IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v12y2019i8p1449-d223268.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/12/8/1449/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/12/8/1449/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. 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.
    2. 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.
    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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Nantian Huang & Hua Peng & Guowei Cai & Jikai Chen, 2016. "Power Quality Disturbances Feature Selection and Recognition Using Optimal Multi-Resolution Fast S-Transform and CART Algorithm," Energies, MDPI, vol. 9(11), pages 1-21, November.
    2. Vojtech Blazek & Michal Petruzela & Tomas Vantuch & Zdenek Slanina & Stanislav Mišák & Wojciech Walendziuk, 2020. "The Estimation of the Influence of Household Appliances on the Power Quality in a Microgrid System," Energies, MDPI, vol. 13(17), pages 1-21, August.
    3. Azcarate, I. & Gutierrez, J.J. & Lazkano, A. & Saiz, P. & Redondo, K. & Leturiondo, L.A., 2016. "Towards limiting the sensitivity of energy-efficient lighting to voltage fluctuations," Renewable and Sustainable Energy Reviews, Elsevier, vol. 59(C), pages 1384-1395.
    4. Igual, R. & Medrano, C., 2020. "Research challenges in real-time classification of power quality disturbances applicable to microgrids: A systematic review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 132(C).
    5. Misael Lopez-Ramirez & Eduardo Cabal-Yepez & Luis M. Ledesma-Carrillo & Homero Miranda-Vidales & Carlos Rodriguez-Donate & Rocio A. Lizarraga-Morales, 2018. "FPGA-Based Online PQD Detection and Classification through DWT, Mathematical Morphology and SVD," Energies, MDPI, vol. 11(4), pages 1-15, March.
    6. Tümay, Mehmet & Demirdelen, Tuğçe & Bal, Selva & Kayaalp, Rahmi İlker & Doğru, Burcu & Aksoy, Mahmut, 2017. "A review of magnetically controlled shunt reactor for power quality improvement with renewable energy applications," Renewable and Sustainable Energy Reviews, Elsevier, vol. 77(C), pages 215-228.
    7. Yue Shen & Muhammad Abubakar & Hui Liu & Fida Hussain, 2019. "Power Quality Disturbance Monitoring and Classification Based on Improved PCA and Convolution Neural Network for Wind-Grid Distribution Systems," Energies, MDPI, vol. 12(7), pages 1-26, April.
    8. 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.
    9. Michał Jasiński & Tomasz Sikorski & Zbigniew Leonowicz & Klaudiusz Borkowski & Elżbieta Jasińska, 2020. "The Application of Hierarchical Clustering to Power Quality Measurements in an Electrical Power Network with Distributed Generation," Energies, MDPI, vol. 13(9), pages 1-19, May.
    10. Wang, Shouxiang & Chen, Haiwen, 2019. "A novel deep learning method for the classification of power quality disturbances using deep convolutional neural network," Applied Energy, Elsevier, vol. 235(C), pages 1126-1140.
    11. Manuel Jesús Hermoso-Orzáez & Alfonso Gago-Calderón & José Ignacio Rojas-Sola, 2017. "Power Quality and Energy Efficiency in the Pre-Evaluation of an Outdoor Lighting Renewal with Light-Emitting Diode Technology: Experimental Study and Amortization Analysis," Energies, MDPI, vol. 10(7), pages 1-13, June.
    12. Prakash Mahela, Om & Gafoor Shaik, Abdul, 2016. "Topological aspects of power quality improvement techniques: A comprehensive overview," Renewable and Sustainable Energy Reviews, Elsevier, vol. 58(C), pages 1129-1142.
    13. Khokhar, Suhail & Mohd Zin, Abdullah Asuhaimi B. & Mokhtar, Ahmad Safawi B. & Pesaran, Mahmoud, 2015. "A comprehensive overview on signal processing and artificial intelligence techniques applications in classification of power quality disturbances," Renewable and Sustainable Energy Reviews, Elsevier, vol. 51(C), pages 1650-1663.
    14. Michał Gwóźdź & Łukasz Ciepliński, 2021. "An Algorithm for Calculation and Extraction of the Grid Voltage Component," Energies, MDPI, vol. 14(16), pages 1-12, August.
    15. Wajahat Ullah Khan Tareen & Muhammad Aamir & Saad Mekhilef & Mutsuo Nakaoka & Mehdi Seyedmahmoudian & Ben Horan & Mudasir Ahmed Memon & Nauman Anwar Baig, 2018. "Mitigation of Power Quality Issues Due to High Penetration of Renewable Energy Sources in Electric Grid Systems Using Three-Phase APF/STATCOM Technologies: A Review," Energies, MDPI, vol. 11(6), pages 1-41, June.
    16. Zhang, Liangheng & Jiang, Congmei & Pang, Aiping & He, Yu, 2024. "Super-efficient detector and defense method for adversarial attacks in power quality classification," Applied Energy, Elsevier, vol. 361(C).
    17. Hong-Keun Ji & Guoming Wang & Gyung-Suk Kil, 2020. "Optimal Detection and Identification of DC Series Arc in Power Distribution System on Shipboards," Energies, MDPI, vol. 13(22), pages 1-16, November.
    18. 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.
    19. Shao, Han & Henriques, Rui & Morais, Hugo & Tedeschi, Elisabetta, 2024. "Power quality monitoring in electric grid integrating offshore wind energy: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 191(C).
    20. Yljon Seferi & Steven M. Blair & Christian Mester & Brian G. Stewart, 2021. "A Novel Arc Detection Method for DC Railway Systems," Energies, MDPI, vol. 14(2), pages 1-21, January.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:12:y:2019:i:8:p:1449-:d:223268. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.