Internal fault detection techniques for power transformers

Authors:
Narri Yadaiah;Nagireddy Ravi
Affiliations:
Dept. of Electrical and Electronics Engineering, Jawaharlal Nehru Technological University Hyderabad, College of Engineering, Hyderabad 500 085, A.P., India;Mini Hydel Stations, Andhra Pradesh Power Generation Corporation Limited, Pochampad, Hyderabad 500 082, A.P., India
Venue:
Applied Soft Computing
Year:
2011

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Abstract

This paper presents the methodologies for incipient fault detection in power transformers both off-line and on-line. An artificial neural network is used to detect faults off-line with dissolved gas analysis reports of transformers and whereas wavelet transforms are being used for on-line fault detection. The accuracy in fault detection through artificial neural networks is compared with Rogers ratio method using the analysis of experimental oil samples for power transformers of power companies in Andhra Pradesh, India. The Wavelet transform techniques have been developed with different mother wavelets to detect incipient faults and to distinguish between incipient fault and short circuit fault. Further their performances with different mother wavelets are compared.