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Harsh winter climate causes various problems for both the public andprivate sector in Sweden, especially in the northern part and the railway industryis no exception. This master thesis project covers an investigation of the eects ofthe winter climate and a phenomena called atmospheric icing on the performance ofthe train in a region called the Botnia-Atlantica region. The investigation was donewith data over a short period January-February 2017 with simulated weather datafrom the Weather research and forecast model that was compared with the periodOctober - December 2016. The investigation only included high speed trains.The trains have been analysed based on two dierent performance measurements.The cumulative delay which is the increment in delay over a section and the currentdelay which is the current delay compared to the schedule. Cumulative delaysare investigated with survival analysis and the current delay is investigated with aMulti-state Markov model.The results show that the weather could have an eect on the trains performancewhere the survival analysis detected connection between the weather and cumulativedelays. The Markov model also showed a connection between the weather anddelayed trains including that the presence of atmospheric icing had a negative eecton remaining in a state of non-delay.

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Wind power will play a major role in the future energy system in Sweden. Most of the major wind parks are planned to be built in sites where the cold climate and atmospheric icing can cause serious problems. This underlines the importance of addressing these issues. The major cause of these problems is in-cloud icing of the rotor blades due to super cooled liquid droplets of clouds. The droplets freeze upon impact with the rotor blade and form hard rime ice. This rime ice causes disruption in the aerodynamics that leads to production losses, extra loads on the rotor blades and when the ice is shed it poses a safety risk to people in the near environment. This master thesis focuses on how to measure the accretion of ice and the correlation between measured ice and production losses of two wind parks in northern Sweden.   The results show a good correlation between the ice accretion on a stationary sensor and the production loss from a wind turbine. In most icing events the icing of the sensor and large production losses from the wind turbine correlated clearly. Attempts to quantify the production losses at a certain ice rate measured with the stationary sensors was done, however no clear results was produced. The reason for this is that the wind turbines often stop completely during an icing event and that the time series analyzed was too short to be able to quantify the losses at certain wind speed and ice rates.   Recommendations on the type of sensor which should be used was to be produced, however the conclusion was that no single sensor has acted satisfactory and could be recommended to measure ice accretion for wind turbine applications. Due to this, at least two sensors are recommended to increase the redundancy in the measurement system. Modeling ice accretion with standard parameters measured has been done and the results show that the time of icing could be determined quite well when the sensors was ice free, however when the sensors and especially the humidity sensors was iced the time of icing was overestimated.   The main conclusion drawn is that there is a clear relationship between the icing of a stationary sensor and the rotor blade. There is still no which fulfills all demands of measuring ice accretion for wind turbine applications, further it is possible with simple models to roughly determine when icing occurs with standard measurements.

