複雜系統可靠度分析模式應用於核電廠安全評估

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研究生：	韓喬威 George-Williams, Hindolo
論文名稱：	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
指導教授：	李敏 Lee, Min Edoardo, Patelli Edoardo, Patelli
口試委員:	Ferson, Scott Ferson, Scott Andrews, John Andrews, John 趙春昌 Chao, Chun-Chang 許榮鈞 Sheu, Rong-Jiun Merk, Bruno Merk, Bruno
學位類別：	博士 Doctor
系所名稱：	原子科學院 - 核子工程與科學研究所 Nuclear Engineering and Science
論文出版年：	2018
畢業學年度：	107
語文別：	英文
論文頁數：	191
中文關鍵詞：	系統可靠性、核電廠、複雜系統、風險評估
外文關鍵詞：	System Reliability, Nuclear Power Plants, Complex Systems, Risk Assessment
相關次數：	點閱：3 下載：0
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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。

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.

Contents

Dedication    i
Acknowledgement    iii
Abstract    v
Declaration    ix
List of Publications    xi
Nomenclature    xix
List of Figures    xxvii
List of Tables    xxx
  Introduction    1
1    Background    1
2    Complex System Reliability    2
3    Motivation    2
3.1    The Role of System Reliability Analysis in Nuclear Safety    3
3.1.1    The Three Mile Island Accident    4
3.1.2    The Chernobyl Nuclear Disaster    5
3.1.3    The Fukushima Daiichi Disaster    5
3.1.4    Concluding Remarks    6
4    Aims and Objectives    7
5    Thesis Structure    7
  The State of the art in System Reliability & Risk Modelling    9
1    Existing Reliability Modelling Techniques    9
2    Dependencies in Engineering Systems    12
2.1    Forms of Interdependencies    13
2.1.1    Common-Cause Failures    14

2.1.2    Cascading Failures    15
3    Maintenance Modelling of Complex Systems    16
3.1    Maintenance Strategy Optimization    16
4    Nuclear Power Plant Safety    18
4.1    Station Blackout Risk Quanti cation    19
5    The Concept of Survival Signature    20
5.1    Theoretical Basics    21
5.2    Applications and Limitations    22
6    Chapter Summary    23
  Multi-state System Reliability Modelling & Evaluation    25
1    Introduction    25
2    Overview of Proposed Approach    26
2.1    Advantages Over Existing Techniques    27
3    Component Modelling    28
3.1    Application to Repairable Multi-state Components and Systems .    30
3.2    Determining Component  State Transition Parameters    32
4    System Modelling    33
4.1    The System as a Directed Graph    34
4.2    System Representation and Flow Analysis    36
4.2.1    Derivation of System Flow Equations    37
4.2.2    Output Calculation and Node Recon guration    41
4.3    Simulation Procedure    42
4.4    Limitations of the Proposed Approach    44
5    Case Studies    44
5.1    Case Study 1: A Simple Pipe Network    44
5.1.1    Analyses    45
5.1.2    Results and Comments    48
5.2    Case Study 2: A Multi-State Bridge Network    48
5.2.1    Analyses    49
5.2.2    Results and Comments    51
5.3    Computational Cost of Approach    53
6    Chapter Summary    54
  Availability Assessment of Interdependent Multi-state Systems    55
1    Introduction    55
2    Overview of Proposed Approach    56
3    Implementation    57
3.1    Decoupling the System    58
3.2    Accounting for Dependencies    61

3.3    Node Recon guration    62
3.4    Determining system performance at time t    62
4    The System Simulation Procedure    63
4.1    Forcing Maintenance    64
4.2    Maintenance Priority & Real-time Component Ranking    65
4.3    The Simulation Algorithm    66
5    Obtaining the Availability and Performance Indices    67
5.1    System Reliability and Recovery Probability    68
5.2    Instantaneous Availability and Expected System Output    69
5.3    Maintenance Response Inadequacy    71
6    Case Study: An O shore Oil Installation    72
6.1    Problem Formulation    72
6.1.1    Interdependencies & Recon guration    73
6.1.2    Maintenance Policy    73
6.1.3    Monte Carlo Simulation    74
6.2    Solution Procedure    74
6.2.1    Modelling the Plant    75
6.2.2    Production Level Determination    77
6.3    Simulation Results    77
6.3.1    Expected Production    81
6.3.2    Reliability and Recovery    81
6.3.3    E ects of Real-time component ranking    82
6.3.4    E ects of limited maintenance teams    83
6.4    Comments and Discussions    83
7    Chapter Summary    84
  Probabilistic Risk Assessment of Station Blackout Accidents    87
1    Introduction    87
2    The Proposed Approach and Scope    88
2.1    Merits & Novelty of Proposed Approach    88
2.2    Solution Sequence    89
3    Station Blackout Modelling    89
3.1    The System Topology    89
3.2    The System Components    91
3.2.1    Modelling the Grid and Switchyard    91
3.2.2    Modelling the Standby Power Systems    92
3.2.3    Accounting for Human Error    94
3.3    Modelling Component Interdependencies    96
3.3.1    The CCF Model    96
3.3.2    The Cascading Failure Model    97

