%J 2023 International Conference on Artificial Intelligence and Applications, ICAIA 2023 and Alliance Technology Conference, ATCON-1 2023 - Proceeding
%L scholars19180
%O 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
%R 10.1109/ICAIA57370.2023.10169796
%D 2023
%X 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.
%K 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
%A B. Rajanarayan Prusty
%A S. Mohan Krishna
%A K. Bingi
%A N. Gupta
%I Institute of Electrical and Electronics Engineers Inc.
%T Risk-Based Reliability Assessment of Modern Power Systems using Machine Learning and Probability Theory