Currently, major power systems almost invariably operate under interconnected conditions to transfer power in a stable and reliable manner. Multi-area reliability evaluation has thus become an invaluable tool in the planning and operation of such systems. Multi - area reliability evaluation is typically done by considering equivalent tie lines between different areas in an integrated power system. It gives approximate results for the reliability indices of a power system as it models each of the areas as a single node to which are connected the entire area generation and loads. The intratransmission lines are only indirectly modeled during the calculation of equivalent tie lines' capacities. This method is very widely used in the power industry, but the influence of the various approximations and assumptions, which are incorporated in this method, on reliability calculations has not been explored. The objective of the research work presented in this thesis is the development of a new method called Composite system based multi - area reliability model, which does multi - area reliability evaluation considering the whole composite system. It models the transmission system in detail and also takes into account the loss sharing policy within an area and no - load loss sharing policy among the areas. The proposed method is applied to standard IEEE 24 bus Reliability Test System (RTS) and the traditional equivalent tie-line method is applied to the multi-area configuration of the same test system. The results obtained by both the methods are analyzed and compared. It is found that the traditional model, although having some advantages, may not give accurate results.

Modern day bridge foundations are often designed for the maximum loading condition on a single member and each following foundation member often follows the same design. A 4-pile and a 2-pile foundation system were analyzed with an upperbound plasticity model in an attempt to approximately represent the system capacity. A sensitivity analysis was performed to measure the change in the ultimate system capacity by altering the parameters used to define individual pile capacities. The load and the resistance are considered to be normally distributed and are each characterized by a mean and a standard deviation. A first order reliability method was used to measure and account for any system redundancy in the foundation system by predicting a probability of failure based on the load and resistance of the system. Based on the most heavily loaded pile and the analysis performed on the 2-pile and 4-pile foundation systems, the probabilities of failure are lower for the system than for the single pile. This would bring reason to state there is some measurable redundancy in the basic systems analyzed where these systems would once be considered to be non-redundant systems. / text

Fossil fuel is presently a major source for electricity production, but it contributes significantly to Green House Gas emissions. Wind is a promising alternative, and can potentially become a major power resource in future power systems. Wind power installations are growing significantly for producing clean energy in electric power systems. As the wind penetration continues to increase to relatively high levels, it can significantly affect the overall performance and reliability of the power system. Hence, it becomes very important to accurately model the behaviour of wind, its interaction with conventional sources and also with other wind resources connected to the power system in order to conduct a realistic assessment of system reliability and benefits from wind energy utilization. When the wind penetration levels are low, all the wind energy generated is utilized to serve the load. However, at higher wind penetration levels, wind energy is spilled due to limitations in the operating reserve or ramping capability of the scheduled generating units. The system reliability and the wind energy benefits are reduced as the wind energy spillage increases due to wind curtailment. Hence, accurate wind models should be researched and developed to include wind energy curtailment in the reliability modelling, considering factors such as the system load level, priority loading order of the generating unit and response rates of the generating units. Researchers have not incorporated these factors in wind power modelling and in the adequacy evaluation of wind integrated power systems. A new analytical technique is developed in the subsequent chapters to carry out a comprehensive wind absorption capability evaluation of the power system, and also to incorporate this characteristic in reliability modelling of the system. Wind curtailments can take place not only due to generation constraints, but also due to transmission line constraints depending on the capacity and location of the wind energy resource in the power system, and the power transfer capacity of the transmission lines connected to the wind farm bus. Therefore, reliability modelling of the power system considering wind curtailments due to both generation and transmission constraints should be carried out to assess the impact of wind farms on bulk system reliability and the wind energy benefits. Wind curtailment is incorporated in the composite power system reliability evaluation by modelling the wind resource both as generation and as negative load. The techniques can be utilized to conduct system adequacy and wind energy benefit assessment both at the capacity planning stages and composite generation/transmission planning stages, incorporating wind power curtailment due to generating unit response limitations. As the wind penetration in a power system increases, the wind farms connected to the system are distributed at different geographical locations. Both analytical and Monte Carlo Simulation based techniques have previously been used by the research group at the University of Saskatchewan to include the cross correlation between the wind characteristics of different wind farms in the wind modelling for reliability evaluation of power systems. However, the combined effect of wind diversity and wind curtailments due to both transmission and generation constraints on the system reliability and wind energy benefit assessment has not been considered. The techniques developed for system adequacy and wind energy benefit assessment considering wind curtailment due to generation and transmission constraints are further modified and presented in this thesis to include wind diversity in the analysis. The developed techniques for adequacy evaluation of wind integrated power systems considering wind power curtailment and diversity should be extremely useful for system planning engineers and policy makers as wind power penetration in power systems continues to increase throughout the world.

