Extreme weather events increasingly challenge the reliability and resilience of modern power systems. This study presents a novel methodology for assessing weather-related disruptions by integrating two large-scale datasets: outage statistics from Swedish energy companies and meteorological hazard data from the Swedish Meteorological and Hydrological Institute (SMHI). A key contribution is the use of large language models to extract and structure relevant information from unstructured SMHI weather reports, creating a structured hazard database. The datasets are merged and analyzed in Power BI, with a focus on wind-related events. The analysis identifies over 123 000 outages linked to wind and wind-induced treefall, offering valuable insights into spatial and temporal patterns of disruption. A multi-day event window is introduced to improve event-outage matching, and the IEEE Major Event Day (MED) methodology is applied to classify extreme disruption days. The results contribute to a deeper understanding of the impact of weather on power systems and lay the groundwork for future research.