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.

Wind turbines are becoming a more common sight and a more important part in the power grid. The benefits are mainly that wind energy is a renewable energy source and a single wind turbine can produce enough electricity to cover several households’ annual electricity need and not producing carbon dioxide as a rest product. Drawbacks are fluctuation in wind speed, which makes it difficult to regulate. The turbines need to be placed far from cities which cause losses in transmission in the national power grid.  In cold areas with long winters there is a risk of high energy losses due to iced blades. If there is ice accretion on the wind turbine blades it can cause a production loss and in extension economical losses by not selling the electricity. Finding those events is of high interest and there are methods to prevent and remove ice. However, there are occasions when there is ice on the blades, but no sensors signal this, and the production loss is a fact. There is a presumed production loss of 5-25 % annually due to icing on wind turbines in Sweden, depending on where the site is located. There is no general method for detecting ice in the industry but there are several methods available developed by different parties.  In this master’s thesis, a software has been developed in cooperation with Siemens Gamesa Renewable Energy to identify production losses on wind turbines due to icing using historical SCADA data. The software filters the raw data to construct a reference curve, to which data during cold weather is compared. It was found that low temperature causes ice losses, and the risk of an ice loss increases as temperature decreases. The annual losses at investigated wind farms were 4-10 % of the expected annual production. / Vindkraftverk blir en allt vanligare syn och en viktigare del i kraftnätet. Fördelarna är framförallt att det är en förnybar energykälla, det blir inga koldioxidutsläpp när vindkraftverken har installerats och ett vindkraftverk kan täcka flera hushålls årliga elbehov. Nackdelar är att vinden inte går att kontrollera och elproduktionen inte är garanterad eller konstant. Vindkraftverk placeras långt ifrån tätorter, vilket leder till förluster under distribution.  I kalla regioner med långa vintrar uppstår en risk för energiförluster på grund av nedisade turbinblad. Om det finns ispåbyggnad på turbinbladen kan det orsaka produktionsförluster och följaktligen en ekonomisk förlust. Det är av stort intresse i att upptäcka dessa och det finns flera metoder för att förbygga is och även avisning. Det antas vara produktionsförluster på 5-25 % årligen på grund av is i Sverige, beroende på vindparkens placering. Det finns ingen generell metod för att upptäcka is inom industrin, men det finns flera metoder utvecklade av olika parter.  I det här examensarbetet har en mjukvara utvecklats i samarbete med Siemens Gamesa Renewable Energy för att upptäcka produktionsförluster hos vindkraftverk orsakade av nedisade turbinblad genom att använda SCADA-data. Mjukvaran filtrerar rådata för att beräkna en referenskurva, mot vilken data för kallt väder kan jämföras. Den visar att det finns korrelation mellan låg temperatur och produktionsförluster samt att risken för produktionsförlust ökar då temperaturen sjunker. De årliga produktionsförlusterna hos de undersökta vindparkerna var 4-10 % av den förväntade årliga produktionen.

Optimizing the operation and maintenance of wind turbines is crucial as the wind energy sector continues to expand. Predicting the mass imbalance of wind turbines, which can seriously damage the rotor blades, gearbox, and other components, is one of the key issues in this field. In this work, we propose a machine learning-based method for predicting the mass imbalance of wind turbines utilizing information from multiple sensors and monitoring systems. We collected data and trained the model from Adwen AD8 wind turbine model and evaluated on the real wind turbine SCADA data which is located at Fraunhofer IWES, Bremerhaven. The data included various parameters such as wind speed, blade root bending moments and rotor speed. We used this data to train and test machine learning classification models based on different algorithms, including extra-tree classifiers, support vector machines, and random forest. Our results showed that the machine learning models were able to predict the mass imbalance percentage of wind turbines with high accuracy. Particularly, the extra tree classifiers with blade root bending moments outperformed other research for multiclassification problem with an F1 score of 0.91 and an accuracy of 90%. Additionally, we examined the significance of various features in predicting the mass imbalance and observed that the rotor speed and blade root bending moments were the most crucial variables. Our research has significant effects for the wind energy sector since it offers a reliable and efficient way for predicting wind turbine mass imbalance. Wind farm operators can save maintenance costs, minimize downtime of wind turbines, and increase the lifespan of turbine components by identifying and eliminating mass imbalances. Also, further investigation will allow us to apply our method to different kinds of wind turbines, and it is simple to incorporate into current monitoring systems as it supports prediction without installing additional sensors. In conclusion, our study demonstrates the potential of machine learning for predicting the percentage of mass imbalance of wind turbines. We believe that our approach can significantly benefit the wind energy industry and contribute to the development of sustainable energy sources.