eprintid: 19180
rev_number: 2
eprint_status: archive
userid: 1
dir: disk0/00/01/91/80
datestamp: 2024-06-04 14:11:37
lastmod: 2024-06-04 14:11:37
status_changed: 2024-06-04 14:05:05
type: conference_item
metadata_visibility: show
creators_name: Rajanarayan Prusty, B.
creators_name: Mohan Krishna, S.
creators_name: Bingi, K.
creators_name: Gupta, N.
title: Risk-Based Reliability Assessment of Modern Power Systems using Machine Learning and Probability Theory
ispublished: pub
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
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
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. © 2023 IEEE.
date: 2023
publisher: Institute of Electrical and Electronics Engineers Inc.
official_url: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85166364371&doi=10.1109%2fICAIA57370.2023.10169796&partnerID=40&md5=bafd32d6fc448f7ab14dae285de9e311
id_number: 10.1109/ICAIA57370.2023.10169796
full_text_status: none
publication: 2023 International Conference on Artificial Intelligence and Applications, ICAIA 2023 and Alliance Technology Conference, ATCON-1 2023 - Proceeding
refereed: TRUE
isbn: 9781665456272
citation:   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.