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Application of machine learning methods in fault detection and classification of power transmission lines: a survey

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Abstract

The rising development of power systems and smart grids calls for advanced fault diagnosis techniques to prevent undesired interruptions and expenses. One of the most important part of such systems is transmission lines. This paper presents a survey on recent machine learning-based techniques for fault detection, classification, and location estimation in transmission lines. In order to provide reliable and resilient electrical power energy, faster and more accurate fault identification tools are required. Costly consequences of probable faults motivate the need for immediate actions to detect them using intelligent methods, especially emerging machine learning approaches that are powerful in solving diagnosis problems. This paper presents a comprehensive review of various machine learning methodologies including naive Bayesian classifier, decision tree, random forest, k-nearest neighbor, and support vector machine as well as artificial neural networks such as feedforward neural network, convolutional neural network, and adaptive neuro-fuzzy inference system that have been used to detect, classify, and locate faults in transmission lines.

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  1. Helen and John C. Hartmann Department of Electrical and Computer Engineering, New Jersey Institute of Technology, Dr Martin Luther King Jr Blvd, Newark, NJ, 07102, USA

    Fatemeh Mohammadi Shakiba, S. Mohsen Azizi & Mengchu Zhou

  2. School of Applied Engineering and Technology, New Jersey Institute of Technology, Dr Martin Luther King Jr Blvd, Newark, NJ, 07102, USA

    S. Mohsen Azizi

  3. K. A. CARE Energy Research and Innovation Center, King Abdulaziz University, Jeddah, 21589, Saudi Arabia

    Abdullah Abusorrah

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  1. Fatemeh Mohammadi Shakiba
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Shakiba, F.M., Azizi, S.M., Zhou, M. et al. Application of machine learning methods in fault detection and classification of power transmission lines: a survey. Artif Intell Rev 56, 5799–5836 (2023). https://doi.org/10.1007/s10462-022-10296-0

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