The purpose of this master thesis is to implement and validate a Power System Stabilizer (PSS) to a generator at Trängslet hydropower station in Dalarna, Sweden. Problems with local rotor oscillations can occur at the hydropower station when power is transferred on a new power line (130 kV) from Northern to Southern part of Dalarna. To ensure good operation properties on this power line Fortum Generation AB and Fortum Distribution AB wants to activate a PSS on a generator. There have been investigations in which situations and what equipment that contributes to rotor oscillations. When this is known the PSS is tuned to damp these oscillations by choosing a good set of parameters. The most important parameters for good performance are to choose amount of gain and phase compensation. The chosen set of parameters are verified with a test in the power station to ensure that the task is completed. Test results shows that the magnitude of the first swing in rotor oscillation is almost the double than what PSS/E shows, but good correlation five seconds after initialization of the disturbance. The inherent dampening performance of the generator is much better in reality than in simulation. The chosen set of parameters contributes to rotor oscillation dampening in a magnitude approximately equal to simulation results in PSS/E, why the conclusion has been made that the implementation of the PSS is successful. There has also been made simulations with different power outputs and amount of generators synchronized whose results shows that damping is greatly increased with the chosen PSS.

When a devastating event such as a forest fire occurs, multiple actions have to be taken. The first priority is to ensure people's safety during the fire, then the fire has to be kept under control and finally extinguished. After all of this, what remains is a damaged area in the forest. The objective of this thesis is to evaluate medium and high-resolution satellite imagery for the classification of different burn severities in a wildfire damaged forest. The classification can then be used to plan where to focus restoration efforts after the fire to achieve a safe and economically beneficial usage of the affected area. Trängslet fire in Dalarna and Lillhärdal fire in Härjedalen, the two of the 2018 forest fire sites in Sweden were chosen for this study. Satellite imagery over both study areas at medium spatial resolution from Sentinel-2 were acquired pre-fire in early July, 2018 and post-fire on October 2, 2018 while imagery at high spatial resolution from Pleiades were acquired on September 13, 2018. Image processing, analysis and classification were performed using Google Earth Engine (GEE) and PCI Geomatica. To ensure the quality of the classifications, field data were collected during a field trip to the Lillhärdal area using Open Data Kit (ODK). ODK was used since it is an application that can collect/store georeferenced information and images. The result that this thesis found is that while both the medium and high-resolution classifications achieved accurate results, the Sentinel-2 classification is the most suited method in most cases since it is an easy and automated classification using differential Normalized Burn Ratio (dNBR) compared to the Pleiades classification where a lot of manual work has to be put in. There are however cases where the Pleiades classification would be preferable, such as when the affected area usually is obscured by clouds and Sentinel-2 thus finds it hard to achieve good images and when a good spatial resolution is required to more easily display the classification with the original image. The most accurate result according to the data collected at the site in Lillhärdal also showed that the Pleiades classification had a precise match of 61.54% and a plausible match of 92.31%. This can be compared to the Sentinel-2 classification that had a precise match of 48.72% and a plausible match of 94.87%. These percentages are based on the visual analysis of collected images at the Lillhärdal site compared to the classifications. This thesis could have been improved if more information regarding the groundwork that had been done after the fire, but before the acquiring of the satellite imagery, were available. The result would also most likely be better if a satellite with better spatial resolution than Sentinel-2 but still with near infrared and short-wave infrared bands would have been used. The reason being that dNBR, which gave a good result, only needs those two bands.