HOME-Offshore: Holistic Operation and Maintenance for Energy from Offshore Wind Farms

首页-海上：海上风电场能源整体运维

• 批准号: EP/P009743/1
• 负责人: Mike Barnes
• 金额: $ 388.4万
• 依托单位: University of Manchester
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2017
• 资助国家: 英国
• 起止时间: 2017 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FP009743%2F1
• 关键词: HOME; Offshore; Holistic; Operation; Maintenance

This project will undertake the research necessary for the remote inspection and asset management of offshore wind farms and their connection to shore. This industry has the potential to be worth £2billion annually by 2025 in the UK alone according to studies for the Crown Estate. At present most Operation and Maintenance (O&M) is still undertaken manually onsite. Remote monitoring through advanced sensing, robotics, data-mining and physics-of-failure models therefore has significant potential to improve safety and reduce costs.Typically 80-90% of the cost of offshore O&M according to the Crown Estate is a function of accessibility during inspection - the need to get engineers and technicians to remote sites to evaluate a problem and decide what remedial action to undertake. Minimising the need for human intervention offshore is a key route to maximising the potential, and minimising the cost, for offshore low-carbon generation. This will also ensure potential problems are picked up early, when the intervention required is minimal, before major damage has occurred and when maintenance can be scheduled during a good weather window. As the Crown Estate has identified: "There is an increased focus on design for reliability and maintenance in the industry in general, but the reality is that there is a still a long way to go. Wind turbine, foundation and electrical elements of the project infrastructure would all benefit from innovative solutions which can demonstrably reduce O&M spending and downtime". Recent, more detailed, academic studies support this position.The wind farm is however an extremely complicated system-of-systems consisting of the wind turbines, the collection array and the connection to shore. This consists of electrical, mechanical, thermal and materials engineering systems and their complex interactions. Data needs to be extracted from each of these, assessed as to its significance and combined in models that give meaningful diagnostic and prognostic information. This needs to be achieved without overwhelming the user. Unfortunately, appropriate multi-physics sensing schemes and reliability models are a complex and developing field, and the required knowledge base is presently scattered across a variety of different UK universities and subject specialisms.This project will bring together and consolidate theoretical underpinning research from a variety of disparate prior research work, in different subject areas and at different universities. Advanced robotic monitoring and advanced sensing techniques will be integrated into diagnostic and prognostic schemes which will allow improved information to be streamed into multi-physics operational models for offshore windfarms. Life-time, reliability and physics of failure models will be adapted to provide a holistic view of wind-farms system health and include these new automated information flows. While aspects of the techniques required in this offshore application have been previously used in other fields, they are innovative for the complex problems and harsh environment in this offshore system-of-systems. 'Marinising' these methods is a substantial challenge in itself. The investigation of an integrated monitoring platform and the reformulation of models and techniques to allow synergistic use of data flow in an effective and efficient diagnostic and prognostic model is ambitious and would allow a major step change over present practice.

该项目将进行对海上风电场远程检查和资产管理所需的研究及其与海岸的联系。根据Crown Estate的研究，该行业有可能到2025年在英国每年价值200亿英镑。目前，大多数操作和维护（O＆M）仍在手动现场进行。因此，通过高级感应，机器人技术，数据挖掘和失败模型进行远程监控具有提高安全性和降低成本的巨大潜力。通常，根据皇冠遗产的O＆M成本的80-90％是检查过程中可访问性的函数 - 需要将工程师和技术人员带到远程站点以评估问题并确定要采取的补救措施。最大程度地减少对海上人工干预的需求是使海上低碳生成最大化潜力并最大程度地降低成本的关键途径。这还将确保在发生重大损害之前以及在良好的天气窗口中安排维护时，在最小的干预措施时，可能会提早提早解决。正如Crown Estate所确定的那样：“对整个行业的可靠性和维护的设计越来越重视，但现实情况是，项目基础设施的风力涡轮机，基础和电气元素都将受益于创新的解决方案，这些解决方案都可以降低O＆M的稳定和停机时间。”最近，更详细的学术研究支持这一职位。但是，风电场是一个非常复杂的系统系统，该系统由风力涡​​轮机，收集阵列和与海岸的连接组成。这包括电气，机械，热和材料工程系统及其复杂的相互作用。数据需要从每个中的每一个中提取，并评估其意义，并在提供有意义的诊断和预后信息的模型中合并。这需要在不压倒用户的情况下实现。不幸的是，适当的多物理传感方案和可靠性模型是一个复杂且发展中的领域，所需的知识库散布在各种不同的英国和学科专业上。这项项目将在不同学科和不同学业和不同大学中汇集理论基础研究，并巩固理论上的基础研究。先进的机器人监测和高级感应技术将集成到诊断和预后方案中，这些方案将允许将改进的信息流到离岸风电场的多物理操作模型中。终身，可靠性和故障模型物理学将适应风格系统健康的整体视图，并包括这些新的自动化信息流。虽然此外应用程序中所需的技术的各个方面以前已在其他领域中使用，但它们对这个离岸系统中的复杂问题和HARMSH环境具有创新性。这些方法本身就是一个重大的挑战。集成监控平台的投资以及对模型和技术的重新制定，以在有效而有效的诊断和预后模型中协同使用数据流，这是雄心勃勃的，并且可以在当前实践中做出重大步骤。

项目成果

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• 发表时间: 2019
• 期刊: The Journal of Engineering
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O&M Cost-Based FMECA: Identification and Ranking of the Most Critical Components for 2-4 MW Geared Offshore Wind Turbines

氧

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• 作者: Cevasco D

Wind Turbine Generator Controller Signals Supervised Machine Learning for Shaft Misalignment Fault Detection: A Doubly Fed Induction Generator Practical Case Study

• DOI: 10.3390/en14061601
• 发表时间: 2021-03
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Long Term Reliability of Power Modules with Low Amplitude Thermomechanical Stresses and Initial Defects

• DOI: 10.1109/ecce.2018.8558137
• 发表时间: 2018-09
• 期刊: 2018 IEEE Energy Conversion Congress and Exposition (ECCE)
• 作者: Borong Hu;Sylvia Konaklieva;L. Ran;N. Kourra;M. Williams;Wei Lai;P. Mawby