Abstract
Load test for wind turbines is complicated and expensive work. Equivalent load evaluation method of wind turbine through neural network was presented in the paper. The statistic data of running historical signals, which were aquired from wind turbine Multibrid M5000, were used as input data of the neural network model. The influence degrees of different running historical signals on estimation results were analysed to aquire important input parameters of neural network. Flapwise and edgewise bending of rotor blade, tower top tilt moment and tower top roll moment were estimated. In comparison with calculation results based on tested load data of Multibrid M5000, the estimation results of wind turbine loads represent very high precision and can be used to predict the life of wind turbine.
| Original language | English |
|---|---|
| Pages (from-to) | 1456-1459 |
| Number of pages | 4 |
| Journal | Taiyangneng Xuebao/Acta Energiae Solaris Sinica |
| Volume | 29 |
| Issue number | 12 |
| State | Published - Dec 2008 |
UN SDGs
This output contributes to the following UN Sustainable Development Goals (SDGs)
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SDG 7 Affordable and Clean Energy
Keywords
- Equivalent load
- Fatigue
- Neural network
- Wind turbine
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