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Bundle Extreme Learning Machine for Power Quality Analysis in Transmission Networks

Author

Listed:
  • Ferhat Ucar

    (Department of Electrical and Electronics Engineering, Technology Faculty, Firat University, Elazig 23119, Turkey)

  • Jose Cordova

    (Department of Electrical and Computer Engineering, Florida State University, Tallahassee, FL 32306, USA)

  • Omer F. Alcin

    (Department of Electrical and Electronics Engineering, Faculty of Engineering and Architecture, Bingol University, Bingol 12000, Turkey)

  • Besir Dandil

    (Department of Mechatronics Engineering, Technology Faculty, Firat University, Elazig 23119, Turkey)

  • Fikret Ata

    (Department of Electrical and Electronics Engineering, Faculty of Engineering and Architecture, Bingol University, Bingol 12000, Turkey)

  • Reza Arghandeh

    (Department of Computing, Mathematics and Physics, Western Norway University of Applied Sciences, 5063 Bergen, Norway)

Abstract

This paper presents a novel method for online power quality data analysis in transmission networks using a machine learning-based classifier. The proposed classifier has a bundle structure based on the enhanced version of the Extreme Learning Machine (ELM). Due to its fast response and easy-to-build architecture, the ELM is an appropriate machine learning model for power quality analysis. The sparse Bayesian ELM and weighted ELM have been embedded into the proposed bundle learning machine. The case study includes real field signals obtained from the Turkish electricity transmission system. Most actual events like voltage sag, voltage swell, interruption, and harmonics have been detected using the proposed algorithm. For validation purposes, the ELM algorithm is compared with state-of-the-art methods such as artificial neural network and least squares support vector machine.

Suggested Citation

  • Ferhat Ucar & Jose Cordova & Omer F. Alcin & Besir Dandil & Fikret Ata & Reza Arghandeh, 2019. "Bundle Extreme Learning Machine for Power Quality Analysis in Transmission Networks," Energies, MDPI, vol. 12(8), pages 1-26, April.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:8:p:1449-:d:223268
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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.
    2. Ferhat Ucar & Omer F. Alcin & Besir Dandil & Fikret Ata, 2018. "Power Quality Event Detection Using a Fast Extreme Learning Machine," Energies, MDPI, vol. 11(1), pages 1-14, January.
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    Cited by:

    1. Wenjian Hu & Mingxing Zhu & Huaying Zhang, 2022. "Application of Block Sparse Bayesian Learning in Power Quality Steady-State Data Compression," Energies, MDPI, vol. 15(7), pages 1-17, March.
    2. Xu Han & Dujie Hou & Xiong Cheng & Yan Li & Congkai Niu & Shuosi Chen, 2022. "Prediction of TOC in Lishui–Jiaojiang Sag Using Geochemical Analysis, Well Logs, and Machine Learning," Energies, MDPI, vol. 15(24), pages 1-25, December.
    3. Mario Šipoš & Zvonimir Klaić & Emmanuel Karlo Nyarko & Krešimir Fekete, 2021. "Determining the Optimal Location and Number of Voltage Dip Monitoring Devices Using the Binary Bat Algorithm," Energies, MDPI, vol. 14(1), pages 1-13, January.

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