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 and condition-based maintenance strategies that can reduce the operating cost and avoid unplanned downtime. Normal behaviour modelling has been introduced to automatically detect anomalous deviations from normal operation based on the turbine's SCADA data. A growing number of machine learning models and related threshold values 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. Every additional model increases the overall lifetime management effort in practice. This research explores and benchmarks multi-target machine learning models as a new approach to capturing a wind turbine's normal behaviour. We present an overview of multi-target regression methods and motivate their application and benefits in wind turbine condition monitoring. As a second contribution, we evaluate and benchmark the performance of multi-target normal behaviour models in a wind turbine case study. We find that multi-target models are advantageous in comparison to single-target modelling in that they can substantially reduce the lifecycle management effort of normal behaviour models without compromising on the accuracy of the models. Finally, we also outline some areas of future research.