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Analysis of power transformer dissolved gas data using the self-organizing map


Reference:

Thang, K. F., Aggarwal, R. K., McGrail, A. J. and Esp, D. G., 2003. Analysis of power transformer dissolved gas data using the self-organizing map. IEEE Transactions on Power Delivery, 18 (4), pp. 1241-1248.

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Abstract

Incipient faults in power transformers can degrade the oil and cellulose insulation, leading to the formation of dissolved gases. Even though established approaches that relate these dissolved gas information to the condition of power transformers are already developed, it is discussed in this paper that they still contain some limitations. In view of that, this paper introduces an alternative approach for the analysis of dissolved gas data, which can produce more convincing interpretation and fault diagnosis. The proposed approach, which is based on the data mining methodology and the self-organizing map, has been compared and validated using conventional interpretation schemes and real fault-cases, thereby proven to be capable of enhancing the condition monitoring of power transformers.

Details

Item Type Articles
CreatorsThang, K. F., Aggarwal, R. K., McGrail, A. J. and Esp, D. G.
Uncontrolled Keywordsdata mining, chemical analysis, incipient faults, cellulose insulation, power engineering computing, self-organising feature maps, oil insulation, power transformer insulation, fault diagnosis, transformer oil, condition monitoring, self-organizing map, power transformers, dissolved gas analysis
DepartmentsFaculty of Engineering & Design > Electronic & Electrical Engineering
RefereedYes
StatusPublished
ID Code6005

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