Deep-Learning-Based Earth Fault Detection Using Continuous Wavelet Transform and Convolutional Neural Network in Resonant Grounding Distribution Systems

• Published in: IEEE sensors journal Vol. 18; no. 3; pp. 1291 - 1300
• Main Authors: Guo, Mou-Fa, Zeng, Xiao-Dan, Chen, Duan-Yu, Yang, Nien-Che
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.02.2018; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Artificial neural networks; CAD; Computer aided design; Computer simulation; Continuous wavelet transform; Continuous wavelet transforms; convolutional neural network (CNN); Deep learning; Distribution systems; Fault detection; faulty feeder detection; Feature extraction; Feeders; Gray scale; Image acquisition; Machine learning; Neural networks; System effectiveness; Time-frequency analysis; Transient analysis; wavelet transform; Wavelet transforms; Zero sequence current
• DOI: 10.1109/JSEN.2017.2776238
• ISSN: 1530-437X

Feature extraction for fault signals is critical and difficult in all kinds of fault detection schemes. A novel simple and effective method of faulty feeder detection in resonant grounding distribution systems based on the continuous wavelet transform (CWT) and convolutional neural network (CNN) is presented in this paper. The time-frequency gray scale images are acquired by applying the CWT to the collected transient zero-sequence current signals of the faulty feeder and sound feeders. The features of the gray scale image will be extracted adaptively by the CNN, which is trained by a large number of gray scale images under various kinds of fault conditions and factors. The features extraction and the faulty feeder detection can be implemented by the trained CNN simultaneously. As a comparison, two faulty feeder detection methods based on artificial feature extraction and traditional machine learning are introduced. A practical resonant grounding distribution system is simulated in power systems computer aided design/electromagnetic transients including DC, the effectiveness and performance of the proposed faulty feeder detection method is compared and verified under different fault circumstances.