  Diagnosis of ice accretion on wind turbine blades is all the time a hard nut
to crack in condition monitoring of wind farms. Existing methods focus on
mechanism analysis of icing process, deviation degree analysis of feature
engineering. However, there have not been deep researches of neural networks
applied in this field at present. Supervisory control and data acquisition
(SCADA) makes it possible to train networks through continuously providing not
only operation parameters and performance parameters of wind turbines but also
environmental parameters and operation modes. This paper explores the
possibility that using convolutional neural networks (CNNs), generative
adversarial networks (GANs) and domain adaption learning to establish
intelligent diagnosis frameworks under different training scenarios.
Specifically, PGANC and PGANT are proposed for sufficient and non-sufficient
target wind turbine labeled data, respectively. The basic idea is that we
consider a two-stage training with parallel GANs, which are aimed at capturing
intrinsic features for normal and icing samples, followed by classification CNN
or domain adaption module in various training cases. Model validation on three
wind turbine SCADA data shows that two-stage training can effectively improve
the model performance. Besides, if there is no sufficient labeled data for a
target turbine, which is an extremely common phenomenon in real industrial
practices, the addition of domain adaption learning makes the trained model
show better performance. Overall, our proposed intelligent diagnosis frameworks
can achieve more accurate detection on the same wind turbine and more
generalized capability on a new wind turbine, compared with other machine
learning models and conventional CNNs.
