## 1 Introduction  
Electricity has a major role and significant impact on modern life activities. Various economic sectors worldwide depend heavily on the electric power industry, and generated energy is used to meet industry and domestic needs [1]. Due to large-scale urbanization and settlement, electricity demand has increased, and maintaining power is crucial for everyday life and economic vitality [2]. Critical power infrastructure, also known as the power grid, is required to generate and distribute electricity based on four fundamental functions, i.e., electricity generation, transmission, distribution, and finally, consumption by end users [3]. These critical infrastructures are susceptible to various natural and man-made risks that threaten their integrity [4]. Natural hazards include weather-related factors, such as rainfall, high winds, and extreme temperatures, and environmental factors, such as earthquakes, storms, fires, lightning, tornadoes, and vegetation [5, 6]. There are numerous man-made risk factors, such as operational parameters, which include maintenance issues, disconnects, current flow, voltage level, and power factors [2]. Equipment conditions are also critical for assessing the associated risks, and commonly known equipment failures include malfunctioning capacitors, transformers, circuit breakers, generators, reactors, and transmission lines. Some specialized risk factors also threaten the reliability of power grids, including cyberattacks, unauthorized access, and the failure of the supervisory control and data acquisition (SCADA) system [7]. These potential risks are severe concerns for operating agencies and all stakeholders, potentially leading to prolonged blackouts that could have a significant impact on the country's economy. One of the motivations for conducting this study is the recent blackout in Europe, particularly in Spain and Portugal, which underscores the importance and timeliness of power grid risk assessment and resilience. This event, which affected millions of people across the region, emphasizes the practical relevance of the proposed model, underscoring the need for enhanced infrastructure resilience in the face of such widespread failures [8; 9]. Similarly to these, the blackout serves as a reminder of the vulnerabilities in power grids. Furthermore, it underscores the need for advanced risk assessment approaches, such as the fuzzy Bayesian network model proposed in this study, to more accurately predict and mitigate the effects of future failures. There are numerous techniques to quantify these risk factors and suggest mitigation measures. One of these promising methods is the quantitative risk assessment approach (QRA), which is commonly used when adequate historical data for past failure incidents are available [10]. The most commonly known QRA methods are fault tree analysis (FTA), event tree analysis (ETA), Monte Carlo simulation (MCS), and Bayesian networks (BNs) [11]. In newly developed or less-documented infrastructures, historical data may be unavailable or unreliable due to certain inherent factors, such as incomplete maintenance records [12]. Qualitative risk assessment techniques are often used in power grid risk assessment to evaluate potential risks and vulnerabilities objectively. These methods may not provide precise numerical values but offer valuable insights into risk characteristics and help prioritize future analyses [13]. A few commonly known qualitative risk assessment techniques are failure modes and effects analysis (FMEA), risk matrices, bowtie analysis, the Delphi method, and expert elicitation [14]. These techniques convert qualitative data into quantitative numbers, and risk is assessed for the facility under consideration [15]. Researchers have conducted numerous studies to assess the risks posed to power grids by suggesting mitigation strategies for the stability of power flow to end users. In the next paragraph, a few important studies are discussed, followed by the research gap and focus of this research.