Journal article
Flexible non-linear predictive models for large-scale wind turbine diagnostics : Flexible non-linear predictive models for large-scale wind turbine diagnostics
Department of Applied Mathematics and Computer Science, Technical University of Denmark1
Cognitive Systems, Department of Applied Mathematics and Computer Science, Technical University of Denmark2
Siemens Diagnostic Center for Wind Turbines3
Department of Applied Electronics, Technical University of Denmark4
Department of Informatics and Mathematical Modeling, Technical University of Denmark5
We demonstrate how flexible non-linear models can provide accurate and robust predictions on turbine component temperature sensor data using data-driven principles and only a minimum of system modeling. The merits of different model architectures are evaluated using data from a large set of turbines operating under diverse conditions.
We then go on to test the predictive models in a diagnostic setting, where the output of the models are used to detect mechanical faults in rotor bearings. Using retrospective data from 22 actual rotor bearing failures, the fault detection performance of the models are quantified using a structured framework that provides the metrics required for evaluating the performance in a fleet wide monitoring setup.
It is demonstrated that faults are identified with high accuracy up to 45 days before a warning from the hard-threshold warning system.
Language: | English |
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Year: | 2017 |
Pages: | 753-764 |
ISSN: | 10991824 and 10954244 |
Types: | Journal article |
DOI: | 10.1002/we.2057 |