Puneet Kumar Singh

Work place: Department of Electrical Engineering, Faculty of Engineering, Dayalbagh Educational Institute, Agra

E-mail: puneet.kr.er@gmail.com


Research Interests: Autonomic Computing, Computing Platform, Mathematics of Computing


Puneet Kumar Singh graduated in Electrical Engineering from Dayalbagh Educational Institute, Agra (2010). He is currently pursuing his M.Tech (Engineering Systems) from Faculty of Engineering, Dayalbagh Educational Institute Agra. His area of specialization is soft computing.  He has also published several papers in national and international conferences.

Author Articles
Neural Network based Modeling and Simulation of Transformer Inrush Current

By Puneet Kumar Singh D K Chaturvedi

DOI: https://doi.org/10.5815/ijisa.2012.05.01, Pub. Date: 8 May 2012

Inrush current is a very important phenomenon which occurs during energization of transformer at no load due to temporary over fluxing. It depends on several factors like magnetization curve, resistant and inductance of primary winding, supply frequency, switching angle of circuit breaker etc. Magnetizing characteristics of core represents nonlinearity which requires improved nonlinearity solving technique to know the practical behavior of inrush current. Since several techniques still working on modeling of transformer inrush current but neural network ensures exact modeling with experimental data. Therefore, the objective of this study was to develop an Artificial Neural Network (ANN) model based on data of switching angle and remanent flux for predicting peak of inrush current. Back Propagation with Levenberg-Marquardt (LM) algorithm was used to train the ANN architecture and same was tested for the various data sets. This research work demonstrates that the developed ANN model exhibits good performance in prediction of inrush current’s peak with an average of percentage error of -0.00168 and for modeling of inrush current with an average of percentage error of -0.52913.

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