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

该项目将为海上风电场的远程检查和资产管理及其与岸上的连接进行必要的研究。根据皇家地产的研究，到2025年，仅在英国，这个行业每年的价值就有可能达到20亿GB。目前，大多数运行维护(O&M)仍然是现场人工进行的。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潜力。根据皇家地产的数据，通常80%-90%的离岸运营和维护成本是在检查期间可访问性的功能-需要让工程师和技术人员到偏远地点评估问题并决定采取何种补救行动。最大限度地减少海上人工干预的需要是最大限度地发挥海上低碳发电的潜力并将成本降至最低的关键途径。这也将确保在所需的干预最少的情况下，在重大损坏发生之前，以及在天气好的时候可以安排维修，及早发现潜在的问题。正如英国皇家地产所指出的：“整个行业越来越注重可靠性和维护性的设计，但现实情况是，还有很长的路要走。风力涡轮机、基础设施和项目基础设施的电气元件都将受益于创新的解决方案，这些解决方案可以明显减少运营和维护支出和停机时间。”最近更详细的学术研究支持这一观点。然而，风电场是一个极其复杂的系统系统，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、热学和材料工程系统及其复杂的相互作用。需要从其中的每一个中提取数据，评估其重要性，并将其组合在提供有意义的诊断和预后信息的模型中。这需要在不让用户不知所措的情况下实现。不幸的是，合适的多物理传感方案和可靠性模型是一个复杂和发展中的领域，所需的知识库目前分散在各种不同的英国大学和学科专业。该项目将汇集和巩固来自不同学科领域和不同大学的各种不同先前研究工作的理论基础研究。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、可靠性和故障物理模型将被调整，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。虽然这项离岸应用中所需技术的某些方面以前曾用于其他领域，但对于这一离岸系统中复杂的问题和恶劣的环境来说，这些技术是创新的。“推广”这些方法本身就是一个巨大的挑战。调查综合监测平台，重新制定模型和技术，以便在有效和高效的诊断和预测模型中协同使用数据流，这是雄心勃勃的，将使目前的做法发生重大变化。

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.