Data Science in Power System Risk Assessment and Management

Risk management in power systems is crucial for ensuring the stability and reliability of electricity supply. Traditional methods have often been inadequate in addressing the complexity and dynamics of modern power networks. This paper explores the role of data science in enhancing risk assessment and management in power systems. Leveraging data-driven techniques, machine learning, and predictive analytics, this study demonstrates how advanced algorithms can improve risk prediction, fault detection, and decision-making processes. We also discuss challenges and potential solutions for integrating these technologies into existing infrastructures. Our findings suggest that data science offers significant potential in mitigating risks, improving operational efficiency, and enhancing grid resilience in the face of unforeseen events and natural disasters.