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Novel methods for wind speeds prediction across multiple locations.

Oleg Gaidai1, Jingxiang Xu2, Ping Yan1

  • 1Shanghai Engineering Research Center of Marine Renewable Energy, College of Engineering Science and Technology, Shanghai Ocean University, Shanghai, China.

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This study introduces novel methods to assess the reliability of complex, multidimensional dynamic systems. These techniques improve the prediction of extreme values and system failure probability, even with high dimensionality and cross-correlation.

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Area of Science:

  • Engineering
  • Environmental Science
  • Statistics

Background:

  • Multidimensional nonlinear dynamic systems present significant analytical challenges.
  • Traditional time series analyses struggle with high dimensionality and cross-correlations.
  • Environmental factors like wind speed and global warming complicate system dependability studies.

Purpose of the Study:

  • To present two novel methodologies for assessing the dependability of multidimensional nonlinear dynamic systems.
  • To enable accurate prediction of extreme values and system failure probability.
  • To address limitations of traditional probability approaches in complex systems.

Main Methods:

  • Coupling a structural reliability approach with a unique extreme value prediction method.
  • Analyzing lengthy ergodic time series from measured or simulated multidimensional systems.
  • Applying the novel method to Norwegian offshore wind speed data.

Main Results:

  • The proposed methodologies effectively handle multidimensionality and cross-correlations.
  • Accurate prediction of extreme values and system failure probability is achieved.
  • Demonstrated efficacy using real-world offshore wind data.

Conclusions:

  • The coupled methodologies offer a robust solution for studying system dependability.
  • The approach facilitates efficient and simple prediction of failure probability.
  • This research advances the analysis of complex environmental and structural dynamic systems.