Multi-target normal behaviour models for wind farm condition monitoring

The trend towards larger wind turbines and remote locations of wind farms fuels the demand for automated condition monitoring strategies that can reduce the operating cost and avoid unplanned downtime. Normal behaviour modelling has been introduced to detect anomalous deviations from normal operation based on the turbine's SCADA data. A growing number of machine learning models of the normal behaviour of turbine subsystems are being developed by wind farm managers to this end. However, these models need to be kept track of, be maintained and require frequent updates. This research explores multi-target models as a new approach to capturing a wind turbine's normal behaviour. We present an overview of multi-target regression methods, motivate their application and benefits in SCADA-based wind turbine condition monitoring, and assess their performance in a wind farm case study. We find that multi-target models are advantageous in comparison to single-target modelling in that they can reduce the cost and effort of practical condition monitoring without compromising on the accuracy. We also outline some areas of future research.

Automated summary

The trend towards larger wind turbines and remote locations of wind farms has increased the demand for automated condition monitoring strategies that can reduce operating costs and prevent unplanned downtime. This research explores multi-target models as a new approach to capturing a wind turbine's normal behavior, which can reduce the cost and effort of practical condition monitoring without sacrificing accuracy. Future research in this area is outlined for further exploration.