DocumentCode :
251699
Title :
Artificial neural network based identification of deviation in frequency response of power transformer windings
Author :
Gandhi, Ketan R. ; Badgujar, Ketan P.
Author_Institution :
Dept. of Electr. Eng., L.D. Coll. of Eng., Ahmedabad, India
fYear :
2014
fDate :
24-26 July 2014
Firstpage :
1
Lastpage :
8
Abstract :
Deformations in windings can be diagnosed by a reliable and powerful method called sweep frequency response analysis (SFRA). In this work the deviation in the frequency response plots is derived in terms of statistical indicators. Nine statistical indicators have been used for the purpose. These indicators, then, complemented using artificial neural network approach, to derive a useful conclusion regarding the deviation based on the frequency responses. Winding deformation case data along with healthy transformer case data have been used to train a multilayer feed-forward neural network with the backpropagation algorithm. The trained neural network can help an expert to analyse statistical indicators to verify the level of deviation and in turn the level of deformation.
Keywords :
backpropagation; deformation; feedforward neural nets; frequency response; power engineering computing; power transformers; statistical analysis; transformer windings; SFRA; artificial neural network based identification; backpropagation algorithm; deformation level; healthy transformer case data; multilayer feed-forward neural network; power transformer windings; statistical indicators; sweep frequency response analysis; trained neural network; winding deformation case data; Artificial neural networks; Frequency response; Magnetics; Neurons; Power transformers; Training; Windings; Power transformer; artificial neural network; frequency response analysis; statistical indicators; winding deformation;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Emerging Research Areas: Magnetics, Machines and Drives (AICERA/iCMMD), 2014 Annual International Conference on
Conference_Location :
Kottayam
Print_ISBN :
978-1-4799-5201-4
Type :
conf
DOI :
10.1109/AICERA.2014.6908217
Filename :
6908217
Link To Document :
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