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http://dx.doi.org/10.5207/JIEIE.2003.17.5.043

Development of Fault Detection Method for a Transformer Using Neural Network  

김일남 (한국수자원공사)
김남호 (강원대학교 전기공학과)
Publication Information
Journal of the Korean Institute of Illuminating and Electrical Installation Engineers / v.17, no.5, 2003 , pp. 43-50 More about this Journal
Abstract
This presents a fault detecting method for a power transformer based upon a neural network. To maintain a normal relay operating conditions, external winding faults of a power transformer and magnetic inrush have been tested under consideration of the EMTP/ATP software and internal faults of power transformer have been tested by the EMTP/BCTRAN software. The neural network has been evaluated by the proposed fault. Input variables of the neural network for the proposed model can be obtained from fundamental currents, restraining and operating currents. This algorithm uses back-propagation and the ratio of a restraining current and an operating current as relay input parameters. The ratio may enhance the fault detection since the restraining currents increase rapidly at external faults. The proposed detecting method has been applied to the practical relay systems for transformer protection. As a result, the proposed detecting method based on the neural network has been shown to have better characteristics.
Keywords
Neural Network; EMTP; Power Transformer Fault; Fault Detection;
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