NDLI: Predicting transformers oil parameters

Content Provider	IEEE Xplore Digital Library
Author	Shaban, K. El-Hag, A. Matveev, A.
Copyright Year	2009
Description	Author affiliation: University of Waterloo, Canada (Matveev, A.) \|\| American University of Sharjah, UAE (El-Hag, A.) \|\| Department of Computer Science and Engineering, Qatar University, Doha, Qatar (Shaban, K.)
Abstract	In this paper different configurations of artificial neural networks are applied to predict various transformers oil parameters. The prediction is performed through modeling the relationship between the transformer insulation resistance extracted from the Megger test and the breakdown strength, interfacial tension, acidity and the water content of the transformers oil. The process of predicting these oil parameters statuses is carried out using two different configurations of neural networks. First, a multilayer feed forward neural network with a back-propagation learning algorithm is implemented. Subsequently, a cascade of these neural networks is deemed to be more promising. Both configurations are evaluated using real-world training and testing data and the accuracy is calculated across a variety of hidden layer and hidden node combinations. The results indicate that even with a lack of sufficient data to train the network, accuracy levels of 83.9% for breakdown voltage, 94.6% for interfacial tension, 56.4% for water content, and 75.4% for oil acidity predictions were obtained by the cascade of neural networks.
Starting Page	196
Ending Page	199
File Size	429449
Page Count	4
File Format	PDF
ISBN	9781424439157
DOI	10.1109/EIC.2009.5166344
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-05-31
Publisher Place	Canada
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Oil insulation Neural networks Petroleum Multi-layer neural network Artificial neural networks Performance evaluation Predictive models Power transformer insulation Insulation testing Electric breakdown
Content Type	Text
Resource Type	Article

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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