Journal of Applied Research in Electrical Engineering

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Document Type : Research article

Authors

Electrical Engineering Department and Centre of Excellence in Power System Management and Control, Sharif University of Technology, Tehran 1458889694, Iran

https://doi.org/10.22055/jaree.2022.40395.1054

Abstract

Partial Discharge (PD) measurement is one of the best solutions for condition assessment of Gas Insulated Switchgears (GISs). For having Condition-based maintenance of GIS, online PD monitoring is of great importance. For this aim, Ultra High Frequency (UHF) PD sensors should be installed inside the GIS during the installation. However, in most installed GISs in industries, the internal UHF PD sensors are not installed. In this paper, a new method for online defect type recognition according to external UHF PD sensors and based on the time-frequency representation of PD signal is proposed. In this case, four artificial defect types named protrusion on the main conductor, protrusion on the enclosure, free moving metal particle, and metal particle on spacer are implanted inside the 132 kV L-Shaped structure of one phase in enclosure GIS. The signal energy at each level of the decomposed signal by Discrete Wavelet Transform (DWT) is applied for features of each defect type. The trends of signal energy variations at each frequency range of signal are applied for discriminating between each defect type. The Deep Feed Forward Network (DFFN) classifier is applied for PD pattern recognition. The results show the benefits and simplicity of the proposed method for PD signal classification, independent from the position of the PD sensor, especially in the case of online PD monitoring of GIS.

Highlights

An online partial discharge pattern recognition method is presented based on measured Partial Discharge (PD) data from an external UHF PD sensor for condition assessment of Gas Insulated Switchgears (GIS).
The time-frequency representation of signal from discrete wavelet transform is applied for feature extractions of each PD defect model.
The feature extracted based on signal energy at each level of DWT decomposition are independent of the positioning of UHF PD sensors.
By using the Deep Feed-Forward Network, the classification accuracy of the proposed method for PD pattern recognition is about 94.5 %.

Keywords

Main Subjects

References

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Journal of Applied Research in Electrical Engineering

Volume 2, Issue 1
Winter and Spring 2023
January 2023
Pages 75-86

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Receive Date: 30 March 2022
Revise Date: 31 October 2022
Accept Date: 06 December 2022

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