%0 Conference Paper
%A Rajanarayan Prusty, B.
%A Mohan Krishna, S.
%A Bingi, K.
%A Gupta, N.
%D 2023
%F scholars:19180
%I Institute of Electrical and Electronics Engineers Inc.
%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
%R 10.1109/ICAIA57370.2023.10169796
%T Risk-Based Reliability Assessment of Modern Power Systems using Machine Learning and Probability Theory
%U https://khub.utp.edu.my/scholars/19180/
%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.
%Z 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