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Study on Discrimination between Inrush and Fault in Transformer: ANN Approach | Chapter 02 | Novel Perspectives of Engineering Research Vol. 8

 Transformer protection is a critical issue in power systems because it requires precisely and swiftly distinguishing between magnetising inrush current and internal fault current. An artificial neural network has been presented and proved to be capable of solving the transformer monitoring and fault detection problem utilising a low-cost, trustworthy, and noninvasive technique. This paper presents an algorithm in which statistical parameters of detailed d1 level wavelet coefficients of signal are used as an input to the artificial neural network (ANN), which develops into a novel approach for online detection method to discriminate the magnetising inrush current and inter-turn fault, as well as the fault location, i.e. whether the interturn fault lies in primary or secondary winding, using discrete wavelet transform and artificial neural net (ANNs). Data from controlled studies was collected in the laboratory using a custom-built single-phase transformer. Following feature extraction using the discrete wavelet transform (DWT), the MLP neural network model was constructed and rigorously trained. The proposed on-line detection system is also discussed.


Author(S) Details

S. R. Paraskar
Department of Electrical Engineering, S.S.G.M.College of Engineering , Shegaon.(M.S.),44203, India.

View Book:- https://stm.bookpi.org/NPER-V8/article/view/6108

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