Mechanical Fault Types Detection in Transformer Windings Using ‎Interpretation of Frequency Responses via Multilayer Perceptron

Document Type : Research paper

Authors

1 Department of Electrical Engineering, Amirkabir University of Technology, Tehran, Iran

2 Faculty of Electrical and Computer engineering, University of Tabriz, Tabriz, Iran

3 High-Voltage Research Group, Niroo Research Institute, Tehran, Iran

4 Iran Grid Secure Operation Research Center, Amirkabir University of Technology, Tehran, Iran

5 Institute of Power Transmission and High Voltage Technology, University of Stuttgart, Stuttgart, Germany

Abstract

The Frequency Response Analysis (FRA) technique has advantages in identifying faults related to power transformers, but it suffers from the interpretation of frequency responses. This paper presents an approach based on statistical indices and Artificial Neural Network (ANN) methods to interpret frequency responses. The proposed procedure divides frequency responses into four frequency regions based on frequency resonances and anti-resonances. Then, Lin’s Concordance Coefficient (LCC) index is used as one of the most appropriate numerical indices to extract features of the four frequency regions. Finally, the Multilayer Perceptron (MLP) neural network is trained by the extracted features to identify and differentiate the types of winding faults. Besides, other intelligent algorithms such as Support Vector Machine (SVM), Extreme Learning Machine (ELM), Probabilistic Neural Network (PNN), and Radial Basis Function (RBF) neural network have been employed to compare the classification results. The proposed techniques have been practically implemented. The Axial Displacement (AD) and Disk Space Variation (DSV) faults are applied as two common mechanical faults in different locations and intensities on the 20kV windings of a 1.6MVA distribution power transformer and their corresponding frequency responses are calculated. Frequency responses calculated from the AD and DSV faults constitute the MLP input data set. The network is trained with part of the input data, and the rest of the data is allocated to validate and test the network. The results show that the suggested method has more proper performance than others using the phase component of the frequency responses in interpreting frequency responses and separation and identifying various mechanical fault types of transformer windings.

Keywords

References

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Journal of Operation and Automation in Power Engineering
Volume 11, Issue 1
April 2023
Pages 11-21

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History

  • Receive Date: 22 July 2021
  • Revise Date: 04 January 2022
  • Accept Date: 15 January 2022
  • First Publish Date: 26 January 2022

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