The objective of this work is to develop an effective TCAD based hot-carrier degradation model in predicting the damage that a SiGe HBT undergoes as it is stressed across bias, time and temperature.

Reliability assessment methods allow the evaluation of the reliability of systems and provide important information on how to improve a system‟s life to reduce risk and hazards. With the advancement in technology, the existing methods were extended and new methods were adopted. The advancement from mechanical to numerical and analog to digital system in many applications, and deregulation of energy sector brought the need to further modify the reliability analysis methods. The scope of this research is to demonstrate the advancement of the Dynamic Flowgraph Methodology (DFM) to reliability modeling of Smart Electrical Transmission System. The reason behind this is the successful operation of electric power under a deregulated electricity market depends on transmission system reliability management. Besides this, analog electro-mechanical systems in existing power system are aging and becoming obsolete. This thesis also illustrates how the electrical transmission system can be renovated into smart electrical transmission system and evaluates the reliability measures. / UOIT

<div>Electronics have now become a part of our daily life and therefore the reliability of microelectronics cannot be overlooked. As the Moore's law era comes to an end, various new system-level innovations (e.g., 3D packaging, evolution of packaging material to molding compounds) with constant scaling of transistors have resulted in increasingly complicated integrated circuits (ICs) configurations. The reliability modeling of complex ICs is a nontrivial concern for a variety of reasons. For example, ever since 2004, self-heating effect (SHE) has become an important reliability concern for ICs. Currently, many groups have developed thermal predictive models for transistors, circuits, and systems. In order to describe SHE self-consistently, the modeling framework must account for correlated self-heating within the ICs. This multi-scales nature of the self-consistency problem is one of the difficult factors poses an important challenge to self-consistent modeling. In addition, coupling between different physical effects within IC further complicates the problem.</div><div><br></div><div>In this thesis, we discuss three challenges, and their solutions related to an IC's reliability issues. We (i) generalize the classical effective medium theory (EMT) to account for anisotropic, heterogeneous system; (ii) develop computationally efficient a physics-based thermal compact model for a packaged ICs to predict junction temperature in the transistor based on the EMT model, and image charge theory. Our thermal compact model bridges different length scales among the sources and rest of the system. Finally (iii) propose the modeling framework of electrical chip package interaction (CPI) due to charge transport within mold compounds by coupling moisture diffusion, electric distribution, and ions transport. The proposed modeling framework not only addresses the three major modeling challenges discussed earlier, but also provides deep and fundamental insights regarding the performance and reliability of modern ICs. </div>

This research proposes an efficient reliability modeling and simulation methodology in power systems to include photovoltaic units, wind farms and storage. Energy losses by wake effect in a wind farm are incorporated. Using the wake model, wind shade, shear effect and wind direction are also reflected. For solar modules with titled surface, more accurate hourly photovoltaic power in a specific location is calculated with the physical specifications. There exists a certain level of correlation between renewable energy and load. This work uses clustering algorithms to consider those correlated variables. Different approaches are presented and applied to the composite power system, and compared with different scenarios using reliability analysis and simulation. To verify the results, reliability indices are compared with those from original data. As the penetration of renewables increases, the reliability issues will become more important because of the intermittent and non-dispatchable nature of these sources of power. Storage can provide the ability to regulate these fluctuations. The use of storage is investigated in this research. To determine the operating states and transition times of all turbines, Monte Carlo is used for system simulation in the thesis. A conventional power system from IEEE Reliability Test Systems is used with transmission line capacity, and wind and solar data are from National Climatic Data Center and National Renewal Energy Laboratory. The results show that the proposed technique is effective and efficient in practical applications for reliability analysis.

