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.

该项目将进行海上风电场及其与海岸连接的远程检查和资产管理所需的研究。根据英国皇家财产局的研究，到2025年，仅在英国，这个行业就有可能每年价值20亿英镑。目前，大多数操作和维护（O&M）仍然是在现场手动进行的。因此，通过先进的传感、机器人技术、数据挖掘和故障物理模型进行远程监控，在提高安全性和降低成本方面具有巨大的潜力。根据Crown Estate的数据，通常海上O&M成本的80-90%是检查期间的可访问性-需要让工程师和技术人员到远程站点评估问题并决定采取什么补救措施。最大限度地减少海上人工干预的需要是最大限度地发挥潜力和最大限度地降低成本的关键途径，以实现海上低碳发电。这也将确保在所需的干预最小的情况下，在发生重大损坏之前，以及在良好天气窗口期间可以安排维护时，尽早发现潜在的问题。正如皇家地产公司所指出的：“总体而言，整个行业越来越关注可靠性和维护性的设计，但现实是，还有很长的路要走。风力涡轮机、项目基础设施的基础和电气元件都将受益于创新的解决方案，这些解决方案可以显着减少O&M支出和停机时间”。然而，风力发电场是一个极其复杂的系统，包括风力涡轮机、收集阵列和与海岸的连接。这包括电气，机械，热和材料工程系统及其复杂的相互作用。需要从这些数据中提取数据，评估其重要性，并结合模型，提供有意义的诊断和预后信息。这需要在不压倒用户的情况下实现。不幸的是，适当的多物理场传感方案和可靠性模型是一个复杂的和发展中的领域，所需的知识基础目前分散在各种不同的英国大学和学科specialism.This项目将汇集和巩固理论基础研究从各种不同的先前研究工作，在不同的学科领域和不同的大学。先进的机器人监测和先进的传感技术将被整合到诊断和预测计划中，这将使改进后的信息能够流入海上风电场的多物理运行模型。寿命，可靠性和故障模型的物理特性将被调整，以提供风电场系统健康的整体视图，并包括这些新的自动化信息流。虽然这种海上应用所需的技术方面以前已用于其他领域，但它们对于这种海上系统中的复杂问题和恶劣环境是创新的。“浸泡”这些方法本身就是一个巨大的挑战。对综合监测平台的调查以及对模型和技术的重新制定，以便在有效和高效的诊断和预测模型中协同使用数据流，这是一项雄心勃勃的工作，将使目前的做法发生重大变化。

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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
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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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• 影响因子: 3.2
• 作者: Ahmed Al-Ajmi;Yingzhao Wang;S. Djurović