WSEAS Transactions on Power Systems


Print ISSN: 1790-5060
E-ISSN: 2224-350X

Volume 13, 2018

Notice: As of 2014 and for the forthcoming years, the publication frequency/periodicity of WSEAS Journals is adapted to the 'continuously updated' model. What this means is that instead of being separated into issues, new papers will be added on a continuous basis, allowing a more regular flow and shorter publication times. The papers will appear in reverse order, therefore the most recent one will be on top.


Automatic Classification of Hybrid Power Quality Disturbances Using Wavelet Norm Entropy and Neural Network

AUTHORS: S. Khokhar, A. A. Mohd Zin, A. S. Mokhtar

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ABSTRACT: The classification of single and multiple power quality (PQ) disturbances is a very important task for the detection and monitoring of various faults and events in electrical power network. This paper presents an automatic classification algorithm for PQ disturbances based on wavelet norm entropy (WNE) features and probabilistic neural network (PNN) as an effective pattern classifier. The discrete wavelet transform (DWT) based multiresolution analysis (MRA) technique is proposed to extract the most important and constructive features of power quality disturbances at various resolution levels. The distinctive norm entropy features of the PQ disturbances are extracted and are employed as inputs to the PNN. Various other architectures of neural networks such as multilayer perceptron (MLP) and radial basis function (RBF) are also employed for comparison. The PNN is found the most suitable classification tool for the classification of the PQ disturbances. The simulation results obtained show that the proposed approach can detect and classify the disturbances effectively and can be applied successfully in real-time electrical power distribution networks

KEYWORDS: Power Quality Disturbances, Wavelet Norm Entropy, multiresolution analysis, Feature Extraction, Artificial Neural Network.

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WSEAS Transactions on Power Systems, ISSN / E-ISSN: 1790-5060 / 2224-350X, Volume 13, 2018, Art. #16, pp. 163-173


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