Winding deformation classification in a power transformer based on the time-frequency image of frequency response analysis using Hilbert-Huang transform and evidence theory. (July 2021)

• Record Type: Journal Article
• Authors: Shamlou, Alireza; Reza Feyzi, Mohammad; Behjat, Vahid
• Is Part Of: International journal of electrical power & energy systems. Volume 129(2021)
• Publication Date: 2021-07
• Subjects: FRA -- Interpretation -- Evidence theory -- DIP -- FEM -- HHT; Electrical engineering -- Periodicals; Electric power systems -- Periodicals; Électrotechnique -- Périodiques; Réseaux électriques (Énergie) -- Périodiques; Electric power systems; Electrical engineering; Periodicals; 621.3
• DOI: 10.1016/j.ijepes.2021.106854
• Languages: English
• ISSNs: 0142-0615

Abstract

Highlights: The present study employs a combined method based on digital image processing and evidence theory to facilitate the interpretation of the FRA response. The HHT method is used to produce a three-dimensional image from the FRA signal. After applying the HHT and obtaining a frequency-time image, the features are extracted from the image histogram. The features are imported into an evidence theory-based classifier. This method is completely automatic and can detect radial and axial deformation faults and their severity with good accuracy. Abstract: This study presents a novel fully-automated technique to interpret a frequency response analysis (FRA) of a power transformer, using a combined method based on digital image processing and evidence theory. Power transformers are widely used in power systems, and their continuous operation depends on proper monitoring and maintenance. While FRA is considered an efficient method for detecting minor damage in the windings of a power transformer in industrial applications, there is no universally accepted method for interpreting FRA results, and this crucial task relies on the opinion of an error-prone human expert. Our method first obtains a time-frequency image using the Hilbert-Huang transform. Next, the image's histogram is imported into an evidence theory-based classifier, which ultimately detects radial and axial deformation faults, and their severity. The proposed method is shown to report faults with remarkable accuracy by a finite element model of a three-phase 125 MVA, 230/132/20 kV autotransformer, in which various faults with different severities are simulated and tested.