Microelectronic systems such as cell phones, computers, consumer electronics, and implantable medical devices consist of subsystems which in turn consist of other subsystems and components. When such systems are designed, fabricated, assembled, and tested, they need to meet reliability, cost, performance, and other targets for being competitive. The design of reliable electronic packaging systems in a systematic and timely manner requires a consistent and unified method for allocating, predicting, and assessing reliability and for recommending design changes at the component and system level with consideration of both random and wearout failures. Accordingly, this dissertation presents a new unified knowledge modeling method for System Design for Reliability (SDfR) called the Reliability Object Model (ROM) method. The ROM method consistently addresses both reliability allocation and assessment for systems composed of series and parallel subsystems. The effectiveness of the ROM method has been demonstrated for allocating, predicting, and assessing reliability, and the results show that ROM is more effective compared to existing methods, providing richer semantics, unified techniques, and improved SDfR quality. Furthermore, this dissertation develops representative reliability metrics for random and wearout failures, and incorporates such metrics into ROM together with representative algorithms for allocation, assessment, and design change recommendations. Finally, this research implemented the ROM method in a computing framework and demonstrated its applicability using several relevant microelectronic system test cases and prototype SDfR tools.

There are presently worldwide targets for decreasing anthropogenic greenhouse gases (GHGs) emissions owing to global climate change concerns. Here in the United Kingdom, the government has committed to reduce its GHGs emissions by at least 80% by 2050 relative to 1990 levels. In order to achieve the ambitious 2050 targets and minimise cumulative emissions along the way, modern power systems are facing a series of great challenges. These challenges include extensive utilisation of renewable generation, diverse demand--side participation in power system operation and planning, as well as considerable application of emerging smart devices and appliances. All of these challenges will significantly increase the complexity of future power systems in terms of both operation and design. Regardless, the primary objective of power systems remains the same. That is the system must supply all the customers (responsive ones and non-responsive ones) with electricity as economically as possible and with an adequate level of continuity and quality. With the widespread utilisation of smart meters and appliances as well as the large-scale installation of different storage technologies, the services that demand response (DR) and electrical energy storage (EES) resources can provide will cover a wide range of ancillary services. More importantly, the grid-scale penetration of DR and EES resources is able to provide energy management and capacity support services, which can be considered as the alternative to generation resources. In this light, considerable amount of research has been done focusing on engaging particular types of electricity users with different kinds of incentives and/or tariff schemes, so that the economic benefits to both utilities and customers are optimised. However, no general framework for systematic assessment of the contribution to power system adequacy of supply from potential grid-scale penetration of DR and EES resources is available up till now, particularly taking specific consideration of DR's flexibility and payback characteristics as well as EES's operational parameters. The research work in this thesis therefore emphasises exclusively on the potential of grid-scale DR and EES resources to serve as alternative resources to electricity generation within the context of power system adequacy of supply. More specifically, based on literature survey of existing studies in similar topics, this thesis has made some substantial contributions and innovations, such as developing novel models of these emerging demand-side resources, implementing a systematic adequacy of supply assessment with new aspect to measure the level of adequacy of supply (new indices), proposing a novel and comprehensive framework for evaluation of the capacity credit of DR and EES, and analysing the economic value based on power system fundamental long--term costs of interruption and supply. Ultimately, this thesis has established a comprehensive framework for assessment of the contribution of DR and EES to power system adequacy of supply. Additionally, the numerical studies carried out in this thesis have enabled the inference of general adequacy of supply implications in terms of deploying DR and EES resources to provide capacity support to power systems.