4    System Simulation & Analysis    99
4.1    SBO Indices:  Computation & Relevance    100
4.2    Incorporation into the Existing Framework    101
5    Case Study:The Maanshan Nuclear Power Plant    102
5.1    Developing the System and Component Models    103
5.2    Component Reliability Data    105
5.3    Representing Component Interdependencies    107
5.4    Results and Discussions    110
6    Chapter Summary    113

  Maintenance Strategy Optimization for Complex Power Systems    115
1    Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .    115
1.1    Advantages of the Proposed Approach . . . . . . . . . . . . . . .    116
2    Problem Formulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . .    117
2.1    De nition of key terms . . . . . . . . . . . . . . . . . . . . . . . .    119
2.2    The Cost Model    . . . . . . . . . . . . . . . . . . . . . . . . . . .    120
2.3    Proposed Maintenance Regimes . . . . . . . . . . . . . . . . . . .    122
2.4    Solution Sequence    . . . . . . . . . . . . . . . . . . . . . . . . . .    123
3    System Reliability and Performance Analysis . . . . . . . . . . . . . . .    124
3.1    Component and System Representation . . . . . . . . . . . . . .    125
3.2    Maintenance Modelling of Components . . . . . . . . . . . . . . .    125
3.3    Determining Component Transition Parameters . . . . . . . . . .    130
3.3.1    Accounting for Non-Markovian Transitions . . . . . . .    130
3.4    Maintenance Strategy Implementation . . . . . . . . . . . . . . .    132
3.5    The Simulation Procedure . . . . . . . . . . . . . . . . . . . . . .    134
4    Case-Studies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .    137
4.1    Case-Study 1: A 50MW Hydroelectric Power Plant . . . . . . . .    137
4.1.1    Modelling the Plant and its Components    . . . . . . . .    138
4.1.2    The E ects of Maintenance on System Performance and
Reliability . . . . . . . . . . . . . . . . . . . . . . . . . .    139
4.1.3    Optimal Maintenance Strategy Identi cation . . . . . .    141
4.1.4    Sensitivity to Cost Levels . . . . . . . . . . . . . . . . .    142
4.1.5    Computational costs . . . . . . . . . . . . . . . . . . . .    142
4.1.6    Discussions . . . . . . . . . . . . . . . . . . . . . . . . .    142
4.2    Case-Study 2: The IEEE-24 Bus Reliability Test System . . . . .    144
4.2.1    Maintenance Information . . . . . . . . . . . . . . . . .    146
4.2.2    Maintenance Grouping and Costs    . . . . . . . . . . . .    147
4.2.3    Objective . . . . . . . . . . . . . . . . . . . . . . . . . .    147
4.2.4    System modelling    . . . . . . . . . . . . . . . . . . . . .    147
4.2.5    Component Modelling . . . . . . . . . . . . . . . . . . .    149


4.2.6    Results and Discussions    150
5    Chapter Summary    152
  An Extended Survival Signature Approach for Dependent Failures    155
1    Introduction    155
2    Overview of Proposed Approach    156
3    Modelling & Simulating the System    156
3.1    Components with Multiple Failure Modes    156
3.2    Cascading Failure Modelling and Propagation    158
3.3    CCF Modelling and Propagation    159
3.4    The Simulation Algorithm    162
3.5    Sensitivity Analysis    164
4    Case Studies    164
4.1    Case Study 1: A Complex Bridge Network    165
4.1.1    Analyses and Results    165
4.1.2    Discussions    167
4.2    Case Study 2: A Hydroelectric Power Plant    168
4.2.1    Analyses and Results    170
4.2.2    Discussions    171
5    Chapter Summary    172
  Conclusions    173
1    Concluding Remarks    173
2    Recommendations for Future Work    175

                                

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