Modeling the effects of the environment on wind turbine failure modes using neural networks

Graeme Wilson, David McMillan

Research output: Contribution to conferencePaper

Abstract

Neural networks were used to investigate if any relationship existed between maximum daily gust speed, average daily wind speed and temperature and wind turbine failure modes. Using five years of weather station data a typical site characteristic was determined using the neural network. This was then compared to a characteristic produced using only weather data for days when failures occurred. These failure and normal characteristics were then compared to determine if any relationships existed. It was found that in some sub-assemblies the failure trends differed to the normal conditions trend, suggesting that there may be relationships.
Original languageEnglish
Publication statusPublished - Sep 2012
EventIET International Conference on Sustainable Power Generation and Supply - Hangzhou, China
Duration: 8 Sep 20129 Sep 2012
http://www.ukchinanet.com/UK-China%20Network_conference%20website%20HZ.html

Conference

ConferenceIET International Conference on Sustainable Power Generation and Supply
Abbreviated titleSUPERGEN 2012
CountryChina
CityHangzhou
Period8/09/129/09/12
Internet address

Keywords

  • wind turbines
  • reliability
  • asset management
  • SAP data
  • environment
  • effects
  • wind turbine failure modes
  • neural networks

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