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Performance Evaluation of Machine Learning for Fault Selection in Power Transmission Lines by Daniel Gutierrez-Rojas; Ioannis T. Christou; Daniel Dantas; Arun Narayanan; Pedro H. J. Nardelli; Yongheng Yang is a Computer Science article available to read on EtoBox.

What is Performance Evaluation of Machine Learning for Fault Selection in Power Transmission Lines about?

## Abstract Learning methods have been increasingly used in power engineering to perform various tasks. In this paper, a fault selection procedure in double-circuit transmission lines employing different learning methods is accordingly proposed. In the proposed procedure, the discrete Fourier transform (DFT) is used to pre-process raw data from the transmission line before it is fed into the learning algorithm, which will detect and classify any fault based on a training period. The performance of different machine learning algorithms is then numerically compared through simulations. The comparison indicates that an artificial neural network (ANN) achieves remarkable accuracy of 98.47%. As a drawback, the ANN method cannot provide explainable results and is also not robust against noisy measurements. Subsequently, it is demonstrated that explainable results can be obtained with high accuracy by using rule-based learners such as the recently developed quantitative association rule mining algorithm (QARMA). The QARMA algorithm outperforms other explainable schemes, while attaining an accuracy of 98%. Besides, it was shown that QARMA leads to a very high accuracy of 97% for highly noi

Who reads Performance Evaluation of Machine Learning for Fault Selection in Power Transmission Lines?

Author

Daniel Gutierrez-Rojas; Ioannis T. Christou; Daniel Dantas; Arun Narayanan; Pedro H. J. Nardelli; Yongheng Yang

Publisher

Springer Science and Business Media LLC

Published

2022

Language

EN

Field

Computer Science (Physical Sciences)