| Abstract [eng] |
The expansion of renewable energy sources is becoming increasingly important in response to the challenges of climate change, aiming to reduce dependence on fossil fuels and enhance energy security. In this context, wind energy in Lithuania is experiencing rapid growth—driven by favorable natural conditions, technological advancement, and supportive policies. However, as installed capacity increases and new wind parks are developed, growing attention is being paid not only to energy production volumes but also to efficiency and operational challenges. One such challenge that can affect turbine performance and power generation outcomes is blade icing. This phenomenon can reduce energy production efficiency, disrupt turbine operation, increase wear, and raise operational costs. Ice accumulation on blades deteriorates their aerodynamic properties, which not only decreases the amount of power generated but can also trigger shutdowns to protect the equipment from damage. Despite the wide range of theoretical, laboratory, and simulation-based studies conducted worldwide on the effects of blade icing, there is still a lack of practical research based on real-world operational data. This shortage of data is observed not only in temperate climate regions but also globally, making it difficult to accurately assess the extent of icing impacts and their variability across different geographical and meteorological contexts. The lack of field research limits the development of reliable loss assessment models and effective prevention strategies. The aim of this study is to fill the existing knowledge gap by quantitatively assessing the impact of blade icing on wind turbine power output in Lithuania and identifying the potential economic risks associated with it. The study includes a theoretical literature review that covers recent scientific sources on the mechanisms of blade icing formation, the meteorological conditions that contribute to it, and the methods used to evaluate its impact. In the empirical part, the T19IceLossMethod algorithm was applied to assess power deviations by comparing theoretical power curves with actual SCADA operational data during low-temperature periods. The analysis was based on 2024 data from 30 wind turbines operating in Lithuania, aiming to determine the extent of power losses caused by both efficiency reduction and turbine shutdowns. Additionally, the influence of tower height, turbine location, and meteorological parameters such as air temperature and precipitation levels was assessed. The results revealed certain trends and regional differences relevant to both operators and investors. This research provides a deeper understanding of the significance of blade icing under real Lithuanian conditions and can serve as a valuable foundation for decisions related to turbine operation strategies, efficiency improvements, implementation of preventive measures, and investment planning, taking into account climate-related risks. |
