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Fault Location in Transmission Line Through Deep Learning—A Systematic Review

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Inventive Systems and Control

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 204))

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Abstract

In a power system, transient stability is very essential. Huge disturbances such as faults in the transmission line need to be separated as rapidly as probable to replace transient stability. In a transmission network, the faulty voltage regulator along with current signals is used for fault location, classification, and detection. Detecting the location of the fault on transmission lines precisely can save the effort of labor and enhance the restoration and repairing process essentially. On transmission lines, accurate pinpointing reduces labor costs and outage time. The location of the fault is independent of resistance of fault and the approach does not need any knowledge of source impedance. Relay predicts an unusual sign and then the breaker of the circuit separates the transmission line which is unhealthy from the remaining health system. This study uses a systematic and explicit technique of deep learning algorithms to determine the location of faults in the transmission line. Deep learning which offers a hierarchy of feature which can study experiences and identify raw data automatically as the human brain performs. It shows greater importance to solve the issues of location in power transmission systems. The deep learning algorithms train neural networks effectively and hinder fundamental issues in overfitting. Deep learning training depends mainly on measurements of electricity. Since deep learning is independent of factors of system namely parameters of line and topology which will have the essential prospect of the application when it is used in the location of the fault. Extensive researches have explained the efficiency and accuracy of deep learning approaches based on fault location in transmission lines.

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Authors and Affiliations

  1. Department of Electrical and Electronic Engineering, A.M.K. Technological Polytechnic College, Chennai to Bangalore Highway, Sembarambakkam, Chennai, 600123, India

    Ormila Kanagasabapathy

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  1. Ormila Kanagasabapathy
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Editors and Affiliations

  1. Research and Industry Incubation Center, Dayananda Sagar College of Engineering, Bengaluru, Karnataka, India

    V. Suma

  2. Department of Electrical Engineering, Dayeh University, Changhua, Taiwan

    Joy Iong-Zong Chen

  3. School of Information Technology, Deakin University, Geelong, VIC, Australia

    Zubair Baig

  4. GoPerception Laboratory, Cornell University, Ithaca, NY, USA

    Haoxiang Wang

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Kanagasabapathy, O. (2021). Fault Location in Transmission Line Through Deep Learning—A Systematic Review. In: Suma, V., Chen, J.IZ., Baig, Z., Wang, H. (eds) Inventive Systems and Control. Lecture Notes in Networks and Systems, vol 204. Springer, Singapore. https://doi.org/10.1007/978-981-16-1395-1_18

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