Detection of Power Transformer Winding Deformation using Improved FRA Based on Binary Morphology and Extreme Point Variation
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IEEE Frequency response analysis (FRA) has recently been developed as a widely accepted tool for power transformer winding mechanical deformation diagnosis, and has proven to be effective and powerful in many cases. However, there still exist problems regarding the application of FRA. FRA is a comparative method in which the measured FRA signature should be compared with its fingerprint. Small differences of FRA signatures in certain frequency bands might be produced by external disturbance, which hinders fault diagnosis. Additionally, the existing correlation coefficient indicator recommended by power industry standards cannot reflect key information of signatures, namely the extreme points. This paper proposes an improved FRA based on binary morphology and extreme point variation. Binary morphology is first introduced to extract the certain frequency bands of signatures with significant difference. A composite indicator of extreme point variation is adopted to realize the diagnosis of fault level. A ternary diagram is constructed by the area proportions of the binary image to identify winding faults, which has a potential to realize cluster analysis of fault types.
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Hashemnia, Seyednaser; Abu-Siada, Ahmed; Islam, Syed (2015)Frequency response analysis (FRA) has been globally accepted as a reliable tool to detect mechanical deformation within power transformers. However, because of its reliance on graphical analysis, interpretation of FRA ...
Improved Power Transformer Winding Fault Detection using FRA Diagnostics – Part 2: Radial Deformation SimulationHashemnia, Seyednaser; Abu-Siada, Ahmed; Islam, Syed (2015)Frequency response analysis (FRA) is proven to be a powerful tool to detect winding deformation within power transformers. Although the FRA test along with the equipment are well developed, interpretation of FRA signature ...
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