abstract: A quantitative analysis of a system that has a complex reliability structure always involves considerable challenges. This dissertation mainly addresses uncertainty in- herent in complicated reliability structures that may cause unexpected and undesired results. The reliability structure uncertainty cannot be handled by the traditional relia- bility analysis tools such as Fault Tree and Reliability Block Diagram due to their deterministic Boolean logic. Therefore, I employ Bayesian network that provides a flexible modeling method for building a multivariate distribution. By representing a system reliability structure as a joint distribution, the uncertainty and correlations existing between system’s elements can effectively be modeled in a probabilistic man- ner. This dissertation focuses on analyzing system reliability for the entire system life cycle, particularly, production stage and early design stages. In production stage, the research investigates a system that is continuously mon- itored by on-board sensors. With modeling the complex reliability structure by Bayesian network integrated with various stochastic processes, I propose several methodologies that evaluate system reliability on real-time basis and optimize main- tenance schedules. In early design stages, the research aims to predict system reliability based on the current system design and to improve the design if necessary. The three main challenges in this research are: 1) the lack of field failure data, 2) the complex reliability structure and 3) how to effectively improve the design. To tackle the difficulties, I present several modeling approaches using Bayesian inference and nonparametric Bayesian network where the system is explicitly analyzed through the sensitivity analysis. In addition, this modeling approach is enhanced by incorporating a temporal dimension. However, the nonparametric Bayesian network approach generally accompanies with high computational efforts, especially, when a complex and large system is modeled. To alleviate this computational burden, I also suggest to building a surrogate model with quantile regression. In summary, this dissertation studies and explores the use of Bayesian network in analyzing complex systems. All proposed methodologies are demonstrated by case studies. / Dissertation/Thesis / Doctoral Dissertation Industrial Engineering 2018

Production Line Systems (PLS) are ubiquitous in today’s manufacturing industry. The need for enhanced efficiencies and higher throughput in such systems has increased their complexity and size that has made performance analysis challenging for practitioners. This thesis introduces a novel approach based on Product Service System (PSS) design and analysis which simplifies Production Line System study and identifies opportunities for performance improvement that can be quantified based on the hardware and maintenance system performance. The approach involves modelling and simulation techniques based on reliability engineering principles and systems thinking. In order to apply the principles of PSS approach to PLS, it is essential to draw a comparison between PLS and PSS, so as to take account of the differences while applying the new approach; a literature review has been carried out on PSS Design and Analysis, that identified the state of the art modelling and simulation techniques in PSS. Additionally, a separate literature review on maintenance system and production line was carried out. This enabled the PSS methodology to be applied to PLS by incorporating the differences. A systems thinking approach has been employed to create the static simulation model of the integrated production line system by means of schematic representations. Key improvement areas, identified from the static simulation model have been modelled dynamically to incorporate the stochastic behaviour of the system. All the dynamic models are developed using a Discrete Event Simulation platform. These models were supported by Monte Carlo Simulation, queuing principles, probabilistical and statistical methods pertinent to reliability engineering. The novel integrated simulation model consists of a production line model and a maintenance system model. The production line model simulates two types of failures in addition to the outages in the system: breakdowns and short stops. The maintenance system model simulates the maintenance actions in the production line by considering the resources availability, repair time, and resources travelling time amongst others. In addition, the maintenance model is capable of optimizing the preventive maintenance interval for maintainable failures for cost, availability and criticality while taking into account the maintenance effectiveness value from the failure data. The simulation model is validated using an industrial case study which consists of a large production line for beer. Sensitivity studies on the simulation model enabled the case study company to focus on strategies for throughput improvement by improving the reliability and maintainability, optimal resources allocation and maintenance interval optimization in targeted areas in the large and complex system. The model developed is generic and can easily be applied to analyse other industrial production line systems. It can also be used as a design tool for new production lines.

