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SCADA alarms processing for wind turbine component failure detection



Early detection of wind turbine failures facilitates the changeover from corrective maintenance towards a predictive approach, thus reducing the O&M costs. This study presents a cost-effective methodology for component failure detection purposes through the analysis of SCADA alarms. The approach categorises the alarms according to a reviewed taxonomy and then detect alarm patterns before actual failures by applying different techniques. Two case studies highlight therelationship between faulty behaviour in different components and between failures and adverse environmental conditions.
Early detection of wind turbine failures facilitates the changeover from corrective maintenance towards apredictive approach, thus reducing the O&M costs.
This study presents acost-effective methodology for component failure detection purposes through the analysis of SCADA alarms. The approach categorises the alarms
according to areviewed taxonomy [1] and then detect alarm patterns before actual failures by applying different techniques [2].
Two case studies highlight the relationship between faulty behaviour in different components and between failures and adverse environmental conditions.
The methodology translates overwhelming data into valuable information. The
automatic use of SCADA data avoids the use of additional equipment and
complex signal analysis processes.
The case studies highlight the relationship between failures and faulty
behaviour in different components and/or adverse environmental conditions.
Certain alarms could then be directly related to upcoming component failures.
Further research work will aim at overcoming the drawbacks of the techniques
by applying more complex machine learning techniques.Moreover,
component-related alarms are being correlated to deviations from normal WT
operating conditions, in terms of performance.
This project has received funding from the European Union's Horizon 2020
research and innovation programme under the Marie Skłodowska-Curie grant
agreement No 642108.(AWESOME project -
1. Wilkinson M, Hendriks B, Spinato F, Gomez E, Bulacio H, Tavner P, Feng Y and Long H.
EWEC 2010
2. Qiu Y, Feng Y, Tavner P J, Richardson P, Erdos G and Chen B (2012) Wind Energy 15
3. Reder M, Gonzalez E and Melero J J(2016) Journal of Physics: Conference Series
WT make Technology Rated Power (kW) Nb of WTs Alarm system
A Geared Generator 1500 55 1
BDirect Drive 2000 37 2
CDirect Drive 2000 19 2
D Geared Generator 850 77 3
E Geared Generator 2000 133 4
F Geared Generator 1800 9 5
G Geared Generator 2000 76 5
Reviewed Taxonomy
system, sub-system, assembly
1-month prior to the
failure (affected WT)
SCADA alarms
Failure Logs
failure data
SCADA alarms
 ↔   →   ∩  =
A = B
  = 1
  > 0
  = 0
 ↔   →   ∩  =
Grid conditions
Environmental conditions
WT operational state
Maintenance activities
Component malfunction
Manual stop or restriction
Failure following many alarms involving the hydraulic system (a2, a3, a4, a5)
and the cooling system (a6, a7, a8). A causal relationship between these
assemblies and the gearbox could be then assumed.
Alarms of a certain component resulted in afailure of another component.
The high wind speed alarm (a26)was activated several times, regardless the
time-sequence patterns with the yaw-related alarms.The significant number of
times a certain alarm is triggered could be considered as an indicator of afailure
likely to occur in the future.
Probabilistic approach reveals the link between the failure and arecurrent
alarm, that could be used as an indicator.
P(a23) = P(a26) = 2.8%
P(a23|a26) = 100%
P(a26→a23) = 2.8%
P(a26) = 36.6%
Critical components
(see [3]) 1 Blades
2 Gearbox
3 Generator
4 Pitch system
5 Yaw system
6 Transformer
7 Frequency converter
The Science of Making Torque from Wind
(TORQUE 2016)
SCADA alarms processing for wind turbine
component failure detection
Elena Gonzalez, Maik Reder and Julio J Melero
Fundación CIRCE
Elena Gonzalez
Fundación CIRCE
C/Mariano Esquillor Gomez, 15 50018 Zaragoza (Spain)
Network diagram of the SCADA
alarm patterns before the failure
Venn diagram
Network diagram of the SCADA alarm patterns before the failure
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The aim of the presented investigation is to explore the time gap between an anomaly appearance in continuously measured parameters of the device and a failure, related to the end of the remaining resource of the device-critical component. In this investigation, we propose a recurrent neural network to model the time series of the parameters of the healthy device to detect anomalies by comparing the predicted values with the ones actually measured. An experimental investigation was performed on SCADA estimates received from different wind turbines with failures. A recurrent neural network was used to predict the temperature of the gearbox. The comparison of the predicted temperature values and the actual measured ones showed that anomalies in the gearbox temperature could be detected up to 37 days before the failure of the device-critical component. The performed investigation compared different models that can be used for temperature time-series modeling and the influence of selected input features on the performance of temperature anomaly detection.
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