Abstract
AbstractWind farm control (WFC) algorithms rely on an estimate of the ambient wind speed, wind direction, and turbulence intensity in the determination of the optimal control setpoints. However, the measurements available in a commercial wind farm do not always carry sufficient information to estimate these atmospheric quantities. In this paper, a novel measure (“observability”) is introduced that quantifies how well the ambient conditions can be estimated with the measurements at hand through a model inversion approach. The usefulness of this measure is shown through several case studies. While the turbine power signals and the inter‐turbine wake interactions provide information on the wind direction, the case studies presented in this article show that there is a strong need for wind direction measurements for WFC to sufficiently cover observability for any ambient condition. Further, generally, more wake interaction leads to a higher observability. Also, the mathematical framework presented in this article supports the straightforward notion that turbine power measurements provide no additional information compared with local wind speed measurements, implying that power measurements are superfluous. Irregular farm layouts result in a higher observability due to the increase in unique wake interaction. The findings in this paper may be used in WFC to predict which ambient quantities can (theoretically) be estimated. The authors envision that this will assist in the estimation of the ambient conditions in WFC algorithms and can lead to an improvement in the performance of WFC algorithms over the complete envelope of wind farm operation.
Highlights
The European Wind Energy Association (EWEA) predicts the amount of installed wind energy to increase from 106 GW in 2012 to 735 GW in 2050, which at that point should provide for about 50% of the European Union's electricity demand.[1]
While the turbine power signals and the inter-turbine wake interactions provide information on the wind direction, the case studies presented in this article show that there is a strong need for wind direction measurements for Wind farm control (WFC) to sufficiently cover observability for any ambient condition
The scientific community surrounding WFC has shown an increasing amount of interest towards the real-time estimation of the ambient conditions inside a wind farm. This ambient flow information is essential to the optimization of the turbine yaw angles for wake steering, which is currently the most popular methodology of WFC for power maximization
Summary
The European Wind Energy Association (EWEA) predicts the amount of installed wind energy to increase from 106 GW in 2012 to 735 GW in 2050, which at that point should provide for about 50% of the European Union's electricity demand.[1]. The interest has largely shifted towards wind farm control (WFC), in which multiple turbines inside a wind farm are coordinated together to improve their combined energy yield.[2] WFC addresses the issue of wakes, which are slower and more turbulent pockets of air that form behind a wind turbine as energy is extracted. Wake formation has led up to an estimated 23% loss in the annual energy yield of the closely spaced Lillgrund offshore wind farm at the coast of Sweden compared WITH an idealized situation without wake formation.[3] The underlying concept of WFC is to influence the wake such that it has a smaller impact on downstream turbines. A popular approach in the literature is yaw-based wake steering, in which the wake position is shifted laterally by purposely operating an upstream turbine at a yaw misalignment. Recent studies have shown the potential of yaw-based wake steering for wind farm power maximization
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