Anomaly Detection Techniques for the Condition Monitoring of Tidal Turbines

Grant S. Galloway, Victoria M. Catterson, Craig Love, and Andrew Robb
Submission Type: 
Full Paper
Supporting Agencies (optional): 
This work was part funded by the Energy Technology Partnership, Scotland
AttachmentSizeTimestamp
phmc_14_072.pdf658.3 KBAugust 29, 2014 - 4:01am

Harnessing the power of currents from the sea bed, tidal power has great potential to provide a means of renewable energy generation more predictable than similar technologies such as wind power. However, the nature of the operating environment provides challenges, with maintenance requiring a lift operation to gain access to the turbine above water. Failures of system components can therefore result in prolonged periods of downtime while repairs are completed on the surface, removing the system’s ability to produce electricity and damaging revenues. The utilization of effective condition monitoring systems can therefore prove particularly beneficial to this industry.
This paper explores the use of the CRISP-DM data mining process model for identifying key trends within turbine sensor data, to define the expected response of a tidal turbine. Condition data from an operational 1 MW turbine, installed off the coast of Orkney, Scotland, was used for this study. The effectiveness of modeling techniques, including curve fitting, Gaussian mixture modeling, and density estimation are explored, using tidal turbine data in the absence of faults. The paper shows how these models can be used for anomaly detection of live turbine data, with anomalies indicating the possible onset of a fault within the system.

Publication Year: 
2014
Publication Volume: 
5
Publication Control Number: 
072
Page Count: 
12
Submission Keywords: 
Condition Monitoring; Anomaly Detection; Tidal Generation; Gaussian Mixture Models; Density Estimation
Submission Topic Areas: 
Data-driven methods for fault detection, diagnosis, and prognosis
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