Power Quality Event Detection Using a Fast Extreme Learning Machine
Date
2018Author
Ucar, Ferhat and Alcin, Omer F. and Dandil, Besir and Ata, Fikret
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Show full item recordAbstract
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.
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