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Evaluation of the Applicability and Advantages of Application of Artificial Neural Network Based Scanning System for Grid Networks

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Progress in Advanced Computing and Intelligent Engineering

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1198))

Abstract

This chapter presents the application of an artificial neural network-based monitoring system power grid network. Neural net modules used for this study are of two kinds, a distributed separate artificial neural net (ANN) module to monitor all lines individually from separate points in the network and central common multiple-input, multiple-layer ANN to monitor all lines together. Only the active power flowing on all the lines of the utility network were monitored using the ANN’s. This work elaborates and evaluates the technical repercussions of both the modules. The ANN model employed was a feed-forward net with backpropagation of error. The aspiration of the task is to deliberate on the opportunities and obstacles of the various configurations of ANN models employed.

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Acknowledgements

This project was technically supported by the professors and staff at the Electrical Engineering Department at Jadavpur University, Kolkata, since February 2017. The financial support was arranged by the Ministry of Human Resource Development under the Indian Government and the Technical-Education-Quality-Improvement-Programme, Phase-2, under grant to registration number R/2016/0010, from February 2015.

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Authors and Affiliations

  1. Electrical Engineering Department, IIEST, Shibpur, Howrah, West Bengal, 711103, India

    Shubhranshu Kumar Tiwary, Jagadish Pal & Chandan Kumar Chanda

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  1. Shubhranshu Kumar Tiwary
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  2. Jagadish Pal
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  3. Chandan Kumar Chanda
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Correspondence to Shubhranshu Kumar Tiwary .

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Editors and Affiliations

  1. Department of Computer Science, Rama Devi Women’s University, Bhubaneswar, Odisha, India

    Chhabi Rani Panigrahi

  2. Department of Computer Science, Rama Devi Women’s University, Bhubaneswar, Odisha, India

    Bibudhendu Pati

  3. Department of Computer Science, University of California, Davis, CA, USA

    Prasant Mohapatra

  4. Cloud Computing and Distributed Systems (CLOUDS) Laboratory, School of Computing and Information Systems, University of Melbourne, Melbourne, VIC, Australia

    Rajkumar Buyya

  5. Department of Computer Science and Information Engineering, Providence University, Taichung, Taiwan

    Kuan-Ching Li

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Tiwary, S.K., Pal, J., Chanda, C.K. (2021). Evaluation of the Applicability and Advantages of Application of Artificial Neural Network Based Scanning System for Grid Networks. In: Panigrahi, C.R., Pati, B., Mohapatra, P., Buyya, R., Li, KC. (eds) Progress in Advanced Computing and Intelligent Engineering. Advances in Intelligent Systems and Computing, vol 1198. Springer, Singapore. https://doi.org/10.1007/978-981-15-6584-7_23

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  • DOI: https://doi.org/10.1007/978-981-15-6584-7_23

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