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Energies 2019, 12(8), 1449; https://doi.org/10.3390/en12081449

Bundle Extreme Learning Machine for Power Quality Analysis in Transmission Networks

1
Department of Electrical and Electronics Engineering, Technology Faculty, Firat University, Elazig 23119, Turkey
2
Department of Electrical and Computer Engineering, Florida State University, Tallahassee, FL 32306, USA
3
Department of Electrical and Electronics Engineering, Faculty of Engineering and Architecture, Bingol University, Bingol 12000, Turkey
4
Department of Mechatronics Engineering, Technology Faculty, Firat University, Elazig 23119, Turkey
5
Department of Computing, Mathematics and Physics, Western Norway University of Applied Sciences, 5063 Bergen, Norway
*
Author to whom correspondence should be addressed.
Received: 7 March 2019 / Revised: 11 April 2019 / Accepted: 11 April 2019 / Published: 16 April 2019
(This article belongs to the Special Issue Digital Solutions for Energy Management and Power Generation)
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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. View Full-Text
Keywords: power quality; event detection; permutation entropy; machine learning; extreme learning machine power quality; event detection; permutation entropy; machine learning; extreme learning machine
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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MDPI and ACS Style

Ucar, F.; Cordova, J.; Alcin, O.F.; Dandil, B.; Ata, F.; Arghandeh, R. Bundle Extreme Learning Machine for Power Quality Analysis in Transmission Networks. Energies 2019, 12, 1449.

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