TY  - JOUR
AU  - Simani, Silvio
AU  - Farsoni, Saverio
AU  - Castaldi, Paolo
PY  - 2023
DA  - 2023/03/21
TI  - Transfer Learning for Fault Detection with Application to Wind Turbine SCADA Data
JO  - Journal of Energy and Power Technology
SP  - 011
VL  - 05
IS  - 01
AB  - The installed wind power capacity is growing worldwide. Remote condition monitoring of wind turbines is employed to achieve higher uptimes and lower maintenance costs. Machine learning models can detect developing damages in wind turbines. Therefore, this paper demonstrates that cross–turbine transfer learning can drastically improve the accuracy of fault detection models in turbines with scarce SCADA data. In particular, it shows that combining the knowledge from turbines with scarce and turbines with plentiful data enables earlier detection of faults than prior art methods. Training fault detection models require large amounts of past and present SCADA data but these data are often unavailable or not representative of the current operation behavior. Newly commissioned wind farms lack SCADA data from the previous operation. Due to control software updates or hardware replacements, older turbines may also lack representative SCADA data. After such events, a turbine’s operation behavior can change significantly so its SCADA data no longer represent its current behavior. Therefore, the work highlights how to reuse and transfer knowledge across wind turbines to overcome this lack of data and enable the earlier detection of faults in wind turbines.
SN  - 2690-1692
UR  - https://doi.org/10.21926/jept.2301011
DO  - 10.21926/jept.2301011
ID  - Simani2023
ER  -