Comparison of anomaly detection techniques for wind turbine gearbox SCADA data: Wind Energy Science conference 2019

Conor McKinnon, James Carroll, Alasdair McDonald, Sofia Koukoura, Conaill Soraghan

Research output: Contribution to conferencePaperpeer-review

Abstract / Description of output

This analysis looks at the use of anomaly detection to assess the condition of wind turbine gearboxes based on data from a number of operational turbines. A comparison is made between various methods of anomaly detection, these being one class support vector machine (OCSVM), random forests, and nonlinear autoregressive neural networks with exogenous inputs (NARX).
Original languageEnglish
Publication statusPublished - 17 Jun 2019

Keywords / Materials (for Non-textual outputs)

  • anomaly detection
  • operations and maintenance (O&M)
  • wind turbines
  • one class support vector machine
  • neural networks with exogenous inputs
  • wind energy

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