<> "The repository administrator has not yet configured an RDF license."^^ . <> . . . "Risk-Based Reliability Assessment of Modern Power Systems using Machine Learning and Probability Theory"^^ . "Risk-based reliability assessment is prevalent for modern power systems under higher penetration of renewable generations. This paper highlights the importance of machine learning and probabilistic approaches for risk-based reliability assessment during power system operation and planning. A set of metrics for realistic risk-based reliability assessment considering over-limit probabilities and corresponding severities is suggested. Probabilistic load flow using Monte-Carlo simulation is used to estimate the over-limit probabilities of power system variables. A detailed presentation of steps for the generation of random samples of a set of correlated random variables, development of realistic risk metrics, and portrayal of their significances via critical result analyses for different cases is expected to serve as a reference text for novice researchers in the field of risk-based reliability assessment of modern power systems integrated with photovoltaic generations. © 2023 IEEE."^^ . "2023" . . . "Institute of Electrical and Electronics Engineers Inc."^^ . . "Institute of Electrical and Electronics Engineers Inc."^^ . . . "2023 International Conference on Artificial Intelligence and Applications, ICAIA 2023 and Alliance Technology Conference, ATCON-1 2023 - Proceeding"^^ . . . . . . . . . . . . . . . . . "B."^^ . "Rajanarayan Prusty"^^ . "B. Rajanarayan Prusty"^^ . . "K."^^ . "Bingi"^^ . "K. Bingi"^^ . . "S."^^ . "Mohan Krishna"^^ . "S. Mohan Krishna"^^ . . "N."^^ . "Gupta"^^ . "N. Gupta"^^ . . . . . "HTML Summary of #19180 \n\nRisk-Based Reliability Assessment of Modern Power Systems using Machine Learning and Probability Theory\n\n" . "text/html" . .