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Multi-Labeled Recognition of Distribution System Conditions by a Waveform Feature Learning Model

Author

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  • Sang-Keun Moon

    (Korea Electric Power Corporation Research Institute, Daejeon 34056, Korea)

  • Jin-O Kim

    (Department of Electrical Engineering, Hanyang University, Seoul 04763, Korea)

  • Charles Kim

    (Department of Electrical and Computer Engineering, Howard University, Washington, DC 20059, USA)

Abstract

A waveform contains recognizable feature patterns. To extract the features of various equipment disturbance conditions from a waveform, this paper presents a practical model to estimate distribution line (DL) conditions by means of a multi-label extreme learning machine. The motivation for the waveform learning is to develop device-embedded models which are capable of detecting and classifying abnormal operations on the DLs. In waveform analysis, power quality waveform modeling criteria are adopted for pattern classification. Typical disturbance waveforms are applied as class models, and the formula-generated waveform features are compared with field-acquired waveforms for disturbance classification. In particular, filtered symmetrical components of the modified varying window scale are applied for feature extraction. The proposed model interacts suitably with the parameter update method in classifying the waveforms in real network situations. The classification result showed disturbance features on model with the real DL waveform data holds a potential for determining additional DL conditions and improving its classification performance through the update mechanism of the learning machine.

Suggested Citation

  • Sang-Keun Moon & Jin-O Kim & Charles Kim, 2019. "Multi-Labeled Recognition of Distribution System Conditions by a Waveform Feature Learning Model," Energies, MDPI, vol. 12(6), pages 1-14, March.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:6:p:1115-:d:216228
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    References listed on IDEAS

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    1. Mahela, Om Prakash & Shaik, Abdul Gafoor & Gupta, Neeraj, 2015. "A critical review of detection and classification of power quality events," Renewable and Sustainable Energy Reviews, Elsevier, vol. 41(C), pages 495-505.
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    Cited by:

    1. Vitor Hugo Ferreira & André da Costa Pinho & Dickson Silva de Souza & Bárbara Siqueira Rodrigues, 2021. "A New Clustering Approach for Automatic Oscillographic Records Segmentation," Energies, MDPI, vol. 14(20), pages 1-18, October.
    2. Juan Carlos Bravo-Rodríguez & Francisco J. Torres & María D. Borrás, 2020. "Hybrid Machine Learning Models for Classifying Power Quality Disturbances: A Comparative Study," Energies, MDPI, vol. 13(11), pages 1-20, June.

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