Sustainable Operation and Maintenance of Offshore Wind Farms Based on the Deep Wind Forecasting

Offshore wind farms are becoming a pivotal solution to address the increasing energy demand worldwide and reduce carbon emissions to achieve a sustainable energy sector. Considering the higher operational and maintenance cost of offshore wind farms, it is important to make a good maintenance plan to...
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Autor*in:	Xue Zhou [verfasserIn] Yajian Ke [verfasserIn] Jianhui Zhu [verfasserIn] Weiwei Cui [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2023

Schlagwörter:	offshore wind farm maintenance planning optimization deep learning

Übergeordnetes Werk:	In: Sustainability - MDPI AG, 2009, 16(2023), 1, p 333
Übergeordnetes Werk:	volume:16 ; year:2023 ; number:1, p 333

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3390/su16010333

Katalog-ID:	DOAJ097765643

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callnumber-first	T - Technology
author	Xue Zhou
spellingShingle	Xue Zhou misc TD194-195 misc TJ807-830 misc GE1-350 misc offshore wind farm misc maintenance planning misc optimization misc deep learning misc Environmental effects of industries and plants misc Renewable energy sources misc Environmental sciences Sustainable Operation and Maintenance of Offshore Wind Farms Based on the Deep Wind Forecasting
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topic_title	TD194-195 TJ807-830 GE1-350 Sustainable Operation and Maintenance of Offshore Wind Farms Based on the Deep Wind Forecasting offshore wind farm maintenance planning optimization deep learning
topic	misc TD194-195 misc TJ807-830 misc GE1-350 misc offshore wind farm misc maintenance planning misc optimization misc deep learning misc Environmental effects of industries and plants misc Renewable energy sources misc Environmental sciences
topic_unstemmed	misc TD194-195 misc TJ807-830 misc GE1-350 misc offshore wind farm misc maintenance planning misc optimization misc deep learning misc Environmental effects of industries and plants misc Renewable energy sources misc Environmental sciences
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title	Sustainable Operation and Maintenance of Offshore Wind Farms Based on the Deep Wind Forecasting
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title_full	Sustainable Operation and Maintenance of Offshore Wind Farms Based on the Deep Wind Forecasting
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title_sort	sustainable operation and maintenance of offshore wind farms based on the deep wind forecasting
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title_auth	Sustainable Operation and Maintenance of Offshore Wind Farms Based on the Deep Wind Forecasting
abstract	Offshore wind farms are becoming a pivotal solution to address the increasing energy demand worldwide and reduce carbon emissions to achieve a sustainable energy sector. Considering the higher operational and maintenance cost of offshore wind farms, it is important to make a good maintenance plan to guarantee the system’s reliability and reduce the total cost related to maintenance activities at the same time. Because maintenance planning is a long-term decision problem and the wind force is random, long-term wind force prediction is needed to help managers evaluate the loss caused by maintenances to be executed in the future. However, long-term wind force prediction is naturally complicated, which is much harder than the short-term (e.g., day-ahead) prediction widely investigated in the literature. In order to overcome this difficulty, we design a deep learning framework combining variational mode decomposition, a convolution neural network, long short-term memory network, and full-connected network. Using the public data from the city of Leeds, the prediction accuracy of the above framework is validated by comparing it with other prediction techniques. Then, the predicted wind force is input into the established optimization model determining preventive maintenances during a predefined period. Because the uncertainty of wind force is replaced by the prediction value, the optimization model can be established as a mixed-integer linear programing model, which only contains limited variables and can be solved quickly. Lastly, an abundance of numerical experiments are conducted to validate the effectiveness of the proposed optimization model, based on which some managerial insights are provided to the managers of offshore wind farms about the optimal operations and maintenance strategy. The research outcome will greatly promote the development of the wind power industry in the future.
abstractGer	Offshore wind farms are becoming a pivotal solution to address the increasing energy demand worldwide and reduce carbon emissions to achieve a sustainable energy sector. Considering the higher operational and maintenance cost of offshore wind farms, it is important to make a good maintenance plan to guarantee the system’s reliability and reduce the total cost related to maintenance activities at the same time. Because maintenance planning is a long-term decision problem and the wind force is random, long-term wind force prediction is needed to help managers evaluate the loss caused by maintenances to be executed in the future. However, long-term wind force prediction is naturally complicated, which is much harder than the short-term (e.g., day-ahead) prediction widely investigated in the literature. In order to overcome this difficulty, we design a deep learning framework combining variational mode decomposition, a convolution neural network, long short-term memory network, and full-connected network. Using the public data from the city of Leeds, the prediction accuracy of the above framework is validated by comparing it with other prediction techniques. Then, the predicted wind force is input into the established optimization model determining preventive maintenances during a predefined period. Because the uncertainty of wind force is replaced by the prediction value, the optimization model can be established as a mixed-integer linear programing model, which only contains limited variables and can be solved quickly. Lastly, an abundance of numerical experiments are conducted to validate the effectiveness of the proposed optimization model, based on which some managerial insights are provided to the managers of offshore wind farms about the optimal operations and maintenance strategy. The research outcome will greatly promote the development of the wind power industry in the future.
abstract_unstemmed	Offshore wind farms are becoming a pivotal solution to address the increasing energy demand worldwide and reduce carbon emissions to achieve a sustainable energy sector. Considering the higher operational and maintenance cost of offshore wind farms, it is important to make a good maintenance plan to guarantee the system’s reliability and reduce the total cost related to maintenance activities at the same time. Because maintenance planning is a long-term decision problem and the wind force is random, long-term wind force prediction is needed to help managers evaluate the loss caused by maintenances to be executed in the future. However, long-term wind force prediction is naturally complicated, which is much harder than the short-term (e.g., day-ahead) prediction widely investigated in the literature. In order to overcome this difficulty, we design a deep learning framework combining variational mode decomposition, a convolution neural network, long short-term memory network, and full-connected network. Using the public data from the city of Leeds, the prediction accuracy of the above framework is validated by comparing it with other prediction techniques. Then, the predicted wind force is input into the established optimization model determining preventive maintenances during a predefined period. Because the uncertainty of wind force is replaced by the prediction value, the optimization model can be established as a mixed-integer linear programing model, which only contains limited variables and can be solved quickly. Lastly, an abundance of numerical experiments are conducted to validate the effectiveness of the proposed optimization model, based on which some managerial insights are provided to the managers of offshore wind farms about the optimal operations and maintenance strategy. The research outcome will greatly promote the development of the wind power industry in the future.
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container_issue	1, p 333
title_short	Sustainable Operation and Maintenance of Offshore Wind Farms Based on the Deep Wind Forecasting
url	https://doi.org/10.3390/su16010333 https://doaj.org/article/8d2532decbf041ad9acb0b0ea5a79866 https://www.mdpi.com/2071-1050/16/1/333 https://doaj.org/toc/2071-1050
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up_date	2024-07-03T13:39:26.058Z
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Sustainable Operation and Maintenance of Offshore Wind Farms Based on the Deep Wind Forecasting

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