: Hong, Junho, Kim, Yong-Hwa, Nhung-Nguyen, Hong, Kwon, Jaerock, Lee, Hyojong
Format: Article
Language:English
Subjects:
Accuracy
Algorithms
Classification
convolutional neural networks
Data processing
fault line location identification
Fault lines
Machine learning
Neural networks
power systems fault classification
Signal processing
Support vector machines
Wavelet transforms
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Summary:The identification of fault types and their locations is crucial for power system protection/operation when a fault occurs in the lines. In general, this involves a human-in-the-loop analysis to capture the transient voltage and current signals using a common format for transient data exchange for power systems (COMTRADE) file. Then, protection engineers can identify the fault types and the line locations after the incident. This paper proposes intelligent and novel methods of faulty line and location detection based on convolutional neural networks in the power system. The three-phase fault information contained in the COMTRADE file is converted to an image file and extracted adaptively by the proposed CNN, which is trained by a large number of images under various kinds of fault conditions and factors. A 500 kV power system is simulated to generate different types of electromagnetic fault transients. The test results show that the proposed CNN-based analyzer can classify the fault types and locations under various conditions and reduce the fault analysis efforts.
ISSN:1996-1073
1996-1073
DOI:10.3390/en15155539