Fault Detection and Classification Based on Co-training of Semisupervised Machine Learning

• Published in: IEEE transactions on industrial electronics (1982) Vol. 65; no. 2; pp. 1595 - 1605
• Main Authors: Abdelgayed, Tamer S., Morsi, Walid G., Sidhu, Tarlochan S.
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.02.2018; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Artificial intelligence; Classification; Classifiers; Computer simulation; Data models; Discrete Wavelet Transform; Discrete wavelet transforms; Electric power grids; Estimation; Fault detection; Fault diagnosis; fault protection; Feature extraction; Machine learning; Parameter identification; pattern recognition; Search algorithms; Semi-supervised learning; semisupervised learning; Support vector machines; Training; Waveforms; Wavelet transforms
• ISSN: 0278-0046; 1557-9948
• DOI: 10.1109/TIE.2017.2726961

This paper presents a semisupervised machine learning approach based on co-training of two classifiers for fault classification in both the transmission and the distribution systems with consideration of microgrids. Unlike previous work in which only labeled data are treated using supervised machine learning approaches, this study uses a semisupervised machine learning approach to handle both labeled and unlabeled data. In order to extract the hidden features in the current and voltage waveforms, the discrete wavelet transform is applied, while the harmony search algorithm is utilized to identify the optimal parameters of the wavelets. The performance of the proposed method was examined on both transmission and distribution test systems in a simulation environment, and also using experimental hardware. The results have shown that the proposed approach provides flexibility and adaptability in dealing with various system conditions/configurations with high accuracy. The results also have demonstrated that the proposed semisupervised approach can improve the fault classification accuracy compared to that obtained using other machine learning approaches (i.e., supervised and unsupervised) in the case of utilizing unlabeled data to build and train the classifier's model.