NDLI: Localization of turn-to-turn fault in transformers using Artificial Neural Networks and winding transfer function

Content Provider	IEEE Xplore Digital Library
Author	Faridi, M. Rahimpour, E. Kharezi, M. Mirzaei, H.R. Akbari, A.
Copyright Year	2010
Description	Author affiliation: Iran-Transfo Company, Zanjan, Iran (Kharezi, M.) \|\| Islamic Azad University, Khodabandeh Branch, Khodabandeh, Iran (Faridi, M.) \|\| ABB AG, Power Transformers Bad Honnef, Germany (Rahimpour, E.) \|\| K. N. Toosi University of Technology Tehran, Iran (Mirzaei, H.R.; Akbari, A.)
Abstract	To automate the procedure of localizing turn-to-turn faults in transformers windings, a method is proposed by employing of Artificial Neural Networks (ANN). For this purpose, a specially made distribution transformer winding is used as a test object to approve the capability of proposed method. This winding is appropriately designed to perform short circuit faults between any two desired adjacent turns. Then the frequency response of winding in both healthy and faulty conditions is measured using the Low Voltage Impulse (LVI) test. Extracted features from frequency responses are used to train and test the proposed ANN. The results show that this method is able to determine the location of turn-to-turn fault in winding.
Starting Page	1
Ending Page	4
File Size	1305986
Page Count	4
File Format	PDF
ISBN	9781424479450
e-ISBN	9781424479443
e-ISBN	9781424479436
DOI	10.1109/ICSD.2010.5568131
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-07-04
Publisher Place	Germany
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Windings Circuit faults Voltage measurement Artificial neural networks Admittance Current measurement Power transformers Neural Network Turn-to-Turn Fault Transformer Winding Transfer Function
Content Type	Text
Resource Type	Article

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