  The cost of wind energy can be reduced by using SCADA data to detect faults
in wind turbine components. Normal behavior models are one of the main fault
detection approaches, but there is a lack of consensus in how different input
features affect the results. In this work, a new taxonomy based on the causal
relations between the input features and the target is presented. Based on this
taxonomy, the impact of different input feature configurations on the modelling
and fault detection performance is evaluated. To this end, a framework that
formulates the detection of faults as a classification problem is also
presented.
