Over the last few decades, wind power has shown a continuous and significant developmentin the energy market globally. Having reached a certain level in both technologyand in dimensions, the role of optimizing wind turbines as well as wind farms hasbecome an additional aspect to future development and research. Since turbine wakescan cause significant power deficits within a farm, research in this area has the potentialfor large improvements in wind farm design. A wake is described as the downstream flow behind the rotor of an operating windturbine. The two main characteristics of wakes are a velocity (momentum) deficit and anincreased turbulence level. The velocity deficit behind the upwind turbine results in apower loss of the downstream turbines, whereas the higher turbulence causes additionalloads on the downstream turbines’ structures resulting in fatigue problems. The study of wakes is a complex topic, they are influenced by an interconnection of anumber of parameters like ambient wind speed and turbulence, atmospheric stabilityconditions (stable, unstable, and neutral), the turbines’ operational characteristics, andthe terrain properties. In order to assess the power deficits affected by wake interaction between turbines,an analysis can be realized by processing SCADA data of turbines in a wind farm. The collected data is treated by a comprehensive filtration process, excluding events of icing, curtailment, faults, etc. and by grouping into different atmospheric conditions, windspeed intervals and wind speed sectors. Finally, power deficit values, as a function ofwind direction, are calculated and quantified, and thereafter analyzed to assess the wakebehavior at different conditions for different cases.In this thesis, the wake-induced power deficit has been investigated in a specificstudy case for three pairs of two neighboring turbines in a forested moderately complexterrain using SCADA data. The production losses amounted between the range of 32% to 67% for the specific site with turbine spacing around 4D. The obtained results werepartially unsatisfactory, caused by the reasons of inaccurate wind direction values due toyaw misalignment issues and challenging separation into different stability conditions. Moreover, the power deficits showed a clear reduction of losses with increasing windspeed. A conclusion regarding the differences between stable and near neutral conditionscould not be determined from the data.

Modelling the wake effect generated by wind turbines is an essential part for calcu- lating a wind farm’s expected energy production. Operating wind turbines disturb the flow of the wind, which results in decreased production of downwind turbines. The N. O. Jensen model is an industry standard wake model that assumes a linear expansion of the downstream wake. The only adjustable parameter in the model is the wake decay constant (WDC), which has traditionally been derived semi em- pirically from terrain surface roughness. However, the WDC defines the expansion rate of the generated wake, and therefore can be linked to the ambient turbulence intensity (TI): high ambient turbulence leads to a faster decay of the generated wake, and therefore to lower wake losses, and vice-versa. Since the influence of the roughness on the ambient turbulence intensity is expected to be less significant at higher heights, these roughness-based WDC values are rather uncertain for the hub heights employed nowadays. The following study presents the results of a comparison between observed and mod- elled wake losses based on different WDC values. To investigate how a change in height affects the wake modelling, two wake scenarios occurring between two tur- bine sets with different hub heights are selected from an operational wind farm. By modelling the wakes using roughness as well as turbulence intensity-based WDCs, conclusions can be drawn on how the predictive capability of the N.O. Jensen model depends on the selection of a suitable WDC value. Finally it is concluded that the goodness of fit between modelled and observed wake losses shows a clear dependency on the wind speed/power production inter- val. At higher wind speeds, the TI-based WDC resulted in a better accuracy of the modelled wake losses as compared to the roughness-based WDC, while for lower wind speeds the N. O. Jensen model performed most accurately when using WDC = 0.075. However, for the investigated cases the overall accuracy of the modelled wake appears to be higher when choosing WDC = 0.075 instead of a TI-based WDC.

Climate change is mankind’s biggest challenge and scientists around the globe agree that civilization is pushing towards a breaking point. Renewable energy are alternatives that are capable to remove the need for fossil fuel. Wind power will play a vital role and has the possibility to confront the challenges that face the globe. In order for wind power to reach its full potential constructors need to take into account the distance between each wind power turbine, as it can cause energy loss and generate less electricity into the system. These energy losses decrease the potential of wind power and thus also for renewable as a whole. Energy losses that emerge within the space between wind power plants are named wake losses. Once the wind has passed the plant, a distance equal to seven rotor diameters is needed for the wind to regain its full force. By positioning the plants within the announced distance, the production of each plant decreases since downstream turbines are not able to generate a full effect.       This Bachelor thesis in Energy Engineering aims to analyse these wake losses for the wind power plant Skäppentorp, which is situated in Mönsterås County. The nearby wind power plant Brotorp is affecting Skäppentorps production and the authors of this degree project chose to present the wake losses as a percentage. A third wind power plant named Idhult functioned as a reference. Idhult is of course not affected before the positioning of Brotorp but neither after it, therefore the plant was used to ensure that weak winds were not ascribed to Brotorp but are a result of a weak wind year. The Bachelor thesis covered thus three wind power plants, Skäppentorp which interacts and is affected by Brotorp and Idhult which served as reference. The wake losses were calculated in Microsoft Excel and set against the software windPRO to validate the programmed produced losses for the same plant. Skäppentorp’s surrounding were divided into 12 sectors, where each sector covers an angle of 30 degrees. By doing so a full circle, 360 degrees, surrounding the plant was established. The wind speed and the production before respectively after Brotorp deployment was produced by using a nearby measuring post. Via an average production value for each sector, before and after Brotorp, a percentage wake loss was calculated. This was set against Idhult to sort away better respectively worse wind years. The period before covered the year 2012 until 2015 and the period after covered 2016 until 2018.  The result from Microsoft Excel indicates that sector four and sector nine were subjected to the highest percentage of losses. The results from the software windPRO however indicated the highest loss in sector four. Three sectors obtained the same percentage loss as windPRO while remaining values came out dissimilar. The distinction between some of the sectors may be caused by the positioning of some of the Brotorp turbines, where some are located on the borderline between sectors. This implies that some turbines affect two sectors when calculated with Microsoft Excel, which it does not when simulated with windPRO. The sum of all sections indicated that Brotorp turbines caused a wake loss of 3,8 %. This was compared to the simulation in windPRO which resulted in 5,7 %.