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).
|Number of pages||1|
|Publication status||Published - 17 Jun 2019|
|Event||Wind Energy Science conference 2019 - University College Cork, Cork, Ireland|
Duration: 17 Jun 2019 → 20 Jun 2019
Conference number: 2019
|Conference||Wind Energy Science conference 2019|
|Period||17/06/19 → 20/06/19|
- anomaly detection
- operations and maintenance (O&M)
- wind turbines
- one class support vector machine
- neural networks with exogenous inputs
- wind energy
Mckinnon, C., Carroll, J., McDonald, A., Koukoura, S., & Soraghan, C. (2019). Comparison of anomaly detection techniques for wind turbine gearbox SCADA data. Abstract from Wind Energy Science conference 2019, Cork, Ireland.