Customers expect electricity to be not only available but also affordable whenever they need it. Due to the stochastic nature of power system component failure, the management of power interruption is challenging. Although the reliability of supply can usually be increased by employing redundant equipment; this means that affordability is compromised. At present, many power utilities have a considerable amount of aged equipment in their networks. Although they have already started replacement planning, the price control schemes imposed by regulatory authorities constrain their capital expenditure budget. This thesis has studied the influence of the end-of-life failure of power transformers on transmission system reliability in order to make decisions on their replacement. Power transformers are selected for the analysis because they are technically complex, expensive, and main feed points of electricity for end users. In addition, surveys on ageing asset show that 50% of transformer populations, in many utilities, have been classified as old since the year 2008. The focus of these reliability analyses is to identify the most critical transformers and to establish a reliability based replacement framework. Modelling of end-of-life failure was reviewed, and the state-of-the-art method of its incorporation into system reliability was adopted. A reliability assessment tool within DIgSILENT PowerFactory package was developed in order to perform reliability studies. This thesis has four original contributions surrounding transmission system reliability analysis. The first contribution is the development of a cost-effective framework that concerns the application of reliability studies on asset replacement decision making. The developed framework has employed reliability importance measures, the Pareto analysis and economic comparison based on reliability incentive/penalty schemes. All the three elements of the framework are original applications to system reliability area. The second contribution is the integration of unconventional end-of-life failure models into system reliability. The unconventional model used in this study is Arrhenius-Weibull distribution, which characterises end-of-life failure under different loading conditions. This study has evaluated the added value provided by including loading levels in failure models and how this enhances the understanding of the effect of operational factors on system reliability. The third contribution is the investigation of dependent failure of power transformers caused by thermal stress. This investigation has led to the development of two probabilistic indicators to rank power transformer based on their criticality to multiple failure events. These new indicators have related the transformer reliability to its age and loading levels. In the fourth contribution, comprehensive studies of the effect of uncertainty associated with failure model parameters were performed. The first study has established bases for a system related approach for refining failure models. The approach is based on assessing the sensitivity of the system reliability or the system reliability applications to the uncertainty in failure model parameters. In the second study, two quantification methods were adopted to propagate the uncertainty in failure model parameters to system reliability indices. These are the second order probability and evidence theory. The last uncertainty study has described the use of sampling based sensitivity analysis to identify the most critical transformers and their area of vulnerability. Studies throughout the thesis have been performed on a realistic transmission network and the IEEE Reliability Test System.

