Model Based Fault Detection of Wind Turbine Drive Trains

Abstract

Wind turbines are fundamental components of power conversion. With the increasing number of wind turbines worldwide, the exposure to harsh environmental conditions and the use of remote areas like offshore, arctic or desert regions, it has become important to predict abnormal machine behaviour as early as possible. Therefore, automated fault detection is necessary. Fault detection will prevent extensive damage in case of a fault and create time to react to faults, for example, to prepare inspections and/or to purchase spare parts. To reduce the downtime of wind turbines, fault detection systems are already widely used but more research and development need to be done. The diagnostic approach of those systems is to use pre-set fixed alert thresholds but has several drawbacks and therefore makes fault isolation difficult. To overcome the drawbacks of the common systems, this paper introduces model based condition monitoring using residual generation to wind turbine drive trains. Residuals describe the difference between the measurements and the model output. The model output represents the non-faulty system behaviour. If the measurement deviates from the model output a fault is indicated. Residual generation is a known approach for other systems. A mathematical model of a system, such as a wind turbine drive train, depends on the actual and past machine operation as well as on the ambient conditions. By knowing these conditions and being able to describe them in an analytic way, a model can be derived using the well-known state-space representation.