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A data-driven method to characterize turbulence-caused uncertainty in wind power generation

Jie Zhang, Rishabh Jain and Bri-Mathias Hodge

Energy, 2016, vol. 112, issue C, 1139-1152

Abstract: A data-driven methodology is developed to analyze how ambient and wake turbulence affect the power generation of wind turbine(s). Using supervisory control and data acquisition (SCADA) data from a wind plant, we select two sets of wind velocity and power data for turbines on the edge of the plant that resemble (i) an out-of-wake scenario and (ii) an in-wake scenario. For each set of data, two surrogate models are developed to represent the turbine(s) power generation as a function of (i) the wind speed and (ii) the wind speed and turbulence intensity. Three types of uncertainties in turbine(s) power generation are investigated: (i) the uncertainty in power generation with respect to the reported power curve; (ii) the uncertainty in power generation with respect to the estimated power response that accounts for only mean wind speed; and (iii) the uncertainty in power generation with respect to the estimated power response that accounts for both mean wind speed and turbulence intensity. Results show that (i) the turbine(s) generally produce more power under the in-wake scenario than under the out-of-wake scenario with the same wind speed; and (ii) there is relatively more uncertainty in the power generation under the in-wake scenario than under the out-of-wake scenario.

Keywords: Data-driven; Surrogate modeling; Uncertainty quantification; Turbulence intensity; Wind distribution (search for similar items in EconPapers)
Date: 2016
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