Load Identification Using Harmonic Based on Probabilistic Neural Network

EMITTER International Journal of Engineering Technology

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Field Value
 
Title Load Identification Using Harmonic Based on Probabilistic Neural Network
 
Creator Anggriawan, Dimas Okky
Amsyar, Aidin
Prasetyono, Eka
Wahjono, Endro
Sudiharto, Indhana
Tjahjono, Anang
 
Subject power system
Harmonic; FFT; Probabilistic Neural Network; Loads
Power Quality
 
Description Due to increase power quality which are caused by harmonic distortion it could be affected malfunction electrical equipment. Therefore, identification of harmonic loads become important attention  in the power system. According to those problems, this paper proposes a Load Identification using harmonic based on probabilistic neural network (PNN). Harmonic is obtained by experiment using prototype, which it consists of microcontroller and current sensor. Fast Fourier Transform (FFT) method to analyze of current waveform on loads become harmonic load data. PNN is used to identify the type of load. To load identification, PNN is trained to get the new weight. Testing is conducted To evaluate of the accuracy of the PNN from combination of four loads. The results demonstrate that this method has high accuracy to determine type of loads based on harmonic load
 
Publisher Politeknik Elektronika Negeri Surabaya (PENS)
 
Contributor
 
Date 2019-06-15
 
Type info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
Peer-reviewed Article

 
Format application/pdf
 
Identifier http://emitter.pens.ac.id/index.php/emitter/article/view/330
10.24003/emitter.v7i1.330
 
Source EMITTER International Journal of Engineering Technology; Vol 7, No 1 (2019); 71-82
2443-1168
2355-391X
10.24003/emitter.v7i1
 
Language eng
 
Relation http://emitter.pens.ac.id/index.php/emitter/article/view/330/132
 
Coverage


 
Rights Copyright (c) 2019 EMITTER International Journal of Engineering Technology
http://creativecommons.org/licenses/by-nc-sa/4.0
 

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