Deep-Learning Based Fault Events Analysis in Power Systems

Junho Hong, Yong-Hwa Kim, Hong Nhung-Nguyen, Jaerock Kwon and Hyojong Lee
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Junho Hong: Department of Electrical and Computer Engineering, University of Michigan-Dearborn, Dearborn, MI 48128, USA
Yong-Hwa Kim: Department of Data Science, Korea National University of Transportation, Uiwang-si 16106, Korea
Hong Nhung-Nguyen: Department of Electronic Engineering, Myongji University, Yongin-si 17508, Korea
Jaerock Kwon: Department of Electrical and Computer Engineering, University of Michigan-Dearborn, Dearborn, MI 48128, USA
Hyojong Lee: Hitachi Energy, Raleigh, NC 27606, USA

Energies, 2022, vol. 15, issue 15, 1-16

Abstract: 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.

Keywords: convolutional neural networks; power systems fault classification; fault line location identification (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
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