Resilience Assessment of Wind Farms in the Arctic with the Application of Bayesian Networks
Infrastructure systems, such as wind farms, are prone to various human-induced and natural disruptions such as extreme weather conditions. There is growing concern among decision makers about the ability of wind farms to withstand and regain their performance when facing disruptions, in terms of res...
Published in: | Energies |
---|---|
Main Authors: | , |
Format: | Article in Journal/Newspaper |
Language: | English |
Published: |
MDPI
2021
|
Subjects: | |
Online Access: | https://hdl.handle.net/10037/21897 https://doi.org/10.3390/en14154439 |
id |
ftunivtroemsoe:oai:munin.uit.no:10037/21897 |
---|---|
record_format |
openpolar |
spelling |
ftunivtroemsoe:oai:munin.uit.no:10037/21897 2023-05-15T14:43:18+02:00 Resilience Assessment of Wind Farms in the Arctic with the Application of Bayesian Networks Mustafa, Albara Barabadi, Abbas 2021-07-22 https://hdl.handle.net/10037/21897 https://doi.org/10.3390/en14154439 eng eng MDPI Mustafa, A. (2023). Risk and Resilience Assessment of Wind Farms Performance in Cold Climate Regions. (Doctoral thesis). https://hdl.handle.net/10037/28610 . Energies FRIDAID 1922471 doi:10.3390/en14154439 1996-1073 https://hdl.handle.net/10037/21897 openAccess Copyright 2021 The Author(s) VDP::Technology: 500::Electrotechnical disciplines: 540 VDP::Teknologi: 500::Elektrotekniske fag: 540 Journal article Tidsskriftartikkel Peer reviewed publishedVersion 2021 ftunivtroemsoe https://doi.org/10.3390/en14154439 2023-03-02T00:04:23Z Infrastructure systems, such as wind farms, are prone to various human-induced and natural disruptions such as extreme weather conditions. There is growing concern among decision makers about the ability of wind farms to withstand and regain their performance when facing disruptions, in terms of resilience-enhanced strategies. This paper proposes a probabilistic model to calculate the resilience of wind farms facing disruptive weather conditions. In this study, the resilience of wind farms is considered to be a function of their reliability, maintainability, supportability, and organizational resilience. The relationships between these resilience variables can be structured using Bayesian network models. The use of Bayesian networks allows for analyzing different resilience scenarios. Moreover, Bayesian networks can be used to quantify resilience, which is demonstrated in this paper with a case study of a wind farm in Arctic Norway. The results of the case study show that the wind farm is highly resilient under normal operating conditions, and slightly degraded under Arctic operating conditions. Moreover, the case study introduced the calculation of wind farm resilience under Arctic black swan conditions. A black swan scenario is an unknowable unknown scenario that can affect a system with low probability and very high extreme consequences. The results of the analysis show that the resilience of the wind farm is significantly degraded when operating under Arctic black swan conditions. In addition, a backward propagation of the Bayesian network illustrates the percentage of improvement required in each resilience factor in order to attain a certain level of resilience of the wind farm under Arctic black swan conditions. Article in Journal/Newspaper Arctic University of Tromsø: Munin Open Research Archive Arctic Norway Energies 14 15 4439 |
institution |
Open Polar |
collection |
University of Tromsø: Munin Open Research Archive |
op_collection_id |
ftunivtroemsoe |
language |
English |
topic |
VDP::Technology: 500::Electrotechnical disciplines: 540 VDP::Teknologi: 500::Elektrotekniske fag: 540 |
spellingShingle |
VDP::Technology: 500::Electrotechnical disciplines: 540 VDP::Teknologi: 500::Elektrotekniske fag: 540 Mustafa, Albara Barabadi, Abbas Resilience Assessment of Wind Farms in the Arctic with the Application of Bayesian Networks |
topic_facet |
VDP::Technology: 500::Electrotechnical disciplines: 540 VDP::Teknologi: 500::Elektrotekniske fag: 540 |
description |
Infrastructure systems, such as wind farms, are prone to various human-induced and natural disruptions such as extreme weather conditions. There is growing concern among decision makers about the ability of wind farms to withstand and regain their performance when facing disruptions, in terms of resilience-enhanced strategies. This paper proposes a probabilistic model to calculate the resilience of wind farms facing disruptive weather conditions. In this study, the resilience of wind farms is considered to be a function of their reliability, maintainability, supportability, and organizational resilience. The relationships between these resilience variables can be structured using Bayesian network models. The use of Bayesian networks allows for analyzing different resilience scenarios. Moreover, Bayesian networks can be used to quantify resilience, which is demonstrated in this paper with a case study of a wind farm in Arctic Norway. The results of the case study show that the wind farm is highly resilient under normal operating conditions, and slightly degraded under Arctic operating conditions. Moreover, the case study introduced the calculation of wind farm resilience under Arctic black swan conditions. A black swan scenario is an unknowable unknown scenario that can affect a system with low probability and very high extreme consequences. The results of the analysis show that the resilience of the wind farm is significantly degraded when operating under Arctic black swan conditions. In addition, a backward propagation of the Bayesian network illustrates the percentage of improvement required in each resilience factor in order to attain a certain level of resilience of the wind farm under Arctic black swan conditions. |
format |
Article in Journal/Newspaper |
author |
Mustafa, Albara Barabadi, Abbas |
author_facet |
Mustafa, Albara Barabadi, Abbas |
author_sort |
Mustafa, Albara |
title |
Resilience Assessment of Wind Farms in the Arctic with the Application of Bayesian Networks |
title_short |
Resilience Assessment of Wind Farms in the Arctic with the Application of Bayesian Networks |
title_full |
Resilience Assessment of Wind Farms in the Arctic with the Application of Bayesian Networks |
title_fullStr |
Resilience Assessment of Wind Farms in the Arctic with the Application of Bayesian Networks |
title_full_unstemmed |
Resilience Assessment of Wind Farms in the Arctic with the Application of Bayesian Networks |
title_sort |
resilience assessment of wind farms in the arctic with the application of bayesian networks |
publisher |
MDPI |
publishDate |
2021 |
url |
https://hdl.handle.net/10037/21897 https://doi.org/10.3390/en14154439 |
geographic |
Arctic Norway |
geographic_facet |
Arctic Norway |
genre |
Arctic |
genre_facet |
Arctic |
op_relation |
Mustafa, A. (2023). Risk and Resilience Assessment of Wind Farms Performance in Cold Climate Regions. (Doctoral thesis). https://hdl.handle.net/10037/28610 . Energies FRIDAID 1922471 doi:10.3390/en14154439 1996-1073 https://hdl.handle.net/10037/21897 |
op_rights |
openAccess Copyright 2021 The Author(s) |
op_doi |
https://doi.org/10.3390/en14154439 |
container_title |
Energies |
container_volume |
14 |
container_issue |
15 |
container_start_page |
4439 |
_version_ |
1766314986111500288 |