Enhancing the distribution power system resilience against hurricane events using a bayesian network line outage prediction model

Abstract Enhancing the grid resilience against hurricane events proactively requires a pre‐disaster system optimization built on accurate system network line outage prediction. In the past, the statistical system level failure predictive model such as generalized linear model (GLM), generalized addi...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Okeolu Samuel Omogoye, Komla Agbenyo Folly, Kehinde Oladayo Awodele
Formato:	article
Lenguaje:	EN
Publicado:	Wiley 2021
Materias:	Engineering (General). Civil engineering (General) TA1-2040
Acceso en línea:	https://doaj.org/article/d3ba041a47ef4fdf9631e6ab06e04240
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:d3ba041a47ef4fdf9631e6ab06e04240
record_format	dspace
spelling	oai:doaj.org-article:d3ba041a47ef4fdf9631e6ab06e042402021-11-19T06:50:35ZEnhancing the distribution power system resilience against hurricane events using a bayesian network line outage prediction model2051-330510.1049/tje2.12091https://doaj.org/article/d3ba041a47ef4fdf9631e6ab06e042402021-11-01T00:00:00Zhttps://doi.org/10.1049/tje2.12091https://doaj.org/toc/2051-3305Abstract Enhancing the grid resilience against hurricane events proactively requires a pre‐disaster system optimization built on accurate system network line outage prediction. In the past, the statistical system level failure predictive model such as generalized linear model (GLM), generalized additive model (GAM), system tree‐based mining model (classification regression tree (CART), Bayesian additive regression model (BART), and the topology‐based system components’ fragility curve (FC)‐Monte Carlo simulation (MCS) model have been used to estimate the hurricane‐induced damage on the grid system. Although these models are suitable for a long term infrastructural planning, increased prediction approximation error with high computational time limit their applications as a components level predictive model that can be used for a short‐term proactive operational planning. To solve these problems, a dynamic bayesian network (BN) model is proposed. The investigation is performed on a standard IEEE 15‐bus system using hurricane events data. The proposed BN's system line outage prediction accuracy and efficiency are validated using the statistical system grid components’ FC‐MCS‐scenario reduction (SCENRED) predictive model. Therefore, creating a platform to develop a cost‐effective pre‐disaster system optimization for system network resilience enhancement against the predicted approaching hurricane events.Okeolu Samuel OmogoyeKomla Agbenyo FollyKehinde Oladayo AwodeleWileyarticleEngineering (General). Civil engineering (General)TA1-2040ENThe Journal of Engineering, Vol 2021, Iss 11, Pp 731-744 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Engineering (General). Civil engineering (General) TA1-2040
spellingShingle	Engineering (General). Civil engineering (General) TA1-2040 Okeolu Samuel Omogoye Komla Agbenyo Folly Kehinde Oladayo Awodele Enhancing the distribution power system resilience against hurricane events using a bayesian network line outage prediction model
description	Abstract Enhancing the grid resilience against hurricane events proactively requires a pre‐disaster system optimization built on accurate system network line outage prediction. In the past, the statistical system level failure predictive model such as generalized linear model (GLM), generalized additive model (GAM), system tree‐based mining model (classification regression tree (CART), Bayesian additive regression model (BART), and the topology‐based system components’ fragility curve (FC)‐Monte Carlo simulation (MCS) model have been used to estimate the hurricane‐induced damage on the grid system. Although these models are suitable for a long term infrastructural planning, increased prediction approximation error with high computational time limit their applications as a components level predictive model that can be used for a short‐term proactive operational planning. To solve these problems, a dynamic bayesian network (BN) model is proposed. The investigation is performed on a standard IEEE 15‐bus system using hurricane events data. The proposed BN's system line outage prediction accuracy and efficiency are validated using the statistical system grid components’ FC‐MCS‐scenario reduction (SCENRED) predictive model. Therefore, creating a platform to develop a cost‐effective pre‐disaster system optimization for system network resilience enhancement against the predicted approaching hurricane events.
format	article
author	Okeolu Samuel Omogoye Komla Agbenyo Folly Kehinde Oladayo Awodele
author_facet	Okeolu Samuel Omogoye Komla Agbenyo Folly Kehinde Oladayo Awodele
author_sort	Okeolu Samuel Omogoye
title	Enhancing the distribution power system resilience against hurricane events using a bayesian network line outage prediction model
title_short	Enhancing the distribution power system resilience against hurricane events using a bayesian network line outage prediction model
title_full	Enhancing the distribution power system resilience against hurricane events using a bayesian network line outage prediction model
title_fullStr	Enhancing the distribution power system resilience against hurricane events using a bayesian network line outage prediction model
title_full_unstemmed	Enhancing the distribution power system resilience against hurricane events using a bayesian network line outage prediction model
title_sort	enhancing the distribution power system resilience against hurricane events using a bayesian network line outage prediction model
publisher	Wiley
publishDate	2021
url	https://doaj.org/article/d3ba041a47ef4fdf9631e6ab06e04240
work_keys_str_mv	AT okeolusamuelomogoye enhancingthedistributionpowersystemresilienceagainsthurricaneeventsusingabayesiannetworklineoutagepredictionmodel AT komlaagbenyofolly enhancingthedistributionpowersystemresilienceagainsthurricaneeventsusingabayesiannetworklineoutagepredictionmodel AT kehindeoladayoawodele enhancingthedistributionpowersystemresilienceagainsthurricaneeventsusingabayesiannetworklineoutagepredictionmodel
_version_	1718420343484317696

Enhancing the distribution power system resilience against hurricane events using a bayesian network line outage prediction model

Ejemplares similares