relation: https://khub.utp.edu.my/scholars/19180/
title: Risk-Based Reliability Assessment of Modern Power Systems using Machine Learning and Probability Theory
creator: Rajanarayan Prusty, B.
creator: Mohan Krishna, S.
creator: Bingi, K.
creator: Gupta, N.
description: 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.
publisher: Institute of Electrical and Electronics Engineers Inc.
date: 2023
type: Conference or Workshop Item
type: PeerReviewed
identifier:   Rajanarayan Prusty, B. and Mohan Krishna, S. and Bingi, K. and Gupta, N.  (2023) Risk-Based Reliability Assessment of Modern Power Systems using Machine Learning and Probability Theory.  In: UNSPECIFIED.     
relation: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85166364371&doi=10.1109%2fICAIA57370.2023.10169796&partnerID=40&md5=bafd32d6fc448f7ab14dae285de9e311
relation: 10.1109/ICAIA57370.2023.10169796
identifier: 10.1109/ICAIA57370.2023.10169796