NDLI: Diagnosis and identification of transformer winding faults from frequency response data by the application of ANN technique

Content Provider	IEEE Xplore Digital Library
Author	Prameela, M. Murthy, G.R. Nirgude, P.M.
Copyright Year	2012
Description	Author affiliation: Central Power Research Institute, UHV Research Laboratory, Hyderabad, India (Nirgude, P.M.) \|\| Vignan University, Guntur, Andhra Pradesh, India (Prameela, M.; Murthy, G.R.)
Abstract	In the present work, model transformer windings were used to obtain Sweep frequency Response Analyzer measurement data for various experimentally simulated faults. Transfer function and its corresponding parameters were computed for different types of transformer faults by subspace based identification method. An attempt was made to use these parameters for training Artificial Neural Network. The result of study on application of Artificial Neural Network algorithm for diagnosing and identifying the transformer winding faults from the Frequency Response Analysis data is presented. It was observed that, predicted fault closely follows the created fault suggesting the effectiveness of Artificial Neural Network technique for distinguishing between the normal and failed state quite satisfactorily.
Starting Page	1
Ending Page	4
File Size	1067379
Page Count	4
File Format	PDF
ISBN	9781467328524
ISSN	21609241
e-ISBN	9781467328517
DOI	10.1109/ICPADM.2012.6318962
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-07-24
Publisher Place	India
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Deformable models Displacement measurement Transfer functions Artificial neural networks Artificial Neural Network Frequency Response Analysis Transfer Function Subspace based system identification
Content Type	Text
Resource Type	Article

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