A data-driven method to characterize turbulence-caused uncertainty in wind power generation. (1st October 2016)
- Record Type:
- Journal Article
- Title:
- A data-driven method to characterize turbulence-caused uncertainty in wind power generation. (1st October 2016)
- Main Title:
- A data-driven method to characterize turbulence-caused uncertainty in wind power generation
- Authors:
- Zhang, Jie
Jain, Rishabh
Hodge, Bri-Mathias - Abstract:
- 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. Highlights: A method was developed to characterize turbulence-caused uncertainty in wind power. Three types of uncertainties in turbine(s) power generation were investigated. Under same windAbstract: 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. Highlights: A method was developed to characterize turbulence-caused uncertainty in wind power. Three types of uncertainties in turbine(s) power generation were investigated. Under same wind conditions, a turbine produced more power in the in-wake scenario. More uncertainty present in the in-wake scenario than in the out-of-wake scenario. … (more)
- Is Part Of:
- Energy. Volume 112(2016)
- Journal:
- Energy
- Issue:
- Volume 112(2016)
- Issue Display:
- Volume 112, Issue 2016 (2016)
- Year:
- 2016
- Volume:
- 112
- Issue:
- 2016
- Issue Sort Value:
- 2016-0112-2016-0000
- Page Start:
- 1139
- Page End:
- 1152
- Publication Date:
- 2016-10-01
- Subjects:
- Data-driven -- Surrogate modeling -- Uncertainty quantification -- Turbulence intensity -- Wind distribution
Power resources -- Periodicals
Power (Mechanics) -- Periodicals
Energy consumption -- Periodicals
333.7905 - Journal URLs:
- http://www.elsevier.com/journals ↗
- DOI:
- 10.1016/j.energy.2016.06.144 ↗
- Languages:
- English
- ISSNs:
- 0360-5442
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library DSC - 3747.445000
British Library DSC - BLDSS-3PM
British Library HMNTS - ELD Digital store - Ingest File:
- 1832.xml