A Data-Driven Method to Characterize Turbulence-Caused Uncertainty in Wind Power Generation

Brian Hodge, Jie Zhang, Rishabh Jain

Research output: Contribution to journalArticlepeer-review

8 Scopus Citations

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.

Original languageAmerican English
Pages (from-to)1139-1152
Number of pages14
JournalEnergy
Volume112
DOIs
StatePublished - 2016

Bibliographical note

Publisher Copyright:
© 2016 Elsevier Ltd

NREL Publication Number

  • NREL/JA-5D00-66925

Keywords

  • Data-driven
  • Surrogate modeling
  • Turbulence intensity
  • Uncertainty quantification
  • Wind distribution

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