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

英国正计划对海上风力发电场进行大规模投资，这将导致在英国海岸线周围安装几组类似的风力涡轮机。从经济角度来看，这些风力涡轮机具有很高的技术可用性，这意味着涡轮机必须在几乎所有时间都处于工作状态并准备好发电。如果不能实现这一目标，很可能会造成巨大的经济损失。不幸的是，近海系统的操作经验相对较少，可以作为所使用的估计的基础。系统可能会以意想不到的方式运行，比如比预期的更早失败，或者更难以维护。即使是众所周知的系统，在新环境中使用时也可能表现不同，这就是可靠性数据库通常指示故障行为范围而不是单个数字估计的原因。可用性很难建模，因为除了不同环境的未知影响之外，运营商和制造商经常需要一段时间来调整他们的流程和系统以适应新的情况，从而导致可用性增长的潜力。然而，随着一批新的涡轮机一起变老，也会有一个老化的过程，这可能导致可用性降低。海上系统的经济效益在很大程度上取决于是否能够实现足够高的可用性，特别是在运营的最初几年，这对收回投资成本很重要。该项目着眼于海上风力涡轮机可用性评估的不确定性程度。当有大量涡轮机时，这种不确定性不是平均的，因为它对风电场中的所有涡轮机都有系统的影响，因此导致整个风电场总体可用性的相应不确定性。这种类型的不确定性通常被称为知识状态不确定性，只有通过长期收集数据才能减少。即使我们还不能收集操作数据，我们仍然可以了解知识状态不确定性的来源。数学模型可以帮助我们理解不同的不确定性来源如何影响可用性的不确定性，并找出我们应该最关注的不确定性来源。反过来，这将有助于研究人员将精力集中在解决最终影响最大的问题上。在这个项目中，运营研究人员将与可再生能源领域的工程师和其他研究人员一起工作，以建立可靠的数学模型来帮助回答这些问题。这样做需要发展新的数学，特别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上。它要求我们找到更好的方法，将专家的信息转化为我们可以在数学模型中使用的形式，它还要求我们找到在计算机上运行模型的新方法。

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.