- 學位論文
複雜系統可靠度分析模式應用於核電廠安全評估
Efficient Reliability Modelling & Analysis of Complex Systems with Application to Nuclear Power Plant Safety
摘要
摘要 因为核能的可持续性以及无污染性，它或许是人类解决世界能源问题最好的机会。但是在生产过程中，核能也会对人们的生命、财产以及经济产生一定的威胁。核能生产设备事故很多是由于零件失效，人为失误，极端天气，蓄意攻击以及自然灾害。考虑到核能系统的复杂性以及交互性，一件很小的初始事件都可能会造成灾难性的后果。因此，意识到这些事件对核电站的影响，以及得知其造成的后果之前，应当采取适当的缓解措施。 系统可靠性是其在一定时间内正常运行的可能性，适用性是其在一定情况下达到要求的运行性。这些参数对核电厂的安全很重要，因为核电站默认配备安全系统，用来一直初始事件的传播。当由于初始事件造成的安全系统不可用或者无法启动，事故就会产生。因此说可靠性和适用性构成了核电厂的安全性。这些参数目前都是使用传统的技术进行估量，例如静态的故障树或事件树分析法，以及它们的衍生方法。尽管它们的受欢迎程度和广受好评的成功，这些传统技术依然不能实现大多数系统动态运行的分析。最重要的是随着系统规模和复杂性的增加，想要应用这些方法必将要做出不切实际的假设。这些不切实际的假设有时会影响所得结果的准确性以及风险决策的质量。传统方法的不足尤其体现在多状态元件系统中，并且由于模糊或者不确定性数据而放大。所以理想的方法应该摒弃不切实际的假设，同时又可以容纳真实系统的属性以及准确性。 本文克服了现有的概率风险评估方法的局限性，详细介绍了一系列有效的可靠性分析法。该文提出的方法主要基于先进混合事件驱动的模特卡罗模拟法，该方法应用负载原则直观的解决系统的拓扑复杂性以及元件多态性。除了其直观性以及相对完整性，该方法一个核心的优势是其普遍的适用性。改法已被应用于各种各样的问题，例如从海岸石油装置的生产可用性，到IEEE-24总线测试系统的维护策略优化，到台湾Maanshan核电站的风险评估。因此本文所提出的技术不仅有助于对核电厂，甚至其他关键系统的风险管理做出有力的决定。这些方法已被纳入开源不确定性量化工具OpenCossan，供业界和其他研究人员使用。
並列摘要
Nuclear power may be our best chance at a permanent solution to the world's energy challenges, owing to its sustainability and environmental friendliness. However, it also poses a great risk to life, property, and the economy, given the possibility of severe accidents during its generation. These accidents are a result of the susceptibility of the generating plants to component failure, human error, extreme environmental events, targeted attacks, and natural disasters. Given the complexity and high interconnectivity of the systems in question, a small glitch, otherwise known as an initiating event, could cascade to catastrophic consequences. It is, therefore, vital that the vulnerability of a plant to these glitches and their ensuing consequences be ascertained, to ensure that the appropriate mitigating actions are taken. The reliability of a system is the likelihood that it survives a defined period and its availability is the likelihood of it being capable of performing its required functions on demand. These quantities are important to a nuclear power plant's safety because, a nuclear power plant by default is equipped with safety systems to inhibit the propagation of an initiating event. An accident ensues if the safety systems required to mitigate some initiating event are unavailable or incapacitated by the initiating event. It is, therefore, easy to see that the reliability, as well as the availability of these systems, shape the safety of the plant. These crucial quantities, currently, are estimated using legacy techniques like static fault and event tree analyses or their derivatives. Despite their popularity and widely acclaimed success, these legacy techniques lack the flexibility to implement fully the operational dynamics of the majority of systems. Most importantly, their ease of application deteriorates with increasing system size and complexity, such that the analyst is often forced to make unrealistic assumptions. These unrealistic assumptions sometimes compromise the accuracy of the results obtained and subsequently, the quality of the risk management decisions reached. Their inadequacy is often amplified if the system is composed of multi-state components or characterised by epistemic uncertainties, induced by vague or imprecise data. The ideal approach, therefore, should be sufficiently robust to not necessitate unrealistic assumptions but flexible enough to accommodate realistic system attributes, while guaranteeing accuracy. This dissertation provides a detailed account of a series of computationally efficient system reliability analysis techniques proposed to address the limitations of the existing probabilistic risk assessment approaches. The proposed techniques are based mainly, on an advanced hybrid event-driven Monte Carlo simulation technique that invokes load-flow principles to resolve, intuitively, the difficulties associated with the topological complexity of systems and the multi-state attributes of their components. In addition to their intuitiveness and relative completeness, a key advantage of the proposed techniques is their general applicability. They have been applied, for instance, to a variety of problems, ranging from the production availability of an offshore oil installation and the maintenance strategy optimization of the IEEE-24 bus test system to the probabilistic risk assessment of station blackout accidents at the Maanshan nuclear power plant in Taiwan. The proposed techniques, therefore, should influence robust decisions in the risk management of not only nuclear power plants but other critical systems as well. They have been incorporated into the open-source uncertainty quantification tool, OpenCossan, to render them readily available to industry and other researchers.