Wind turbine gearbox fault prognosis using high-frequency SCADA data

Verma, Ayush; Zappalá, Donatella; Sheng, Shawn; Watson, Simon J.

doi:10.1088/1742-6596/2265/3/032067

Wind turbine gearbox fault prognosis using high-frequency SCADA data

Journal Article · Sun May 01 00:00:00 EDT 2022 · Journal of Physics. Conference Series

DOI:https://doi.org/10.1088/1742-6596/2265/3/032067· OSTI ID:1874233

Verma, Ayush ^[1]; Zappalá, Donatella ^[1]; ^[2]; Watson, Simon J. ^[1]

Delft Univ. of Technology (Netherlands)
National Renewable Energy Lab. (NREL), Golden, CO (United States)

Condition-based maintenance using routinely collected Supervisory Control and Data Acquisition (SCADA) data is a promising strategy to reduce downtime and costs associated with wind farm operations and maintenance. New approaches are continuously being developed to improve the condition monitoring for wind turbines. Development of normal behaviour models is a popular approach in studies using SCADA data. This paper first presents a data-driven framework to apply normal behaviour models using an artificial neural network approach for wind turbine gearbox prognostics. A one-class support vector machine classifier, combining different error parameters, is used to analyse the normal behaviour model error to develop a robust threshold to distinguish anomalous wind turbine operation. A detailed sensitivity study is then conducted to evaluate the potential of using high-frequency SCADA data for wind turbine gearbox prognostics. The results based on operational data from one wind turbine show that, compared to the conventionally used 10-min averaged SCADA data, the use of high-frequency data is valuable as it leads to improved prognostic predictions. High-frequency data provides more insights into the dynamics of the condition of the wind turbine components and can aid in earlier detection of faults.

View Accepted Manuscript (DOE)

Research Organization:: National Renewable Energy Laboratory (NREL), Golden, CO (United States)

Sponsoring Organization:: USDOE Office of Energy Efficiency and Renewable Energy (EERE), Renewable Power Office. Wind Energy Technologies Office

Grant/Contract Number:: AC36-08GO28308

OSTI ID:: 1874233

Report Number(s):: NREL/JA-5000-81931; MainId:82704; UUID:0633ca8c-0d55-4cd5-909f-f6345f8b02d9; MainAdminID:64068

Journal Information:: Journal of Physics. Conference Series, Journal Name: Journal of Physics. Conference Series Journal Issue: 3 Vol. 2265; ISSN 1742-6588

Publisher:: IOP PublishingCopyright Statement

Country of Publication:: United States

Language:: English

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