Power system fault classification method based on sparse representation and random dimensionality reduction projection

Cheng, Long; Wang, Lingyun; Gao, Feng

doi:10.1109/pesgm.2015.7286147

Cited by 15 publications

(1 citation statement)

References 9 publications

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“…In Thukaram, Khincha, and Vijaynarasimha (2005), principal component analysis (PCA) was applied directly to current and voltage signals, and in Cheng, Wang, and Gao (2015), a feature extraction stage based on random projections was applied. In Godse and Bhat (2020), the feature extraction and feature selection stages were conducted using a morphological median filter (MMF) and information gain, respectively.…”

Section: Introductionmentioning

confidence: 99%

Higher-Order Statistics-Based Method for Transmission Line Fault Classification

Galdino Bouzon,

Aparecido Fonseca,

Leal

et al. 2023

Theor Appl Eng

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Research on power transmission lines has been the subject of several studies aiming to provide relevant information to electrical system users. One of the focuses of this study area is transmission line fault classification. This paper presents an approach for fault classification using higher-order statistics and an artificial neural network-based classifier. A detector based on Euclidean distance was implemented to reduce classifier complexity. The proposed method takes advantage of requiring only 1⁄32 cycles of postfault data to perform the classification; therefore, it is suitable for real-time processing. The proposed method classified 10 classes of faults with global efficiency above 97%.

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Section: Introductionmentioning

confidence: 99%