Investigations on Incipient Fault Diagnosis of Power Transformer Using Neural Networks and Adaptive Neurofuzzy Inference System

Continuity of power supply is of utmost importance to the consumers and is only possible by coordination and reliable operation of power system components. Power transformer is such a prime equipment of the transmission and distribution system and needs to be continuously monitored for its well-bein...

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Bibliographic Details
Published in:Applied computational intelligence and soft computing 2014-01, Vol.2014 (2014), p.1-9
Main Authors: Wagh, Nandkumar, Deshpande, D. M.
Format: Article
Language:English
Subjects:
Accuracy
Adaptive systems
Algorithms
Artificial intelligence
Back propagation
Design
Diagnosis
Fault diagnosis
Fuzzy logic
Fuzzy sets
Gases
Inference
Input output
Intelligence
Investigations
Methods
Neural networks
Parameter estimation
Testing laboratories
Transformers
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Summary:Continuity of power supply is of utmost importance to the consumers and is only possible by coordination and reliable operation of power system components. Power transformer is such a prime equipment of the transmission and distribution system and needs to be continuously monitored for its well-being. Since ratio methods cannot provide correct diagnosis due to the borderline problems and the probability of existence of multiple faults, artificial intelligence could be the best approach. Dissolved gas analysis (DGA) interpretation may provide an insight into the developing incipient faults and is adopted as the preliminary diagnosis tool. In the proposed work, a comparison of the diagnosis ability of backpropagation (BP), radial basis function (RBF) neural network, and adaptive neurofuzzy inference system (ANFIS) has been investigated and the diagnosis results in terms of error measure, accuracy, network training time, and number of iterations are presented.
ISSN:1687-9724
1687-9732
DOI:10.1155/2014/845815