Identifying Precursors to Frequency Fluctuation Events in Electrical Power Generation Data

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

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.

Keywords

Frequency fluctuation Change point detection Precursors Wavelet transform Correlation and regression 

Notes

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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Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  • Md. Shahidul Islam
    • 1
  • Russel Pears
    • 1
  • Boris Bačić
    • 1
  1. 1.School of Engineering, Computer and Mathematical SciencesAuckland University of TechnologyAucklandNew Zealand

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