Classification of transformer faults using frequency response analysis based on cross-correlation technique and support vector machine

Document Type : Original Article

Author

Faculty of Engineering, Department of Electrical Engineering, Islamic Azad University, Zanjan Branch, Zanjan, Iran

Abstract

One of the most important methods for transformers fault diagnosis (especially mechanical defects) is the frequency response analysis (FRA). The most important step in the FRA diagnostic process is to differentiate the faults and classify them in different classes. This paper uses the intelligent support vector machine (SVM) method to classify transformer faults. To this end, two groups of transformers were tested and necessary measurements were obtained; the transformers with healthy conditions and the ones with various fault conditions (axial displacement, radial deformation, disc space variation, short-circuits, and core deformation). Then, by dividing the frequency intervals of the measurements, a new feature based on the cross-correlation technique was proposed for SVM training and validation. After the training process, by applying the data obtained from actual transformers, the performance of SVM in different conditions was evaluated and compared. Finally, the most appropriate feature was presented.

Keywords

References

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History

  • Receive Date: 17 September 2020
  • Revise Date: 07 December 2020
  • Accept Date: 01 January 2021

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