Automated wind turbine fault detection from SCADA sensor data with machine learning methods
Authors
Iqbal, Md Ridwan Al
ORCID
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Other Contributors
Bennett, Kristin P.
Drineas, Petros
Hendler, James A.
Drineas, Petros
Hendler, James A.
Issue Date
2015-12
Keywords
Computer science
Degree
MS
Terms of Use
This electronic version is a licensed copy owned by Rensselaer Polytechnic Institute, Troy, NY. Copyright of original work retained by author.
Full Citation
Abstract
Supervisory control and data acquisition (SCADA) is a system that automatically collects data from an array of sensors. We propose to use SCADA sensor data from wind turbines and detect potential faults using machine learning techniques. However, fault detection from sensor readings with supervised learning is confronted with several challenges. There is a high amount of variability due to external conditions which reduces learning accuracy. There is also no prior label that identifies which particular turbine has entered a faulty state at a particular time. Another important challenge is the fact that the sensor data is a time series that requires specialized algorithms.
Description
December 2015
School of Science
School of Science
Department
Dept. of Computer Science
Publisher
Rensselaer Polytechnic Institute, Troy, NY
Relationships
Rensselaer Theses and Dissertations Online Collection
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