Mathematical tools for improving the understanding of uncertainty in offshore turbine operation and maintenance

用于提高对海上涡轮机运行和维护不确定性的理解的数学工具

基本信息

批准号：
EP/I017380/1
负责人：
Tim Bedford
金额：
$ 31.12万
依托单位：
University of Strathclyde
依托单位国家：
英国
项目类别：
Research Grant
财政年份：
2011
资助国家：
英国
起止时间：
2011 至无数据
项目状态：
已结题

来源：
https://gtr.ukri.org/projects?ref=EP%2FI017380%2F1
关键词：
Mathematical tools improving understanding uncertainty

项目摘要

The UK is planning to make massive investments in offshore wind farms which will result in several fleets of similar wind turbines being installed around the UK coastline. The economic case for these wind turbines assumes a very high technical availability, which means simply that the turbines have to be working and ready to generate electricity for nearly all of the time. Not achieving this availability could well result in large economic losses. Unfortunately there is relatively little operational experience of offshore systems on which to base the estimates used. The systems may turn out to behave in unexpected ways by failing earlier than expected, or by proving more difficult to maintain. Even well-known systems can behave differently when used in new environments, which is why reliability databases often indicate ranges of failure behaviour rather than single number estimates. Availability is difficult to model because, in addition to the unknown impact of different environments, there is often a period of adjustment in which operators and manufacturers adapt their processes and systems to the new situation, leading to the potential for availability growth. However, with a new fleet of turbines there is also an aging process as they all grow older together which could lead to lower availability. The economic case for offshore systems depends a lot on whether high enough availability can be achieved, particularly in the early years of operation which are important for paying back the investment costs. This project looks at the degree of uncertainty there is in availability estimates for offshore wind turbines. This uncertainty is not one that averages out when there are a large number of turbines, because it has a systematic affect across all the turbines in a wind farm and therefore leads to corresponding uncertainty in the overall availability across the wind farm. This type of uncertainty is often called state-of-knowledge uncertainty and only gets reduced by collecting data over the longer term. Even if we are not yet able to collect operational data, we can still gain an understanding of the sources of state-of-knowledge uncertainty. Mathematical models can help us understand how different sources of uncertainty affect the uncertainty about availability, and to find out which ones we should be most concerned about. That, in turn, will help researchers to focus their energies on resolving the issues that ultimately have the biggest impact.In this project, operations researchers will work together with engineers and other researchers in the renewables sector, in order to build credible mathematical models to help answer these questions. Doing that requires the development of new mathematics, particularly in the way we represent how uncertainties are affected by different environmental and engineering aspects. It requires us to find better ways of getting information from experts into a form that we can use in the mathematical models, and it also requires us to find new ways of running the models on a computer.

英国正计划在海上风电场进行大量投资，这将导致英国海岸线周围安装几个类似的风力涡轮机队。这些风力涡轮机的经济案例假设技术可用性很高，这仅意味着涡轮机必须在几乎所有时间内都可以发电。无法实现这种可用性可能会导致巨大的经济损失。不幸的是，离岸系统的操作经验相对较少，可以在其上基于所使用的估计。该系统可能会因早于预期的早失败或证明更难维护而以意外的方式行为。在新环境中使用时，即使是知名的系统也可能行为不同，这就是为什么可靠性数据库通常表明故障行为的范围而不是单个数字估计值的范围。可用性很难建模，因为除了不同环境的未知影响外，经营者和制造商通常会调整时期，使其流程和系统适应新情况，从而导致了可用性增长的潜力。但是，随着新的涡轮机队的衰老过程，它们都长大了，这可能会导致可用性降低。离岸系统的经济案例在很大程度上取决于是否可以实现足够高的可用性，尤其是在运营的早期，这对于偿还投资成本很重要。该项目着眼于海上风力涡轮机的可用性估计值。这种不确定性并不是当大量涡轮机时平均，因为它在风电场的所有涡轮机上都有系统的影响，因此导致整个风电场的总体可用性相应的不确定性。这种类型的不确定性通常称为已知不确定性，并且只能在长期内收集数据来减少。即使我们还不能收集运营数据，我们仍然可以了解已知不确定性的来源。数学模型可以帮助我们了解不同的不确定性来源如何影响可用性的不确定性，并找出我们最关心哪些不确定性。反过来，这将帮助研究人员将精力集中在解决最终影响最大的问题上。在该项目中，运营研究人员将与可再生能源部门的工程师和其他研究人员一起工作，以建立可信的数学模型，以建立可信的数学模型帮助回答这些问题。这样做需要开发新数学，尤其是在我们表示不确定性如何受到不同环境和工程方面的影响的方式。它要求我们找到更好的方法将信息从专家获取到可以在数学模型中使用的形式，并且还要求我们找到在计算机上运行模型的新方法。