Self-adaptive optimized maintenance of offshore wind turbines by intelligent Petri nets

Ali Saleh; Manuel Chiachio; Juan Fernandez SalasAthanasios Kolios

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Self-adaptive optimized maintenance of offshore wind turbines by intelligent Petri nets

机译：Self-adaptive optimized maintenance of offshore wind turbines by intelligent Petri nets

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With the emerging monitoring technologies, condition-based maintenance is nowadays a reality for the wind energy industry. This is important to avoid unnecessary maintenance actions, which increase the operation and maintenance costs, along with the costs associated with downtime. However, condition-based maintenance requires a policy to transform system conditions into decision-making while considering monetary restrictions and energy productivity objectives. To address this challenge, an intelligent Petri net algorithm has been created and applied to model and optimize offshore wind turbines' operation and maintenance. The proposed method combines advanced Petri net modelling with Reinforcement Learning and is formulated in a general manner so it can be applied to optimize any Petri net model. The resulting methodology is applied to a case study considering the operation and maintenance of a wind turbine using operation and degradation data. The results show that the proposed method is capable to reach optimal condition-based maintenance policy considering maximum availability (equal to 99.4) and minimal operational costs.

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来源
《Reliability engineering & system safety》 |2023年第3期|109013.1-109013.20|共20页
作者
Ali Saleh; Manuel Chiachio; Juan Fernandez SalasAthanasios Kolios;
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作者单位

Andalusian Research Institute in Data Science and Computational Intelligence, University of Granada, 18071, Spain,Dept Structural Mechanics & Hydraulics Engineering, University of Granada, 18071, Granada, Spain;

Department of Wind & Energy Systems, Technical University of Denmark, RisoCampus Frederiksborgvej 399, DK-4000, Roskilde, Denmark;

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收录信息美国《科学引文索引》(SCI);美国《工程索引》(EI);
原文格式 PDF
正文语种英语
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关键词
Petri net; Reinforcement learning; Q-learning; Offshore wind turbines; Condition-based maintenance;

Self-adaptive optimized maintenance of offshore wind turbines by intelligent Petri nets

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