When ice is accumulated on a wind turbine's rotor blade its aerodynamics are altered, leading to reduced efficiency and sometimes altered pressure oscillations around the blade. These pressure oscillations can be detected as sound. With sound level measurements over a long time, combined with known ice conditions in the same period, the measured sound data can be used to classify the ice conditions. This master's thesis aims to investigate the possibilities of using sound level measurements at 36 frequency bands in the range 6.3–20 000 Hz along with machine learning and wind speed to detect icing on wind turbine blades. Four k-NN models have been trained and evaluated using two different data configurations that each treat two different means of normalization: one uses the raw sound level data in dBA which has been standardized using z-score. The other uses the wind power density Iwind = 0.5ρU3 instead of the reference sound intensity I0 = 10-12 W/m2 in the decibel formula L = 10log10(I/I0) to reduce the influence of wind speed on the data. The sound/wind speed hybrid data was also z-score standardized. Available data was from February 21st to March 3rd in 2023 and March 1st to April 3rd in 2024. In the summer of 2023, the leading edges of the rotor blades on the investigated wind turbine were renovated which might have altered the sound. Therefore, what is denoted as Data configuration A used 2024 data as training data while 2023 data was used solely for testing. Data configuration B on the other hand used data from March 1st to March 17th 2024 for training and data from April 1st to April 3rd 2024 for testing as the rotor blades were identical between those data sets. Wind conditions were also more similar between training and testing data for Data configuration B. The models were optimized using grid search, varying k, distance metrics and feature combinations of the 36 frequency bands, while maximizing the balanced accuracy, BA, of the model using 5-fold cross-validation. For Data configuration A, this resulted in a balanced accuracy in the testing stage at BAtesting = 0.535 using the dBA sound level data, and BAtesting = 0.601 using the data normalized with wind power density. For Data configuration B, balanced accuracy was BAtesting = 0.845 using the dBA sound level data, and BAtesting = 0.773 using the data normalized with wind power density. The main conclusion is that icing can be detected using sound level measurements, wind speed and machine learning although the models in this project generalize poorly partly due to limited data and partly due to how the models were constructed. The models perform better with wind speeds similar to the training data. / När is ackumuleras på vindturbinblad ändras aerodynamiken vilket leder till lägre verkningsgrad och ibland förändrade tryckoscillationer kring bladet. Dessa tryckoscillationer kan detekteras i form av ljud. Med hjälp av ljudmätningar över en längre tid, kombinerat med kända isförhållanden under tidsperioden, kan ljuddatan användas för att klassificera isförhållandena. Målet med detta examensarbete är att undersöka möjligheterna att använda ljudnivåmätningar vid 36 frekvensspann mellan 6,3–20 000 Hz tillsammans med maskininlärning och vindhastighet för att detektera isbildning på vindkraftverk. Fyra modeller baserade på algoritmen k-NN har tränats och utvärderats med två olika datakonfigurationer som vardera behandlar två olika metoder för normalisering: en använder obehandlad ljudnivådata i enheten dBA som har standardiserats med z-poäng. Den andra använder vindenergidensiteten Iwind = 0.5ρU3 istället för referensintensiteten I0 = 10-12 W/m2 i formeln för decibel L = 10log10(I/I0) för att begränsa vindhastighetens inverkan på datan. Ljud-/vindhybriddatan standardiserades också med z-poäng. Den tillgängliga datan var mellan 21 februari och 3 mars 2023 samt 1 mars till 3 april 2024. Sommaren 2023 renoverades bladen på det undersökta vindkraftverket vilket kan ha påverkat ljudet. Därför användes data från 2024 som träningsdata och data från 2023 som testdata i vad som benämns som Data configuration A. Data configuration B använde istället data från 1-17 mars 2024 för träning och data från 1-3 april 2024 för testning eftersom rotorbladen var identiska mellan de datamängderna. Vindförhållandena var också mer lika inom Data configuration B. Modellerna optimerades med grid search genom att variera k, avståndsmått, och vilken kombination av de 36 frekvensspannen som ingår i modellen. Balanserad träffsäkerhet, BA, är resultatet som maximerades genom 5-delad korsvalidering. För Data configuration A resulterade detta under teststadiet i BAtesting = 0,535 med omodifierad ljuddata och BAtesting = 0,601 då vindenergidensiteten användes som ljudets referensnivå. För Data configuration B var den balanserade träffsäkerheten BAtesting = 0,845 med omodifierad ljuddata och BAtesting = 0,773 då vindenergidensiteten användes som ljudets referensnivå. Den främsta slutsatsen är att isbildning kan detekteras med ljudnivåmätningar, vindhastighet och maskininlärning men modellerna som har tagits fram i detta projekt presterar relativt dåligt, delvis på grund av en begränsad datamängd och delvis på grund av hur modellerna har konstruerats. Modellerna presterade bättre för testdata med liknande vindförhållanden.

This thesis deals with static and dynamic analysis of an lookout tower construction. For dynamic analysis a computational model in ANSYS software is created. Static analysis is performed by Scia Engineer software. Both of this software use finite element method. It is also focused on wind load determination by Eurocode 1, structural factor calculation, modal analysis and vortex shedding. Verification of selected parts according to Eurocodes is included.