The safety case and the lessons learned for the reliability and maintainability case

This paper examine the safety case and the lessons learned for the reliability and maintainability case

System reliability analyses and optimal maintenance planning of corroding pipelines

The failure of corroding pipeline joints may induce severe consequences. However, maintenance is expensive due to the cost of excavating and repairing a single joint and typically a significant number of joints that need repair. It is central to develop an optimal cost-effective maintenance strategy that balances cost and safety. A key component of the strategy is the reliability based condition evaluation of pipeline joints. The focus of the research reported in this thesis is therefore developing efficient reliability assessment methods for pipeline individual joints, and developing an optimal maintenance framework for the entire pipeline system. First, efficient system reliability methods relying on the first-order reliability method (FORM) and important sampling (IS) are developed for the assessment of the time-dependent probabilities of small leak and burst failure of pipeline joints containing multiple corrosion defects. In addition, a novel method is developed within the FORM to obtain the design points efficiently. An improved equivalent component approach for evaluating multi-normal integrals is also developed to improve the efficiency of the FORM for system reliability analysis. In addition, a multi-objective optimization-based maintenance framework for corroding pipeline systems is formulated optimizing three objectives, i.e. the conditioned probabilities of burst and small leak, respectively, and repair cost. An improved genetic algorithm with a pre-training population is utilized to investigate the optimal Pareto front. The benefits of this framework enable decision makers to access a series of non-dominated optimal repairing solutions with respect to multiple conflicting objectives

Bayesian Subset Simulation: a kriging-based subset simulation algorithm for the estimation of small probabilities of failure

The estimation of small probabilities of failure from computer simulations is a classical problem in engineering, and the Subset Simulation algorithm proposed by Au & Beck (Prob. Eng. Mech., 2001) has become one of the most popular method to solve it. Subset simulation has been shown to provide significant savings in the number of simulations to achieve a given accuracy of estimation, with respect to many other Monte Carlo approaches. The number of simulations remains still quite high however, and this method can be impractical for applications where an expensive-to-evaluate computer model is involved. We propose a new algorithm, called Bayesian Subset Simulation, that takes the best from the Subset Simulation algorithm and from sequential Bayesian methods based on kriging (also known as Gaussian process modeling). The performance of this new algorithm is illustrated using a test case from the literature. We are able to report promising results. In addition, we provide a numerical study of the statistical properties of the estimator.Comment: 11th International Probabilistic Assessment and Management Conference (PSAM11) and The Annual European Safety and Reliability Conference (ESREL 2012), Helsinki : Finland (2012

Reliable fault-tolerant model predictive control of drinking water transport networks

This paper proposes a reliable fault-tolerant model predictive control applied to drinking water transport networks. After a fault has occurred, the predictive controller should be redesigned to cope with the fault effect. Before starting to apply the fault-tolerant control strategy, it should be evaluated whether the predictive controller will be able to continue operating after the fault appearance. This is done by means of a structural analysis to determine loss of controllability after the fault complemented with feasibility analysis of the optimization problem related to the predictive controller design, so as to consider the fault effect in actuator constraints. Moreover, by evaluating the admissibility of the different actuator-fault configurations, critical actuators regarding fault tolerance can be identified considering structural, feasibility, performance and reliability analyses. On the other hand, the proposed approach allows a degradation analysis of the system to be performed. As a result of these analyses, the predictive controller design can be modified by adapting constraints such that the best achievable performance with some pre-established level of reliability will be achieved. The proposed approach is tested on the Barcelona drinking water transport network.Postprint (author's final draft

Adaptive Robust Optimization with Dynamic Uncertainty Sets for Multi-Period Economic Dispatch under Significant Wind

The exceptional benefits of wind power as an environmentally responsible renewable energy resource have led to an increasing penetration of wind energy in today's power systems. This trend has started to reshape the paradigms of power system operations, as dealing with uncertainty caused by the highly intermittent and uncertain wind power becomes a significant issue. Motivated by this, we present a new framework using adaptive robust optimization for the economic dispatch of power systems with high level of wind penetration. In particular, we propose an adaptive robust optimization model for multi-period economic dispatch, and introduce the concept of dynamic uncertainty sets and methods to construct such sets to model temporal and spatial correlations of uncertainty. We also develop a simulation platform which combines the proposed robust economic dispatch model with statistical prediction tools in a rolling horizon framework. We have conducted extensive computational experiments on this platform using real wind data. The results are promising and demonstrate the benefits of our approach in terms of cost and reliability over existing robust optimization models as well as recent look-ahead dispatch models.Comment: Accepted for publication at IEEE Transactions on Power System