  We provide a condition monitoring system for wind farms, based on normal
behaviour modelling using a probabilistic multi-layer perceptron with transfer
learning via fine-tuning. The model predicts the output power of the wind
turbine under normal behaviour based on features retrieved from supervisory
control and data acquisition (SCADA) systems. Its advantages are that (i) it
can be trained with SCADA data of at least a few years, (ii) it can incorporate
all SCADA data of all wind turbines in a wind farm as features, (iii) it
assumes that the output power follows a normal density with heteroscedastic
variance and (iv) it can predict the output of one wind turbine by borrowing
strength from the data of all other wind turbines in a farm. Probabilistic
guidelines for condition monitoring are given via a CUSUM control chart. We
illustrate the performance of our model in a real SCADA data example which
provides evidence that it outperforms other probabilistic prediction models.
