A fault classification method by RBF neural network with OLS learning procedure

Lin, Wen-Chyi; Yang, Chih‐Hsiang; Lin, Jiaxin; Tsay, Ming-Tong

doi:10.1109/pica.2001.932332

Cited by 14 publications

(15 citation statements)

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“…They consist of a network with a single hidden layer and a structure similar to back propagation networks [4]. The in [8] presented investigation results confirm the advantages the RBF neural networks over the others solutions for classification tasks. Each hidden layer unit has a centroid i c and smoothing factor i σ .…”

Section: Rbf Neural Networksupporting

confidence: 57%

Proposal of enhanced classification of disturbing phenomena in electrical networks

Janík

2010

2010 9th International Conference on Environment and Electrical Engineering

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The author proposes a method of power quality classification using SVM (Support Vector Machines) neural networks. It shoves enhanced performance in comparison to a classifier based on the RBF (Radial Basis Function) network. Both, RBF and SVM networks are introduced and described as appropriate tools for classification problems. Space phasor is used for feature extraction needed by the classifiers. Different disturbance classes has been simulated (e.g. sags, voltage fluctuations, transients) using Matlab modeling. The classifiers has been tested with these signals.

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Section: Rbf Neural Networksupporting

confidence: 57%

Proposal of enhanced classification of disturbing phenomena in electrical networks

Janík

2010

2010 9th International Conference on Environment and Electrical Engineering

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“…As an application, fault analysis in power transmission systems using ANN has been subject of some researches [6][7][8][9]. Based on a radial basis function (RBF) neural network with orthogonalleast-square (OLS) learning procedure, a simple fault classification method was reported in [10]. The method identifies various patterns of associated voltages and currents, but it does not have the capability of fault location, and furthermore, some faults cannot be classified because the method can not identify all possible patterns related to different faults.…”

Section: Q2mentioning

confidence: 99%

“…The method identifies various patterns of associated voltages and currents, but it does not have the capability of fault location, and furthermore, some faults cannot be classified because the method can not identify all possible patterns related to different faults. The RBF neural network was also compared with the back-propagation (BP) neural network, and it was shown that the RBF neural network classifies faults better than BP neural network [10]. Discrete wavelet transform (DWT) as a soft computing tool has been used in different applications [11][12][13].…”

Section: Q2mentioning

confidence: 99%

Two novel proposed discrete wavelet transform and filter based approaches for short-circuit faults detection in power transmission lines

Fathabadi

2015

Applied Soft Computing

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“…Knowledge based fault detection [3][4][5][6][7][8][9][10][11][12][13][14][15][16] utilises prior knowledge of the system quantities (voltages and currents and waveforms) under different fault and system operating conditions. This knowledge is then used to train a learning system to identify abnormal conditions and classify them.…”

Section: Introductionmentioning

confidence: 99%