Wind turbines are typically installed in multiple rows inside wind farms. In these configurations, the upstream turbines create a wake flow characterized with reduced wind speed and increased turbulence, which reduces the energy production and induces more severe fatigue loads of downstream turbines.

Wake effects are recognized as one of the largest source of uncertainty in wind farm loads and power production predictions [1]. The accuracy of wake-induced effect predictions is relevant for several purposes such as estimation of turbine lifetime, structural reliability analysis and for optimizing the turbine operation. The need for improved wake models has driven a significant advancement in computational fluid dynamics (CFD) methods, with promising results [2]. However, these methods are very computationally demanding and they are not feasible for full load basis analysis of wind turbines. The Dynamic Wake Meandering (DWM) model [3] is an alternative engineering approach to CFD, which predicts the wind speed deficit together with a meandering process to simulate the flow field in wakes [4]. Several researches have validated the performance of the DWM with high-fidelity CFD simulations and field data [4]–[9]. Although previous studies have demonstrated the feasibility of the DWM, they have not adequately characterized the statistical errors and uncertainties introduced by the model. Furthermore, novel approaches to retrieve wake parameters using nacelle-based lidars [10], enable opportunities to calibrate the DWM using full-scale wakes measurements obtained directly from wind turbines. The objective of this study is to define and demonstrate procedures to validate and potentially calibrate the DWM, using wake measurements from nacelle-mounted lidars, and to characterize the resulting uncertainty of predicted loads against measured loads.

Reference: 

[1] Walker, “An evaluation of the predictive accuracy of wake effects models for offshore wind farms,” 2015.

[2] A. Makridis and J. Chick, “Validation of a CFD model of wind turbine wakes with terrain effects,” J. Wind Eng. Ind. Aerodyn., vol. 123, pp. 12–29, 2013.

[3] G. C. Larsen, H. A. Madsen, K. Thomsen, and T. J. Larsen, “Wake meandering: A pragmatic approach,” Wind Energy, vol. 11, no. 4, pp. 377–395, 2008.

[4] T. J. Larsen, H. A. Madsen, G. C. Larsen, and K. S. Hansen, “Validation of the dynamic wake meander model for loads and power production in the Egmond aan Zee wind farm,” Wind Energy, vol. 16, no. 4, pp. 605–624, 2013.

[5] T. J. Larsen, G. Larsen, H. Aagaard Madsen, and S. M. Petersen, “Wake effects above rated wind speed . An overlooked contributor to high loads in wind farms,” Sci. Proceedings, EWEA Annu. Conf. Exhib. Paris, Fr., pp. 95–99, 2015.

[6] H. A. Madsen, G. C. Larsen, and K. Thomsen, “Wake flow characteristics in low ambient turbulence conditions ” Copenhagen Offshore Wind 2005 ”,” Design, no. January 2005, 2005.

[7] H. A. Madsen, G. C. Larsen, T. J. Larsen, N. Troldborg, and R. Mikkelsen, “Calibration and Validation of the Dynamic Wake Meandering Model for Implementation in an Aeroelastic Code,” J. Sol. Energy Eng., vol. 132, no. 4, p. 041014, 2010.

[8] Bingol, “Light detection and ranging measurements of wake dynamics part I: one‐dimensional scanning,” Wind energy, vol. 13, pp. 51–61, 2010.

[9] Trujillo, “Light detection and ranging measurements of wake dynamics. Part II: two-dimensional scanning,” 2011.

[10] D. P. Held and J. Mann, “Wake detection in the turbine in ow using nacelle lidars,” Submitt. to Wind Energy Sci., no. 2018, 2018.