Abstract
Slowly developing faults in wind turbine can, when not detected and fixed on time, cause severe damage and downtime. We are proposing a fault detection method based on Artificial Neural Networks (ANN) and the recordings from Supervisory Control and Data Acquisition (SCADA) systems installed in wind farms. We establish a model for the normal behaviour of a wind turbine from considered fault-free
data and test the proposed model on further data. We show that ANN can be used for early fault detection in wind turbines monitoring. Concerning vibrational levels in x and y directions we base our fault
detection upon a generalized-likelihood-test. An upper and a lower control bounds are established for x and y respectively, given a minimum false alarm probability η based on the statistical characteristics of the
data.
data and test the proposed model on further data. We show that ANN can be used for early fault detection in wind turbines monitoring. Concerning vibrational levels in x and y directions we base our fault
detection upon a generalized-likelihood-test. An upper and a lower control bounds are established for x and y respectively, given a minimum false alarm probability η based on the statistical characteristics of the
data.
| Original language | English |
|---|---|
| Publication date | 2015 |
| Number of pages | 1 |
| Publication status | Published - 2015 |
Keywords
- Artificial Neural Networks
- Condition Monitoring
- Fault Detection
- Supervisory Control and Data Acquisition
- Wind Turbine