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http://dx.doi.org/10.5391/JKIIS.2016.26.5.409

A Study on the Signal Processing Techiques for Pattern Classification of Electrical Loads  

Lim, Young Bae (Electrical Safety Research Institute, a subsidiary of Korea Electrical Safety Corporation)
Kim, Dong Woo (Electrical Safety Research Institute, a subsidiary of Korea Electrical Safety Corporation)
Jin, Sangmin (School of Electrical and Electronics Engineering, Hongik University)
Cho, Seongwon (School of Electrical and Electronics Engineering, Hongik University)
Publication Information
Journal of the Korean Institute of Intelligent Systems / v.26, no.5, 2016 , pp. 409-415 More about this Journal
Abstract
Recently several techniques for disaster prevention based on IoT(Internet of Things) are being developed. In this paper, a new smart pattern classification method for electric loads is proposed. CT(Current Transformer) data are extracted from electric loads, and then the sampled CT data are converted using FFT and MFCC. FFT and FMCC data are used for the input data of neural networks. Experiments were conducted using FFT and MFCC data for 7 kinds of electric loads. Experiments results indicate the superiority of MFCC in comparison to FFT.
Keywords
Electric Load; MFCC; Neural Networks; Pattern Classification; Signal Processing;
Citations & Related Records
Times Cited By KSCI : 3  (Citation Analysis)
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