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

Huihui Wang, Ping Wang and Tao Liu
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Huihui Wang: School of Electrical Engineering and Automation, Tianjin University, Tianjin 300072, China
Ping Wang: School of Electrical Engineering and Automation, Tianjin University, Tianjin 300072, China
Tao Liu: School of Electrical Engineering and Automation, Tianjin Polytechnic University, Tianjin 300387, China

Energies, 2017, vol. 10, issue 1, 1-19

Abstract: 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.

Keywords: transient power quality; S-transform; width factor; feature extraction; probabilistic neural network (PNN) (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2017
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (9)

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