Deep autoencoder for condition monitoring of wind turbines - Detecting and diagnosing anomalies
Typ
Examensarbete för masterexamen
Program
Complex adaptive systems (MPCAS), MSc
Publicerad
2019
Författare
Renman, Johanna
Modellbyggare
Tidskriftstitel
ISSN
Volymtitel
Utgivare
Sammanfattning
Over the last decade, energy production from wind turbines has grown by 400%,
prompted by public investments and climate change awareness as well as advances
in technology. With subsidies offered to wind farms dwindling, the owners and
operators of wind farms are forced to cut operational cost to stay profitable. This
has lead to a renewed focus on predictive and preventive maintenance, targeting not
only the traditionally well monitored large components in the wind turbine, but also
including smaller, more easily replaced components. Advances in the use of machine
learning to model complex system, combined with the growing access of data have
allowed advanced methods for condition monitoring and anomaly detection to be
developed. This has been applied to the field of condition monitoring for wind
turbines in various research projects, utilizing data from the Supervisory Control
and Data Acquisition (SCADA) system available in modern wind turbines. Most
of these systems have modeled just one component at a time and are therefore
not able to provide a complete condition monitoring system. Using autoencoders
for condition monitoring of wind turbines enables the whole wind turbine to be
modeled as one system, by learning the internal connections between the SCADA
signals. This has previously been studied, but most studies focus on the anomaly
detection step, and leave out the important part of diagnosing which signals are most
affected by the anomaly. By finding these signals, the source of the fault can be
found, which is important to allow for recommendation on where to do maintenance.
This thesis investigates the application of deep autoencoders to detect and diagnose
developing faults. The autoencoder has been used to produce a residual, taken as
the error between the input to the autoencoder and its reconstructed signal. For the
fault detection, the Mahalanobis distance has been used on the residual. For fault
diagnosis, the residual for each signal has been standardized and analyzed to examine
which signals are mostly affected by the fault. This was tested on eight known faults
found in five different components: gearbox, cooling system, hydraulic system, yaw
encoder issue and generator slipring. The proposed condition monitoring system
was successful in detecting and diagnosing all faults but one.
This thesis also presents an approach to understanding what the autoencoder has
learned, with the use of simulated faults. The study provides a good method for discovering
what connections between the SCADA signals the autoencoder has learned
as well as information about how the residual is affected when one signal is experiencing
a fault.
Beskrivning
Ämne/nyckelord
Wind turbine , condition monitoring , anomaly detection , preventive maintenance , SCADA , autoencoder , neural networks , fault diagnosis , anomaly diagnosis , fault detection