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Identifying Precursors to Frequency Fluctuation Events in Electrical Power Generation Data

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Data Mining (AusDM 2017)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 845))

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Abstract

To predict an occurrence extraordinary phenomena, such as failures and fluctuations in an electrical power system, it is important to identify precursor events that signal an impending fluctuation event. In this paper we integrate wavelet analysis with statistical inference methods to identify a precursor pattern for frequency fluctuation prediction. The frequency time series data was converted into the wavelet domain to extract the time dynamics after which change point detection methods were used to signal significant deviations in the wavelet domain. The change points extracted were taken as early indicators of a fluctuation event. Using historical data on known fluctuation events we trained a regression model to estimate the gap between a change point and its corresponding fluctuation point. Our results show that change points could be predicted a number of time steps in advance with a low false alarm rate.

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Acknowledgments

We would like to thank Mike Phethean and Nabil Adam, Transpower NZ Ltd for supplying the data and explaining to us the complexities of power management on the New Zealand national electrical grid.

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Authors and Affiliations

  1. School of Engineering, Computer and Mathematical Sciences, Auckland University of Technology, Auckland, 1010, New Zealand

    Md. Shahidul Islam, Russel Pears & Boris Bačić

Authors
  1. Md. Shahidul Islam
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  2. Russel Pears
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  3. Boris Bačić
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Corresponding author

Correspondence to Md. Shahidul Islam .

Editor information

Editors and Affiliations

  1. RMIT University, Melbourne, Victoria, Australia

    Yee Ling Boo

  2. University of Wollongong, Wollongong, New South Wales, Australia

    David Stirling

  3. La Trobe University, Melbourne, Victoria, Australia

    Lianhua Chi

  4. School of Information Technology and Mathematical Sciences, University of South Australia, Adelaide, South Australia, Australia

    Lin Liu

  5. La Trobe University, Melbourne, Victoria, Australia

    Kok-Leong Ong

  6. Microsoft Pty Ltd, Singapore, Singapore

    Graham Williams

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Shahidul Islam, M., Pears, R., Bačić, B. (2018). Identifying Precursors to Frequency Fluctuation Events in Electrical Power Generation Data. In: Boo, Y., Stirling, D., Chi, L., Liu, L., Ong, KL., Williams, G. (eds) Data Mining. AusDM 2017. Communications in Computer and Information Science, vol 845. Springer, Singapore. https://doi.org/10.1007/978-981-13-0292-3_13

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  • DOI: https://doi.org/10.1007/978-981-13-0292-3_13

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-13-0291-6

  • Online ISBN: 978-981-13-0292-3

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