Towards wind farm performance optimization through empirical models

• Main Authors: Evans, Scott C., Zhanpan Zhang, Iyengar, Satish, Jianhui Chen, Hilton, John, Gregg, Peter, Eldridge, David, Jonkhof, Mark, McCulloch, Colin, Shokoohi-Yekta, Mohammad
• Format: Conference Proceeding
• Language: English
• Subjects: Analytical models; Fluid flow measurement; Machine Learning; Measurement uncertainty; Optimization; Power measurement; Predictive models; Statistical Learning; Turbines; Wind Farm Optimization; Wind Turbine Power Curve
• ISSN: 1095-323X; 2996-2358
• DOI: 10.1109/AERO.2014.6836203

Wind Turbine performance improvement measurements are challenging, especially when improvements affect air flow to the nacelle anemometer sensor which is often used to baseline performance. Uncertainty in this area can impede optimization of wind farms by making it difficult to show the benefit of upgrades to individual turbines, jointly optimize wind turbine performance in a farm, and validate the effects of optimization algorithms - particularly farm level algorithms and strategies that mitigate waking affects. In this paper we introduce methods that augment traditional methods for baselining wind turbine performance using multi-feature estimation based on empirical data and present a method for normalizing AEP uncertainty estimates. This innovative method does not rely solely on nacelle anemometer estimates or expensive additional sensors, as has been the historical approach but can leverage these trusted sensors if they are available. Future directions for whole farm optimizations are discussed.