@inproceedings{scholars19180,
            year = {2023},
             doi = {10.1109/ICAIA57370.2023.10169796},
            note = {cited By 0; Conference of 2023 International Conference on Artificial Intelligence and Applications, ICAIA 2023 and Alliance Technology Conference, ATCON-1 2023 ; Conference Date: 21 April 2023 Through 22 April 2023; Conference Code:190534},
         journal = {2023 International Conference on Artificial Intelligence and Applications, ICAIA 2023 and Alliance Technology Conference, ATCON-1 2023 - Proceeding},
       publisher = {Institute of Electrical and Electronics Engineers Inc.},
           title = {Risk-Based Reliability Assessment of Modern Power Systems using Machine Learning and Probability Theory},
          author = {Rajanarayan Prusty, B. and Mohan Krishna, S. and Bingi, K. and Gupta, N.},
            isbn = {9781665456272},
             url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85166364371&doi=10.1109\%2fICAIA57370.2023.10169796&partnerID=40&md5=bafd32d6fc448f7ab14dae285de9e311},
        keywords = {Electric load flow; Intelligent systems; Machine learning; Monte Carlo methods; Reliability analysis; Reliability theory, Learning Theory; Machine-learning; Over-limit probability; Photovoltaics generations; Power; Power systems reliability; Reliability assessments; Risk-based; Risks assessments; Severity, Risk assessment},
        abstract = {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. {\^A}{\copyright} 2023 IEEE.}
}