Proceedings of the ASME 2017 International Mechanical Engineering Congress and Exposition, IMECE2017, November 3-9, 2017, Tampa, Florida, USA

Analysis of Wind Turbine Capacity Factor Improvement by Correcting Yaw Error Using LIDAR


Roozbeh Bakhshi and Peter Sandborn
CALCE, Center for Advanced Life Cycle Engineering, Department of Mechanical Engineering, University of Maryland, College Park, Maryland 20740, USA

Abstract:

Yaw error is the angle between a turbine’s rotor central axis and the wind flow. The presence of yaw error results in lower power production from turbines. Y aw error also puts extra loads on turbine components, which in turn lowers their reliability. In this study we develop a stochastic model to calculate the average capacity factor of a 50 turbine offshore wind farm and investigate the effects of minimizing the yaw error on the capacity factor. In this paper , we define the capacity factor in terms of energy production, which is consistent with the common practice of wind farms rather than the power production capacity factor definition that is used in textbooks and research article s )). The benefit of using the energy product ion is that it incorporates both the power production improvements and downtime decreases . For minimizing the yaw error, a nacelle mounted LIDAR is used While the LIDAR is on a turbine, it collects wind speed and direction data for a period of time, which is used to calculate a correction bias for the yaw controller of the turbine , then it will be moved to another turbine in the farm to perform the same task . The results of our investigation shows that although the improvements of the capacity factor are less than the theoretical values, the extra income from the efficiency improvements is larger than the cost of the LIDAR.

This article is available to CALCE Consortium Members for personal review.

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