## 1 Introduction
Using the QRA approach, [PERSON] et al. [16] proposed a model using hierarchical Bayesian analysis (HBA) to quantify the risks associated with submarine pipelines. The methodology used accounts for uncertainty in the data and model, and an industrial case study is used to demonstrate the use of this approach. [PERSON] et al. [15] adopted a qualitative risk assessment approach and conducted expert interviews across Europe and the US to identify significant risks to power grids. Based on the results, it is concluded that false data injection, denial of services, and malware attacks are significant threats. [PERSON] et al. [17] proposed a risk assessment model combining the data-driven analytical hierarchy process (D-AHP) and grey theory for smart grids. To obtain comprehensive assessment values and risk grades, 5 first-level and 13 second-level indices were combined using an empirical dataset for the Suzhou Smart Grid Demonstration Project in China. To evaluate the impact of cyber systems on power grid dispatching systems, [PERSON] et al. [18] proposed a risk assessment model and calculated the power grid risk probabilities and consequences through risk index levels. To highlight the IT vulnerabilities and cyber threats to power grids, [PERSON] et al. [19] proposed a hybrid risk assessment model using fuzzy theory. This study provides meaningful insights into ensuring power grid stability by combining linguistic and numerical analyses. [PERSON] et al. [PERSON] et al. (2020) proposed a novel approach for accessing cascading failure sequences in AC/DC hybrid power grids using a game model and quantitative indices. Although the model was validated on China's provincial AC/DC hybrid power grid, its scalability and practical implications must be considered. By applying graph theory to identify the communities at risk of power loss due to wildfires, [PERSON] et al. [PERSON] et al. (2019) proposed a novel approach. Fire probability data and betweenness centrality and minimum cut analyses highlight vulnerable areas. Potential limitations to the method include the difficulty of practical implementation and the need for further validation with larger, more complex grids. [PERSON] et al. [PERSON] et al. (2019) proposed an innovative technique for assessing and mitigating risks in large-scale power grids, specifically transmission line trips caused by natural hazards. The proposed risk analysis method combines FTA and BN modeling to assess and predict tripping accidents caused by wildfires, lightning, strong winds, and ice storms. However, real-world interactions and uncertainties may be distorted by modeling simplifications. In recent years, several methodologies have been proposed to assess risks in power grid systems, such as intelligent inspection models based on edge computing (e.g., RepYOLO for power line inspection) and data collection protocols for smart grids [PERSON] et al. (2019). These approaches primarily focus on improving operational efficiency and security in specific aspects of grid management, such as data integrity and defect detection. Additionally, studies like the one focusing on cascading failures through risk-graph analysis examine network vulnerabilities but fail to address the probabilistic nature of uncertainty and expert elicitation, which is critical for effective risk management [PERSON] et al. (2020). Our work proposes a novel fuzzy Bayesian network approach that integrates expert opinions and weather-related risks, providing a more comprehensive model for assessing power grid vulnerabilities, including the impact of cascading failures and external disturbances. After conducting a detailed literature review, we confidently state that while previous research has addressed significant threats to power grid systems, most studies focus primarily on a limited range of risks. In particular, cyber threats and transmission line failures are often the primary areas of concern, leaving many other potential risks underexplored.