Load and Risk Based Maintenance Management of Wind Turbines
The cost of maintenance is a considerable part of the total life cycle cost in wind turbines, especially for offshore applications. Research has shown that some critical components account for most of the downtime in the wind turbines. An improvement of maintenance practices and focused condition based maintenance for critical components can improve the reliability of the wind turbines; at the same time appropriate maintenance management can reduce maintenance costs.
This thesis presents the conceptual application of the reliability centered asset management (RCAM) approach, which was defined for electrical distribution systems by Bertling in 2005, to wind turbine application. Following the RCAM approach failure statistics extracted from the maintenance records of 28 onshore wind turbines, rated 2MW, are presented. It is realized from the statistics that gearbox is a critical component for the system and the gearbox bearings are major cause of failures in gearboxes.
A maintenance management framework called self evolving maintenance scheduler (SEMS) is proposed in the thesis. The SEMS framework considers the indication of deterioration from various condition monitoring systems to formulate an optimal maintenance strategy for the damaged component. In addition to SEMS, an artificial neural network (ANN) based condition monitoring approach using the data stored in the supervisory control and data acquisition (SCADA) system is proposed. The proposed approach uses a statistical distance measurement called Mahalanobis distance to identify any abnormal operation of monitored component. A self evolving feature to keep the ANN model up-to-date with the changing operating conditions is also proposed.
The proposed ANN based condition monitoring approach is applied for gearbox bearing monitoring to two cases with real SCADA data, from two wind turbines of the same manufacturer, rated 2 MW, and situated in the south of Sweden. The results show that the proposed approach is capable of detecting damage in the gearbox bearings in good time before a complete failure. The application of the proposed condition monitoring approach with the SEMS maintenance management framework has a potential to reduce the maintenance cost for critical components close to end of life.
supervisory control and data acquisition (SCADA)
condition monitoring system (CMS)
Artificial neural networks (ANN)
life cycle cost