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Energies 2018, 11(1), 145; https://doi.org/10.3390/en11010145

Power Quality Event Detection Using a Fast Extreme Learning Machine

1
Department of Electrical and Electronics Engineering, Technology Faculty, Firat University, 23119 Elazig, Turkey
2
Department of Electrical and Electronics Engineering, Faculty of Engineering and Architecture, Bingol University, 12000 Bingol, Turkey
3
Department of Mechatronics Engineering, Technology Faculty, Firat University, 23119 Elazig, Turkey
*
Author to whom correspondence should be addressed.
Received: 9 November 2017 / Revised: 31 December 2017 / Accepted: 3 January 2018 / Published: 7 January 2018
(This article belongs to the Special Issue Power Electronics and Power Quality)
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Abstract

Monitoring Power Quality Events (PQE) is a crucial task for sustainable and resilient smart grid. This paper proposes a fast and accurate algorithm for monitoring PQEs from a pattern recognition perspective. The proposed method consists of two stages: feature extraction (FE) and decision-making. In the first phase, this paper focuses on utilizing a histogram based method that can detect the majority of PQE classes while combining it with a Discrete Wavelet Transform (DWT) based technique that uses a multi-resolution analysis to boost its performance. In the decision stage, Extreme Learning Machine (ELM) classifies the PQE dataset, resulting in high detection performance. A real-world like PQE database is used for a thorough test performance analysis. Results of the study show that the proposed intelligent pattern recognition system makes the classification task accurately. For validation and comparison purposes, a classic neural network based classifier is applied. View Full-Text
Keywords: event detection; power quality; histogram; machine learning; wavelet transform event detection; power quality; histogram; machine learning; wavelet transform
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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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Ucar, F.; Alcin, O.F.; Dandil, B.; Ata, F. Power Quality Event Detection Using a Fast Extreme Learning Machine. Energies 2018, 11, 145.

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