  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 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.
