Using Machine Learning to Create Turbine Performance Models (Presentation)
Wind turbine power output is known to be a strong function of wind speed, but is also affected by turbulence and shear. In this work, new aerostructural simulations of a generic 1.5 MW turbine are used to explore atmospheric influences on power output. Most significant is the hub height wind speed, followed by hub height turbulence intensity and then wind speed shear across the rotor disk. These simulation data are used to train regression trees that predict the turbine response for any combination of wind speed, turbulence intensity, and wind shear that might be expected at a turbine site. For a randomly selected atmospheric condition, the accuracy of the regression tree power predictions is three times higher than that of the traditional power curve methodology. The regression tree method can also be applied to turbine test data and used to predict turbine performance at a new site. No new data is required in comparison to the data that are usually collected for a wind resource assessment. Implementing the method requires turbine manufacturers to create a turbine regression tree model from test site data. Such an approach could significantly reduce bias in power predictions that arise because of different turbulence and shear at the new site, compared to the test site.
- Research Organization:
- National Renewable Energy Lab. (NREL), Golden, CO (United States)
- Sponsoring Organization:
- USDOE Office of Energy Efficiency and Renewable Energy Wind Power Program
- DOE Contract Number:
- AC36-08GO28308
- OSTI ID:
- 1079091
- Report Number(s):
- NREL/PR-5000-58314
- Resource Relation:
- Conference: Presented at the Power Curve Working Group, 12 March 2013, Brande, Denmark; Related Information: NREL (National Renewable Energy Laboratory)
- Country of Publication:
- United States
- Language:
- English
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