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Energies 2017, 10(1), 107; doi:10.3390/en10010107

Power Quality Disturbance Classification Using the S-Transform and Probabilistic Neural Network

1,2,* , 1
School of Electrical Engineering and Automation, Tianjin University, Tianjin 300072, China
School of Control and Mechanical Engineering, Tianjin Chengjian University, Tianjin 300384, China
School of Electrical Engineering and Automation, Tianjin Polytechnic University, Tianjin 300387, China
Author to whom correspondence should be addressed.
Academic Editor: Birgitte Bak-Jensen
Received: 19 September 2016 / Revised: 28 December 2016 / Accepted: 9 January 2017 / Published: 17 January 2017
(This article belongs to the Special Issue Distribution Power Systems and Power Quality)
View Full-Text   |   Download PDF [11323 KB, uploaded 17 January 2017]   |  


This paper presents a transient power quality (PQ) disturbance classification approach based on a generalized S-transform and probabilistic neural network (PNN). Specifically, the width factor used in the generalized S-transform is feature oriented. Depending on the specific feature to be extracted from the S-transform amplitude matrix, a favorable value is determined for the width factor, with which the S-transform is performed and the corresponding feature is extracted. Four features obtained this way are used as the inputs of a PNN trained for performing the classification of 8 disturbance signals and one normal sinusoidal signal. The key work of this research includes studying the influence of the width factor on the S-transform results, investigating the impacts of the width factor on the distribution behavior of features selected for disturbance classification, determining the favorable value for the width factor by evaluating the classification accuracy of PNN. Simulation results tell that the proposed approach significantly enhances the separation of the disturbance signals, improves the accuracy and generalization ability of the PNN, and exhibits the robustness of the PNN against noises. The proposed algorithm also shows good performance in comparison with other reported studies. View Full-Text
Keywords: transient power quality; S-transform; width factor; feature extraction; probabilistic neural network (PNN) transient power quality; S-transform; width factor; feature extraction; probabilistic neural network (PNN)

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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Wang, H.; Wang, P.; Liu, T. Power Quality Disturbance Classification Using the S-Transform and Probabilistic Neural Network. Energies 2017, 10, 107.

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