Building a sustainable structure in soil or rock that satisfies all predefined technical requirements implies choosing a rational and effective construction method. An important aspect is how the performance of the structure is verified. For cases when the geotechnical behaviour is hard to predict, the existing design code for geotechnical structures, Eurocode 7, suggests the so-called “observational method” to verify that the performance is acceptable. The basic principle of the method is to accept predefined changes in the design during construction, in order to accommodate the actual ground conditions, if the current design is found unsuitable. Even though this in theory should ensure an effective design solution, formal application of the observational method is rare. It is therefore not clear which prerequisites and circumstances that must be present for the observational method to be applicable and be the more suitable method. This licentiate thesis gives a critical review of the observational method, based on, and therefore limited by, the outcome of the performed case studies. The aim is to identify and highlight the crucial aspects that make the observational method difficult to apply, thereby providing a basis for research towards a more applicable definition of the method. The main topics of discussion are (1) the apparent contradiction between the preference for advanced probabilistic calculation methods to solve complex design problems and sound, qualitative engineering judgement, (2) the limitations of measurement data in assessing the safety of a structure, (3) the fact that currently, no safety margin is required for the completed structure when the observational method is applied, and (4) the rigidity of the current definition of the observational method and the implications of deviations from its principles. Based on the review, it is argued that the observational method can be improved by linking it to a probabilistic framework. To be applicable, the method should be supported by guidelines that explain and exemplify how to make the best use of it. The engineering judgement is however not lost; no matter how elaborate probabilistic methods are used, sound judgement is still needed to define the problem correctly. How to define such a probabilistic framework is an urgent topic for future research, because this also addresses the concerns regarding safety that is raised in the other topics of discussion. / För att i berg eller jord kunna konstruera en anläggning, som uppfyller satta tekniska krav, krävs det att man väljer en rationell och effektiv konstruktionsmetod. En viktig aspekt i detta val är hur man verifierar konstruktionens funktion avseende exempelvis bärförmåga eller stadga. För fall när konstruktionens beteende svårt att förutsäga, erbjuder gällande standard (Eurokod 7) den så kallade observationsmetoden. Denna metod tillåter i förväg förberedda förändringar i designen under konstruktionstiden, om observationer av konstruktionens beteende indikerar att så behövs. På så vis anpassas konstruktionen till de faktiska förhållandena i marken. Trots att detta tillvägagångssätt i teorin borde ge en rationell design, används metoden sällan. Det råder därför oklarheter om vilka förutsättningar och omständigheter som krävs för att observationsmetoden ska kunna användas och dessutom utgöra den bästa lösningen. I denna licentiatuppsats granskas observationsmetoden och dess användbarhet. Målet med licentiatuppsatsen är att belysa de aspekter som kan utgöra svårigheter när observationsmetoden används. Dessa identifierades under arbetet med några fallstudier. Licentiatuppsatsen ger därmed en utgångspunkt för fortsatt forskning för att ta fram en mer användbar definition av observationsmetoden. De viktigaste aspekterna som diskuteras i uppsatsen är (1) den skenbara motsatsen mellan användandet av sannolikhetsbaserade beräkningsmetoder för att lösa komplexa dimensioneringsfrågor och kvalitativa ingenjörsmässiga bedömningar, (2) de begränsningar som finns när man använder mätdata för att utvärdera konstruktioners säkerhet, (3) att det för tillfället saknas krav på säkerhetsmarginal mot brott för konstruktioner som byggts med observationsmetoden, och (4) vad svårigheten att uppfylla Eurokodens strikta definition innebär för metodens användbarhet. Utifrån resultatet av granskningen dras slutsatsen att observationsmetoden kan förbättras genom att ge den ett sannolikhetsbaserat ramverk. För att förenkla användningen bör riktlinjer och anvisningar utformas. Även om metoden utvecklas mot en högre grad av beräkningskomplexitet, kommer ingenjörsmässiga bedömningar också framgent att vara viktiga, eftersom en avgörande aspekt är hur problemställningen formuleras. Med ett sannolikhetsbaserat ramverk ökar möjligheten att lösa de frågeställningar kring säkerhet som också diskuteras i uppsatsen. / <p>QC 20140415</p>

Low volume high value (LVHV) supply chains such as airline manufacturing, power plant construction, and shipbuilding are especially susceptible to risks. These industries are characterized by long lead times and a limited number of suppliers that have both the technical know-how and manufacturing capabilities to deliver the requisite goods and services. Disruptions within the supply chain are common and can cause significant and costly delays. Although supply chain risk management and supply chain reliability are topics that have been studied extensively, most research in these areas focus on high vol- ume supply chains and few studies proactively identify risks. In this research, we develop methodologies to proactively and quantitatively identify and mitigate supply chain risks within LVHV supply chains. First, we propose a framework to model the supply chain system using fault-tree analysis based on the bill of material of the product being sourced. Next, we put forward a set of mathematical optimization models to proactively identify, mitigate, and resource at-risk suppliers in a LVHV supply chain with consideration for a firm’s budgetary constraints. Lastly, we propose a machine learning methodology to quan- tify the risk of an individual procurement using multiple logistic regression and industry available data, which can be used as the primary input to the fault tree when analyzing overall supply chain system risk. Altogether, the novel approaches proposed within this dissertation provide a set of tools for industry practitioners to predict supply chain risks, optimally choose which risks to mitigate, and make better informed decisions with respect to supplier selection and risk mitigation while avoiding costly delays due to disruptions in LVHV supply chains.