Selection of Proper Activation Functions in Back-Propagation Neural Network Algorithm for Transformer and Transmission System Protection



This paper presents an analysis on the selection of an appropriate activation function used in neural networks for fault diagnosis decision algorithm in transformer and transmission line protection scheme. A decision algorithm based on a combination of discrete wavelet transform (DWT) and back-propagation neural networks (BPNN) is developed. The discrete wavelet transform is employed for extracting the high frequency component contained in the fault signals. The training process for the neural network and fault diagnosis decision are implemented using toolboxes on MATLAB/Simulink. The activation functions in each hidden layers and output layer have been varied to find out and to select the best activation function for fault diagnosis decision algorithm. It is found that the use of Hyperbolic tangent-function for the hidden layers, and linear activation function for the output layer gives the most satisfactory accuracy in these particular case studies.


Activation functions Back-propagation neural network Fault Protection Transmission system Transformer Wavelet transforms 



The authors wish to gratefully acknowledge financial support for this research from the King Mongkut’s Institute of Technology Ladkrabang Research fund, Thailand. The authors would like also to thank for partially supported by the Faculty of Engineering, Rajamangala University of Technology Rattanakosin Research fund.


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Copyright information

© Springer Science+Business Media Dordrecht 2015

Authors and Affiliations

  1. 1.Faculty of EngineeringKing Mongkut’s Institute of Technology LadkrabangLadkrabang, BangkokThailand
  2. 2.Faculty of EngineeringRajamangala University of Technology RattanakosinSalaya Phutthamonthon, Nakhon PathomThailand

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