## 4 Results and Discussion  
Results of the comparative analysis will be presented in this section after normalization. The normalization is done by dividing the power production of each wind turbine of the row/column with the measured power production of the first wind turbine in the row/column. The reported error bar range is equal to the normalized standard deviation of the measured power production. Figure 5 below presents the results of Row 3 and 5 for the 120\({}^{\circ}\) wind direction. It is observed that the power production of the first wind turbine is captured by all methods/models. Regarding the second wind turbine of the row, however, the ACD (2016) method and the Larsen model do not capture the steep reduction of the measured power output. In contrast, the ACD (2008) method and the Jensen model give results within the error range. The power production of the third wind turbine is captured within the error range when using the ACD (2016) and the Larsen model, which is not the case for the ACD (2008) and the Jensen model. None of the methods/models can capture the sharp power production increase observed in the measurements from the second to the third wind turbine. The power production of the subsequent wind turbines in Figure 5a is captured within good accuracy when using the ACD (2016) method, whereas the other method/models underestimate the power production. Regarding Figure 5b, the increase of the power output observed between the C5 and F5 wind turbines due to the gap, is captured best by the ACD (2016) method. Other researchers have also studied the Lillgrund wind farm case. Researchers in [44] used the \(k-\varepsilon\) and the \(k-\varepsilon\)\(f_{p}\) turbulence models to investigate their effects on the simulated power production for rows 3 and 5. Their results with the \(k-\varepsilon\) turbulence model coincide with the ones presented here for the ACD (2016) method case. On the other hand, the \(k-\varepsilon\)\(f_{p}\) made better predictions of the power production, especially for the second wind turbine in the row. Furthermore, other scholars have used LES with an ACD method to simulate power production for the same wind turbines rows of the Lillgrund wind farm [23]. Their simulations predicted with better accuracy the power production of the same wind turbine rows including the power deficit of the second wind turbine and the power increase between the C5 and the F5 wind turbines. That is expected since LES fully resolves the effect of the large turbulent eddies. Results of columns B and D for the 222\({}^{\circ}\) wind direction case are presented in Figure 6.  
Figure 5: Normalized power production for the 120\({}^{\circ}\) direction (**a**) Row 3 and (**b**) Row 5. Here again the ACD (2016) method and the Larsen model over-predict the power production of the second wind turbine, whereas the ACD (2008) and the Jensen model under-predict the power production. It is known from literature that ACD models generally perform poorly in the near wake of a wind turbine [25]. For the Lillgrund wind farm with its exceptionally close spacing, this might impact the accuracy of the results. The second wind turbine in the row/column can be considered lying in the near wake region and this could explain the over-prediction of the method. For the remaining wind turbines in the rows the ACD (2016) method and the Larsen model outperform the ACD (2008) method and the [PERSON] model. In another study, [11], in which the focus of the comparative analysis was on the wind turbines positioned in column C for a main wind direction of \(222^{\circ}\) at a wind speed of \(8\pm 0.5\,\mathrm{m}\,\mathrm{s}^{-1}\), it was found that the three wake models [4, 9, 10] performed similarly for the Lillgrund case. Here we find that the [PERSON] wake model in general outperforms the [PERSON] wake model. This highlights the complexity of this type of analysis.