Power Quality Event Detection Using a Fast Extreme Learning Machine

Ferhat Ucar, Omer F. Alcin, Besir Dandil and Fikret Ata
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Ferhat Ucar: Department of Electrical and Electronics Engineering, Technology Faculty, Firat University, 23119 Elazig, Turkey
Omer F. Alcin: Department of Electrical and Electronics Engineering, Faculty of Engineering and Architecture, Bingol University, 12000 Bingol, Turkey
Besir Dandil: Department of Mechatronics Engineering, Technology Faculty, Firat University, 23119 Elazig, Turkey
Fikret Ata: Department of Electrical and Electronics Engineering, Faculty of Engineering and Architecture, Bingol University, 12000 Bingol, Turkey

Energies, 2018, vol. 11, issue 1, 1-14

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.

Keywords: event detection; power quality; histogram; machine learning; wavelet transform (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: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (7)

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