Optimal group maintenance decision for a wind farm based on condition‐based maintenance

Abstract This study investigated the group maintenance (GM) strategy based on condition‐based maintenance (CBM) of a parallel wind farm with multiple identical wind turbines. In terms of this strategy, the state of deterioration of the system is determined by periodic inspection, with the GM activit...

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Autores principales:	Jinhe Wang, Xiaohong Zhang, Jianchao Zeng
Formato:	article
Lenguaje:	EN
Publicado:	Wiley 2021
Materias:	condition‐based maintenance group maintenance parallel system stationary probability density function wind farm Renewable energy sources TJ807-830
Acceso en línea:	https://doaj.org/article/68b73fd747774a46810a747399b75150
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spelling	oai:doaj.org-article:68b73fd747774a46810a747399b751502021-11-26T14:02:23ZOptimal group maintenance decision for a wind farm based on condition‐based maintenance1099-18241095-424410.1002/we.2644https://doaj.org/article/68b73fd747774a46810a747399b751502021-12-01T00:00:00Zhttps://doi.org/10.1002/we.2644https://doaj.org/toc/1095-4244https://doaj.org/toc/1099-1824Abstract This study investigated the group maintenance (GM) strategy based on condition‐based maintenance (CBM) of a parallel wind farm with multiple identical wind turbines. In terms of this strategy, the state of deterioration of the system is determined by periodic inspection, with the GM activities at each inspection point performed according to the number of failed turbines, which are determined by the corresponding state of deterioration of the system. Under this strategy, the probabilities that influence GM are derived by the stationary probability density function, which is based on the stationary law of the state of the wind farm system. Furthermore, employing numerical experiments and a case study, we verified the correctness and effectiveness of the stationary probability density function under different deterioration‐related parameters and obtained the optimal number of failed turbines when GM is performed, as well as the optimal interval between two consecutive inspection points. Finally, through comparison of strategies and sensitivity analysis, we demonstrated the economic advantages and the effectiveness of the proposed strategy. For a parallel system with high maintenance set‐up cost owing to severe environmental conditions, our proposed GM policy based on CBM outperformed other existing strategies in terms of cost benefit and ease of implementation. Highlights Proposing group maintenance (GM) schedules for cost‐effectiveness of the whole wind farm. Determining optimal inspection interval and number of the failed wind turbines for GM. A stationary probability density function is developed to address maintenance relevance. Investigating effects of various parameters on the optimal policy performance. Saving huge maintenance cost over the other two maintenance strategies.Jinhe WangXiaohong ZhangJianchao ZengWileyarticlecondition‐based maintenancegroup maintenanceparallel systemstationary probability density functionwind farmRenewable energy sourcesTJ807-830ENWind Energy, Vol 24, Iss 12, Pp 1517-1535 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	condition‐based maintenance group maintenance parallel system stationary probability density function wind farm Renewable energy sources TJ807-830
spellingShingle	condition‐based maintenance group maintenance parallel system stationary probability density function wind farm Renewable energy sources TJ807-830 Jinhe Wang Xiaohong Zhang Jianchao Zeng Optimal group maintenance decision for a wind farm based on condition‐based maintenance
description	Abstract This study investigated the group maintenance (GM) strategy based on condition‐based maintenance (CBM) of a parallel wind farm with multiple identical wind turbines. In terms of this strategy, the state of deterioration of the system is determined by periodic inspection, with the GM activities at each inspection point performed according to the number of failed turbines, which are determined by the corresponding state of deterioration of the system. Under this strategy, the probabilities that influence GM are derived by the stationary probability density function, which is based on the stationary law of the state of the wind farm system. Furthermore, employing numerical experiments and a case study, we verified the correctness and effectiveness of the stationary probability density function under different deterioration‐related parameters and obtained the optimal number of failed turbines when GM is performed, as well as the optimal interval between two consecutive inspection points. Finally, through comparison of strategies and sensitivity analysis, we demonstrated the economic advantages and the effectiveness of the proposed strategy. For a parallel system with high maintenance set‐up cost owing to severe environmental conditions, our proposed GM policy based on CBM outperformed other existing strategies in terms of cost benefit and ease of implementation. Highlights Proposing group maintenance (GM) schedules for cost‐effectiveness of the whole wind farm. Determining optimal inspection interval and number of the failed wind turbines for GM. A stationary probability density function is developed to address maintenance relevance. Investigating effects of various parameters on the optimal policy performance. Saving huge maintenance cost over the other two maintenance strategies.
format	article
author	Jinhe Wang Xiaohong Zhang Jianchao Zeng
author_facet	Jinhe Wang Xiaohong Zhang Jianchao Zeng
author_sort	Jinhe Wang
title	Optimal group maintenance decision for a wind farm based on condition‐based maintenance
title_short	Optimal group maintenance decision for a wind farm based on condition‐based maintenance
title_full	Optimal group maintenance decision for a wind farm based on condition‐based maintenance
title_fullStr	Optimal group maintenance decision for a wind farm based on condition‐based maintenance
title_full_unstemmed	Optimal group maintenance decision for a wind farm based on condition‐based maintenance
title_sort	optimal group maintenance decision for a wind farm based on condition‐based maintenance
publisher	Wiley
publishDate	2021
url	https://doaj.org/article/68b73fd747774a46810a747399b75150
work_keys_str_mv	AT jinhewang optimalgroupmaintenancedecisionforawindfarmbasedonconditionbasedmaintenance AT xiaohongzhang optimalgroupmaintenancedecisionforawindfarmbasedonconditionbasedmaintenance AT jianchaozeng optimalgroupmaintenancedecisionforawindfarmbasedonconditionbasedmaintenance
_version_	1718409296325115904

Optimal group maintenance decision for a wind farm based on condition‐based maintenance

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