## 1 Introduction
To highlight the practical significance and relevance of our proposed risk assessment model, we compare it with existing methods from the recent literature. These methods, which include power line intelligent inspection, secure data collection in smart grids, and cascading failure vulnerability analysis, focus on different aspects of infrastructure risk. Although these methods provide valuable insights, they focus on specific types of risks or heavily rely on historical data and traditional engineering models. In contrast, our proposed model offers a more comprehensive approach by integrating FBNs and expert elicitation. Our model identifies a broader range of risk factors, including equipment failure, human error, and weather-related risks such as extreme temperatures, flooding, and lightning. Including these weather-related factors, which are increasingly critical for infrastructure resilience, provides a more holistic view of risk. Through this comparison, we demonstrate that our methodology offers enhanced adaptability, precision, and reliability in assessing the risks to power grid systems, proving its practical significance and superiority over existing approaches. Additionally, the lack of historical data and the vagueness of existing datasets can lead to under- or overestimating risk assessments [PERSON] et al. (2020). Furthermore, due to the unavailability of incident data for certain risk factors, such as floods or earthquakes, existing models may struggle to generate results when new threats emerge. To bridge these gaps, this study combines fuzzy set theory (FST) through expert elicitation and the BN, commonly known as the fuzzy Bayesian network (FBN). The BN incorporates fuzzy logic because it aligns with the intricacies and uncertainties inherent to power grid systems, giving it distinct advantages [PERSON] et al. (2020). Expert elicitation will provide a more comprehensive risk assessment, accounting for external and internal factors [27]. Using the BN, ongoing risk assessment will remain relevant, and the model will be updated as new evidence is acquired. Integrating an FBN in this study enables a comprehensive risk assessment of power grids by combining quantitative data and expert opinions. Due to changing environmental conditions, evolving cyber threats, and the dynamic nature of power grids, the FBN is exceptionally well suited for handling imprecise, incomplete, or uncertain information, which is common in power grid risk assessments [28]. Another novelty of this study is that it compares root causes, also known as basic events (BEs), identified through expert elicitation with those identified through historical data only. Expert opinions gathered through surveys enhance the efficacy of this risk assessment model. These insights provide valuable complements to the quantitative data, making them essential for a comprehensive understanding of power grid risks. Applying this methodology enhances the comprehensiveness of our risk assessment and ensures that our findings benefit the stakeholders involved in ensuring power grid stability and security. The remainder of this paper is organized as follows: Section 2 provides an overview of the fuzzy Bayesian network (FBN), introducing Bayesian networks, fuzzy set theory, and the role of expert elicitation in risk assessment. Section 3 outlines the methodology for applying the FBN to power grid risk assessment, detailing the identification of risk factors, data collection, and the construction of the FBN. Section 4 presents the model implementation using both crisp and fuzzy probabilities to assess power grid risks. Section 5 discusses the results, including a comparison of models and a sensitivity analysis. Finally, Section 6 concludes the paper, summarizing the findings and suggesting directions for future research.