【作者】 汪勇；

【导师】 黄树红； Jun Ni；

【作者基本信息】 华中科技大学 ， 热能工程， 2010， 博士

【摘要】 可靠性一直是衡量大型能源系统(比如核电和火力发电系统)性能的一个重要指标。由于其部件设备发生故障的随机性和故障类型的多样性,此类系统本身具有明显的多态性,其可靠性评价结果也伴随有一定的统计不确定性。根据可靠性评价进行系统结构设计和部件冗余分配是可靠性工程理论中一个重要的组合优化问题,它旨在构造一个具有高可靠性、低成本的最佳系统结构。对于传统的二态系统,该优化问题已有很多文献进行了深入研究,但是目前对于多态系统的可靠性优化研究仍然有限。本论文的研究重点在于解决多态系统可靠性优化中的如下三个基本问题。第一,本论文提出一种新的、基于粒子群优化(Particle Swarm Optimization, PSO)和局部搜索(Local Search, LS)的混合算法,来解决串并联和桥接多态系统可靠性异质优化问题。该混合算法的目的是确定合适的部件类型以及冗余度,使所构建的系统在满足所需的可靠性要求的前提下,具有最低的成本。所开发的新型局部搜索策略和动态罚函数提高了该混合算法的性能。本论文的第一部分主要工作采用了出现在文献中的一个常见假设：部件的可靠性精确已知。通用生成函数(Universal Generating Function, UGF)方法被用来精确计算系统的可靠性。为了评估该混合算法的有效性,本论文将其与现有的启发式和元启发式算法如遗传算法(Genetic Algorithm, GA)、禁忌搜索(Tabu Search, TS)、蚁群优化(Ant Colony Optimization, ACO)等进行了比较。结果显示利用PSO/LS算法求解得到的系统构架具有更低的成本。第二,在许多现实应用中,部件的可靠性往往不能精确给出,而需要根据试验或历史运行数据进行估计。在对部件可靠性进行估计的过程中,由于试验或历史运行数据的有限性,估计出的部件可靠性往往具有统计意义上的不确定性。根据部件的可靠性和系统的结构函数对系统的可靠性进行计算时,这种不确定性会传播至系统层面。在本论文的第二部分主要工作中,系统可靠性的计算已从精确计算的概念转换到了统计估计计算的概念。该统计估计计算模型是建立在推断统计理论基础上的。本论文通过一个迭代推导过程对不确定性传播机制进行了数学解释,并求出了系统可靠性的无偏估计以及伴随的方差和协方差的无偏估计。根据推导结果,本论文又为系统可靠性的估计值提出了一个新的具有95%置信度的置信区间下界,并证明其优于现有文献中的两个置信区间下界。推导结果也用于建立三个衡量部件重要度的指标。第三,在第一部分和第二部分工作的基础上,本论文将可靠性优化问题的模型进行了修改以集成统计不确定性,混合PSO/LS算法也进行了相应的修改。修改后的新模型旨在构建一个最低成本的系统,同时该系统能以95%的置信度水平满足所需的可靠性要求。对于同样的可靠性要求,利用新模型求得的系统方案通常成本更高,但系统结构得以强化,同时系统可靠性的期望值也得以提高。此外,新优化模型提供至少95%的置信度确保求得的系统方案符合要求的可靠性标准,而原来的优化模型则无法提供这样的保证。本论文中使用了已有文献中出现的各种数值算例来检验所提出的混合PSO/LS优化算法的有效性,与已有研究成果的对比突出了该混合算法的优点。本论文同时实现了一些算例的蒙特卡罗仿真(Monte Carlo Simulation, MCS),并将其作为辅助工具来验证可靠性估计推导的准确性,结果表明仿真结果与理论推导结果吻合良好。本论文同时提供了一个600MW发电机组的简化汽水回路系统作为一个完整的实例,以验证所提出的算法、方法和模型的具体应用效果。更多还原

【Abstract】 Reliability is an important performance measure of large-scale energy systems (such as nuclear and coal-fired thermal power systems). Due to the randomness and diversity of component failure, these systems have an inherent multi-state property, and their reliability evaluation results are accompanied by statistical uncertainty. In reliability engineering, redundancy allocation based on reliability evaluation results is an important combinatorial optimization problem that aims to determine an optimal system structure with high reliability and low cost. Although this optimization problem has been well studied for traditional binary-state systems (BSSs), its exploration for multi-state systems (MSSs) is still limited. This doctoral dissertation focuses on addressing the following three fundamental issues that are essential for the optimization problem for multi-state systems.First, a novel hybrid algorithm based on particle swarm optimization and local search (PSO/LS) has been proposed to solve the basic formulation of the optimization problem for series-parallel and bridge MSSs with heterogeneous redundancy. The hybrid algorithm aims to select appropriate components and the levels of redundancy so that the. built system has a minimum cost while providing a desired level of reliability. Novel local search neighborhood strategies and a dynamic penalty scheme are proposed to enhance the performance of the hybrid algorithm. The common assumption appeared in the literature is adopted in this part of the work:true reliabilities of components are assumed to be precisely known. The universal generating function (UGF) method is applied to precisely calculate the system reliability. The effectiveness of the proposed hybrid algorithm is assessed by comparing it with existing best-known algorithms based on heuristics and meta-heuristics such as genetic algorithms (GAs), tabu search (TS), and ant colony optimization (ACO), reported in the literature. The comparisons have shown the merits of the proposed PSO/LS algorithm in terms of the improved solution quality.Second, realizing that component reliabilities have to be estimated based on experimental or historical operation data, this dissertation argues that the assumption that true component reliabilities are precisely known may not be applicable to many real-world applications. Statistical uncertainty arises at the component level due to limited experimental or operational data and propagates to the system level. In this dissertation, the perspective of the acquisition of system reliability based on component reliabilities and the structure function has been switched from precise calculation to statistical estimation. The reliability estimation problem for MSSs is addressed based on the theories in inferential statistics. The uncertainty propagation mechanism is mathematically explained through an iterative theoretical derivation. The unbiased estimators of system reliability and the associated variance are obtained. With the help of the derived estimators, a lower confidence bound is proposed and proved superior to two existing bounds in the literature. Three importance measures of individual components in an MSS are also proposed.Finally, the optimization problem formulation is revised to consider statistical uncertainty. The hybrid PSO/LS algorithm is revised accordingly to determine the system structure with a minimum cost while providing a desired level of reliability with 95%of confidence. The solution provides additional assurance about the optimized system structure. For the same reliability requirement, the revised solution generally costs more, but the system structure is enhanced, and the expected system reliability is improved. Furthermore, the solution of the revised optimization model provides at least 95%of assurance that the system structure will satisfy the required reliability standard, while the solution of the original optimization model is not able to provide such an implication.Throughout this dissertation, various numerical examples of MSSs appeared in the literature have been used to examine the effectiveness of the proposed hybrid PSO/LS optimization algorithm. Comparisons with existing research results have highlighted the merits of the proposed algorithm. Monte Carlo simulation has also been implemented as a helpful tool to validate the accuracy of the reliability estimation derivation. It has been shown that the proposed approach is in good agreement with the simulation results. A complete example system, the water/steam loop system of a 600MW electric power generation unit, is provided to facilitate understanding the application of the proposed algorithms, approaches, and models.更多还原