NDLI: Neural-network-based signature recognition for harmonic source identification

Content Provider	IEEE Xplore Digital Library
Author	Srinivasan, D. Ng, W.S. Liew, A.C.
Copyright Year	1986
Abstract	This paper proposes a neural-network (NN)-based approach to nonintrusive harmonic source identification. In this approach, NNs are trained to extract important features from the input current waveform to uniquely identify various types of devices using their distinct harmonic "signatures". Such automated, noninvasive device identification will be critical in future power-quality monitoring and enhancement systems. Several NN-based classification models including multilayer perceptron (MLP), radial basis function (RBF) network, and support vector machines (SVM) with linear, polynomial, and RBF kernels were developed for signature extraction and device identification. These models were trained and tested using spike train data gathered from the Fourier analysis of the input current waveform in the presence of multiple devices. The performance of these models was compared in terms of their accuracy, generalization ability, and noise tolerance limits. The results showed that MLPs and SVM were both able to determine the presence of devices based on their harmonic signatures with high accuracy. MLP was found to be the best signature identification method because of its low computational requirements and ability to extract the information necessary for highly accurate device identification.
Sponsorship	IEEE Power Engineering Society
Starting Page	398
Ending Page	405
Page Count	8
File Size	378587
File Format	PDF
ISSN	08858977
Volume Number	21
Issue Number	1
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2006-01-01
Publisher Place	U.S.A.
Access Restriction	One Nation One Subscription (ONOS)
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Object recognition Support vector machines Power system modeling Support vector machine classification Data mining Feature extraction Power quality Computerized monitoring Multilayer perceptrons Polynomials signature identification Artificial neural network (ANN) power harmonics
Content Type	Text
Resource Type	Article
Subject	Energy Engineering and Power Technology Electrical and Electronic Engineering

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