Wind turbine generator prognostics using field SCADA data

R.A. Peter*, D. Zappalá, Verena Schamboeck, S.J. Watson

*Corresponding author for this work

Research output: Contribution to journalConference articleScientificpeer-review

4 Citations (Scopus)
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This paper presents a novel prognostic method to estimate the remaining useful life (RUL) of generators using the SCADA (Supervisory Control And Data Acquisition) systems installed in wind turbines. A data-driven wind turbine anomaly classification method is developed. The anomalies are quantified into a health indicator to measure the component degradation over time. An Autoregressive Integrated Moving Average (ARIMA) time series forecasting technique is then applied to predict the RUL of the wind turbine generator. The proposed method has been validated using industry field data showing accurate predictions of RUL with a 21 day lead time for maintenance of the turbine.
Original languageEnglish
Article number032111
Number of pages11
JournalJournal of Physics: Conference Series
Issue number3
Publication statusPublished - 2022
EventTORQUE 2022 - Delft, Netherlands
Duration: 1 Jun 20223 Jun 2022
Conference number: 9

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