Offshore wind operation and maintenance (O&M) costs could reach up to 1/3
of the overall project costs. In order to accelerate the deployment of offshore wind
farms, costs need to come down. A key contributor to the O&M costs are the
component failures and the downtime caused by them. Thus, an understanding
is needed on the ...
Offshore wind operation and maintenance (O&M) costs could reach up to 1/3
of the overall project costs. In order to accelerate the deployment of offshore wind
farms, costs need to come down. A key contributor to the O&M costs are the
component failures and the downtime caused by them. Thus, an understanding
is needed on the root cause of these failures. Previous research has indicated the
relationship between wind turbine failures and environmental conditions. These
studies are using work order data from onshore and offshore assets. A limitation
of using work orders is that the time of the failure is not known and consequently
the exact environmental conditions cannot be identified. However, if turbine alarms
are used to make this correlation, more accurate results can be derived. This paper
quantifies this relationship and proposes a novel tool for predicting wind turbine 1
fault alarms for a range of subassemblies, using wind speed statistics. A large
variation of the failures between the different subassemblies against the wind speed
is shown. The tool uses five years of operational data from an offshore wind farm
to create a data-driven predictive model. It is tested under low and high wind
conditions, showing very promising results of more than 86% accuracy on seven
different scenarios. This study is of interest to wind farm operators seeking to utilize
the operational data of their assets to predict future faults, which will allow them
to better plan their maintenance activities and have a more efficient spare part
management system.
