Abstract
Abstract. Methods of turbine wake modeling are being developed to more accurately account for spatially variant atmospheric conditions within wind farms. Most current wake modeling utilities are designed to apply a uniform flow field to the entire domain of a wind farm. When this method is used, the accuracy of power prediction and wind farm controls can be compromised depending on the flow-field characteristics of a particular area. In an effort to improve strategies of wind farm wake modeling and power prediction, FLOw Redirection and Induction in Steady State (FLORIS) was developed to implement sophisticated methods of atmospheric characterization and power output calculation. In this paper, we describe an adapted FLORIS model that features spatial heterogeneity in flow-field characterization. This model approximates an observed flow field by interpolating from a set of atmospheric measurements that represent local weather conditions. The objective of this method is to capture heterogeneous atmospheric effects caused by site-specific terrain features, without explicitly modeling the geometry of the wind farm terrain. The implemented adaptations were validated by comparing the simulated power predictions generated from FLORIS to the actual recorded wind farm output from the supervisory control and data acquisition (SCADA) recordings and large eddy simulations (LESs). When comparing the performance of the proposed heterogeneous model to homogeneous FLORIS simulations, the results show a 14.6 % decrease for mean absolute error (MAE) in wind farm power output predictions for cases using wind farm SCADA data and a 18.9 % decrease in LES case studies. The results of these studies also indicate that the efficacy of the proposed modeling techniques may vary with differing site-specific operational conditions. This work quantifies the accuracy of wind plant power predictions under heterogeneous flow conditions and establishes best practices for atmospheric surveying for wake modeling.
Highlights
Low-fidelity wake modeling utilities such as FLOw Redirection and Induction in Steady State (FLORIS) are typically used for the estimation of wind farm power output or the implementation of wind farm controls that help improve the overall performance of a wind farm
This adapted version of FLORIS presents several novel developments within the scope of control-oriented wake modeling research: an interpolation algorithm is implemented, which allows the user to define a gradient of atmospheric characteristics across the flow field, based on several measurements within or adjacent to the wind farm; elements of spatially variant wind direction, wind speed, and turbulence intensity are integrated into wake calculations of the preexisting FLORIS model; and an additional method is introduced to minimize error in power prediction accuracy caused by highturbulence intensity and wind speed variance
This article introduces a method to include heterogeneous flow fields into the FLORIS simulation tool, as well as a turbulence correction to the power reported at each turbine
Summary
Low-fidelity wake modeling utilities such as FLOw Redirection and Induction in Steady State (FLORIS) are typically used for the estimation of wind farm power output or the implementation of wind farm controls that help improve the overall performance of a wind farm. As a contribution to this area of research, this article will present a modified version of FLORIS that features an advantageous capability in modeling wind farms with spatially variant weather conditions and complex terrain This adapted version of FLORIS presents several novel developments within the scope of control-oriented wake modeling research: an interpolation algorithm is implemented, which allows the user to define a gradient of atmospheric characteristics across the flow field, based on several measurements within or adjacent to the wind farm; elements of spatially variant wind direction, wind speed, and turbulence intensity are integrated into wake calculations of the preexisting FLORIS model; and an additional method is introduced to minimize error in power prediction accuracy caused by highturbulence intensity and wind speed variance. This study aims to analyze the accuracy of power output predictions and wake modeling performance for the proposed wake model, through comparisons to large eddy simulation (LES) wind farm supervisory control and data acquisition (SCADA) records
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