Flexible non-linear predictive models for large-scale wind turbine diagnostics

Research output: Research - peer-reviewJournal article – Annual report year: 2017

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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.

Original languageEnglish
JournalWind Energy
Volume20
Issue number5
Pages (from-to)753-764
Number of pages12
ISSN1095-4244
DOIs
StatePublished - 1 May 2017
CitationsWeb of Science® Times Cited: 1

    Research areas

  • condition monitoring, data analysis, fault-detection
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