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International Conference on Next Generation Computing Technologies

NGCT 2017: Smart and Innovative Trends in Next Generation Computing Technologies pp 376–389Cite as

Ensemble Algorithms for Islanding Detection in Smart Grids

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Part of the book series: Communications in Computer and Information Science ((CCIS,volume 827))

Abstract

In this paper, a predictive model for islanding detection in the presence of DG connected in smart grid is presented. In order to predict islanding state, an advanced machine learning approach, has been applied. The data is generated by carrying out simulation for various cases of islanding and non-islanding. The generated data-set for various cases is analyzed with and without dimensionality reduction using Principal Component Analysis. A hybrid classifier is then designed to correctly classify the cases into islanding & non-islanding. A comparison between various learning algorithms in terms of accuracy, precision, recall and F-score is also made.

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Author information

Authors and Affiliations

  1. Electrical and Electronics Engineering, UIET, Panjab University, Chandigarh, India

    Rubi Pandey & Damanjeet Kaur

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  1. Rubi Pandey
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  2. Damanjeet Kaur
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Correspondence to Damanjeet Kaur .

Editor information

Editors and Affiliations

  1. Indian Institute of Technology Patna, Patna, Bihar, India

    Pushpak Bhattacharyya

  2. University of Petroleum and Energy Studies, Dehradun, India

    Hanumat G. Sastry

  3. University of Petroleum and Energy Studies, Dehradun, India

    Venkatadri Marriboyina

  4. University of Petroleum and Energy Studies, Dehradun, India

    Rashmi Sharma

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Pandey, R., Kaur, D. (2018). Ensemble Algorithms for Islanding Detection in Smart Grids. In: Bhattacharyya, P., Sastry, H., Marriboyina, V., Sharma, R. (eds) Smart and Innovative Trends in Next Generation Computing Technologies. NGCT 2017. Communications in Computer and Information Science, vol 827. Springer, Singapore. https://doi.org/10.1007/978-981-10-8657-1_29

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  • DOI: https://doi.org/10.1007/978-981-10-8657-1_29

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-8656-4

  • Online ISBN: 978-981-10-8657-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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