TY - JOUR
T1 - Application of machine learning methods in fault detection and classification of power transmission lines
T2 - a survey
AU - Shakiba, Fatemeh Mohammadi
AU - Azizi, S. Mohsen
AU - Zhou, Mengchu
AU - Abusorrah, Abdullah
N1 - Publisher Copyright:
© 2022, The Author(s), under exclusive licence to Springer Nature B.V.
PY - 2022
Y1 - 2022
N2 - 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.
AB - 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.
KW - Adaptive neuro-fuzzy inference system
KW - Artificial neural network
KW - Convolutional neural network
KW - Deep learning
KW - Fault detection
KW - Fault location estimation
KW - Fault type classification
KW - Machine learning
KW - Transmission line
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U2 - 10.1007/s10462-022-10296-0
DO - 10.1007/s10462-022-10296-0
M3 - Article
AN - SCOPUS:85141873356
SN - 0269-2821
JO - Artificial Intelligence Review
JF - Artificial Intelligence Review
ER -