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Customized Hidden Layered ANN Based Pattern Recognition Technique for Differential Protection of Power Transformer

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Proceedings of the 11th International Conference on Soft Computing and Pattern Recognition (SoCPaR 2019) (SoCPaR 2019)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1182))

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Abstract

This article presents the use of Customized Multi-Layer ANN based Pattern Recognition Technique for the numerical differential protection of a power transformer. An efficient Resilient Back Propagation trained neural network model with customized parallel hidden layers is proposed for the said purpose. The task of the ANN is to discriminate among various operating conditions of the transformer and issue trip signal, only in the case of internal fault. The data base required for the training of algorithm is obtained by using MATLAB/SIMULINK environment.

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Author information

Authors and Affiliations

  1. Vardhaman College of Engineering, Hyderabad, India

    Harish Balaga & Deepthi Marrapu

Authors
  1. Harish Balaga
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  2. Deepthi Marrapu
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Corresponding author

Correspondence to Harish Balaga .

Editor information

Editors and Affiliations

  1. Scientific Network for Innovation and Research Excellence, Machine Intelligence Research Labs (MIR), Auburn, WA, USA

    Ajith Abraham

  2. Department of Computer Science and Engineering, Vardhaman College of Engineering, Hyderabad, Telangana, India

    M. A. Jabbar

  3. Departamento de Inteligencia Artificial, Universidad Politécnica de Madrid, Madrid, Spain

    Sanju Tiwari

  4. ISEP - Instituto Superior de Engenharia do Porto, Porto, Portugal

    Isabel M. S. Jesus

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Balaga, H., Marrapu, D. (2021). Customized Hidden Layered ANN Based Pattern Recognition Technique for Differential Protection of Power Transformer. In: Abraham, A., Jabbar, M., Tiwari, S., Jesus, I. (eds) Proceedings of the 11th International Conference on Soft Computing and Pattern Recognition (SoCPaR 2019). SoCPaR 2019. Advances in Intelligent Systems and Computing, vol 1182. Springer, Cham. https://doi.org/10.1007/978-3-030-49345-5_15

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