Farm-wide dynamic event classification as load input for wind turbine drivetrain lifetime prognosis

Research output: Chapter in Book/Report/Conference proceedingConference paperResearch

Abstract

One of the advantages of the current industrial digitalization trend, the so-called Industry 4.0, is that machines are becoming increasingly sensorized and connected to the internet. This is similar in the wind industry. Detailed measurements from hundreds of sensors embedded in the wind turbine are being sent continuously to cloud computing data-centers. Condition monitoring techniques can leverage these huge volumes of available data to increase detection potential and insights in system behavior by long-term trending. In addition to condition monitoring, these embedded sensors offer information for failure prognosis and lifetime insights. In this paper, a framework to automatically obtain the load history of different turbines within a farm is presented using high frequency SCADA. Special attention is paid to the effects of wake. The fact that data of similar machines of a fleet is collected in a central cloud environment allows for exploiting system similarity in a monitoring and root cause analysis context.
Original languageEnglish
Title of host publicationISMA2020 International Conference on Noise and Vibration Engineering
Place of PublicationLeuven, Belgium
PublisherKU Leuven
Pages3541-3551
Number of pages10
Volume29
Publication statusPublished - 2021
EventInternational Conference on Noise and Vibration Engineering 2020 - Leuven, Belgium
Duration: 7 Sep 20209 Sep 2020

Conference

ConferenceInternational Conference on Noise and Vibration Engineering 2020
Abbreviated titleISMA2020
CountryBelgium
CityLeuven
Period7/09/209/09/20

Keywords

  • Wind turbine
  • Drivetrain
  • Prognostics

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