Bundle Extreme Learning Machine for Power Quality Analysis in Transmission Networks

Ferhat Ucar, Jose Cordova, Omer F. Alcin, Besir Dandil, Fikret Ata and Reza Arghandeh
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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

Energies, 2019, vol. 12, issue 8, 1-26

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.

Keywords: power quality; event detection; permutation entropy; machine learning; extreme learning machine (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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