TY  - CONF
A1  - Rajanarayan Prusty, B.
A1  - Mohan Krishna, S.
A1  - Bingi, K.
A1  - Gupta, N.
UR  - https://www.scopus.com/inward/record.uri?eid=2-s2.0-85166364371&doi=10.1109%2fICAIA57370.2023.10169796&partnerID=40&md5=bafd32d6fc448f7ab14dae285de9e311
PB  - Institute of Electrical and Electronics Engineers Inc.
SN  - 9781665456272
Y1  - 2023///
ID  - scholars19180
TI  - Risk-Based Reliability Assessment of Modern Power Systems using Machine Learning and Probability Theory
KW  - Electric load flow; Intelligent systems; Machine learning; Monte Carlo methods; Reliability analysis; Reliability theory
KW  -  Learning Theory; Machine-learning; Over-limit probability; Photovoltaics generations; Power; Power systems reliability; Reliability assessments; Risk-based; Risks assessments; Severity
KW  -  Risk assessment
N2  - 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.
N1  - 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
AV  - none
ER  -