A structured framework for prioritizing atmospheric icing mitigation techniques in critical infrastructures

Atmospheric icing poses a significant threat to power infrastructure in cold and high-latitude regions. This risk is especially critical in Arctic areas, where data availability is limited. Ice accretion can lead to mechanical failures, service interruptions, and safety hazards. As climate change increases the frequency and severity of such events, the need for reliable and adaptable mitigation strategies has become more urgent. Although various Atmospheric Anti-/De-Icing (AADI) techniques exist, each with specific strengths and limitations, their practical application is often limited by the absence of a structured method for prioritization. In harsh environments, effective decision-making requires evaluating both internal and external risk factors, including usability, cost, readiness level, and environmental exposure. To address this need, a structured probabilistic framework was developed for prioritizing AADI techniques during Atmospheric Ice Accretion Emergency Events (AIAEEs). The framework integrates expert judgment with three key performance indicators: Technology Readiness Level (TRL), Technology Usability (TU), and Technology Cost (TC). Monte Carlo Simulations (MCS), the Fuzzy Delphi Analytical Process (FDAP), and K-means clustering were used to manage uncertainty and reflect environmental variability through distribution fitting and scenario analysis. The framework was applied to the Kvandal–Balsfjord 420 kV transmission line in Arctic Norway. Based on climatic divisions and expert input, 55 techniques were evaluated. Techniques such as superhydrophobic coatings, flexible materials, and mechanical covers were consistently prioritized. This framework supports transparent, data-driven decision-making and can be enhanced using real-time monitoring and advanced predictive models.