The work reported in this thesis is in the area of computational modeling of reliability of engineering structures. The emphasis of the study is on developing methods that are suitable for analysis of large-scale structures such as aircraft structure components. This class of problems continues to offer challenges to an analyst with the most difficult aspect of the analysis being the treatment of nonlinearity in the structural behavior, non-Gaussian nature of uncertainties and quantification of low levels of probability of failure (of the order of 10-5 or less), requiring significant computational effort. The present study covers static/ dynamic behavior, Gaussian/ non-Gaussian models of uncertainties, and (or) linear/ nonlinear structures. The novel elements in the study consist of two components: • application of modeling tools that already exists in the area of design and analysis of computer experiments, and . • application of data based extreme value analysis procedures that are available in the statistics literature. The first component of the work provides opportunity to combine space filling sampling strategies (which have promise for reducing variance of estimation) with kriging based modeling in reliability studies-an opportunity that has not been explored in the existing literature. The second component of the work exploits the virtues of limiting behavior of extremes of sequence of random variables with Monte Carlo simulations of structural response-a strategy for reliability modeling that has not been explored in the existing literature. The hope here is that failure events with probabilities of the order of 10-5 or less could be investigated with relatively less number of Monte Carlo runs. The study also brings out the issues related to combining the above sources of existing knowledge with finite element modeling of engineering structures, thereby leading to newer tools for structural reliability analysis. The thesis is organized into four chapters. The first chapter provides a review of literature that covers methods of reliability analysis and also the background literature on design and analysis of computer experiments and extreme value analysis. The problem of reliability analysis of randomly parametered, linear (or) nonlinear structures subjected to static and (or) dynamic loads is considered in Chapter 2. A deterministic finite element model for the structure to analyze sample realization of the structure is assumed to be available. The reliability analysis is carried out within the framework of response surface methods, which involves the construction of surrogate models for performance functions to be employed in reliability calculations. These surrogate models serve as models of models, and hence termed as meta-models, for structural behavior in the neighborhood of design point. This construction, in the present study, has involved combining space filling optimal Latin hypercube sampling and kriging models. Illustrative examples on numerical prediction of reliability of a ten-bay truss and a W-seal in an aircraft structure are presented. Limited Monte Carlo simulations are used to validate the approximate procedures developed. The reliability of nonlinear vibrating systems under stochastic excitations is investigated in Chapter 3 using a two-stage Monte Carlo simulation strategy. Systems subjected to Gaussian random excitation are considered for the study. It is assumed that the probability distribution of the maximum response in the steady state belongs to the basin of attraction of one of the classical asymptotic extreme value distributions. The first stage of the solution strategy consists of an objective selection of the form of the extreme value distribution based on hypothesis tests, and the next involves the estimation of parameters of the relevant extreme value distribution. Both these steps are implemented using data from limited Monte Carlo simulations of the system response. The proposed procedure is illustrated with examples of linear/nonlinear single-degree and multi-degree of freedom systems driven by random excitations. The predictions from the proposed method are compared with results from large-scale Monte Carlo simulations and also with classical analytical results, when available, from theory of out-crossing statistics. The method is further extended to cover reliability analysis of nonlinear dynamical systems with randomly varying system parameters. Here the methods of meta-modeling developed in Chapter 2 are extended to develop response surface models for parameters of underlying extreme value distributions. Numerical examples presented cover a host of low-dimensional dynamical systems and also the analysis of a wind turbine structure subjected to turbulent wind loads and undergoing large amplitude oscillations. A summary of contributions made along with a few suggestions for further research is presented in Chapter 4.

<p><p>In the operation, planning and design of the transmission system it is of greatest concern to quantify the reliability security margin to unwanted conditions. The deterministic N-1 criterion has traditionally provided this security margin to reduce the consequences of severe conditions such as widespread blackouts. However, a deterministic criterion does not include the likelihood of different outage events. Moreover, experience from blackouts shows, e.g. in Sweden-Denmark September 2003, that the outages were not captured by the N-1 criterion. The question addressed in this thesis is how this system security margin can be quantified with probabilistic methods. A quantitative measure provides one valuable input to the decision-making process of selecting e.g. system expansions alternatives and maintenance actions in the planning and design phases. It is also beneficial for the operators in the control room to assess the associated security margin of existing and future network conditions.</p><p>This thesis presents a method that assesses each component's risk to an insufficient transfer capability in the transmission system. This shows on each component's importance to the system security margin. It provides a systematic analysis and ranking of outage events' risk of overloading critical transfer sections (CTS) in the system. The severity of each critical event is quantified in a risk index based on the likelihood of the event and the consequence of the section's transmission capacity. This enables a comparison of the risk of a frequent outage event with small CTS consequences, with a rare event with large consequences.</p><p>The developed approach has been applied for the generally known Roy Billinton Test System (RBTS). The result shows that the ranking of the components is highly dependent on the substation modelling and the studied system load level.</p><p>With the restriction of only evaluating the risks to the transfer capability in a few CTSs, the method provides a quantitative ranking of the potential risks to the system security margin at different load levels. Consequently, the developed reliability based approach provides information which could improve the deterministic criterion for transmission system planning.</p></p>

The availability, or reliability, of an engineering component greatly influences the operational cost and safety characteristics of a modern system over its life-cycle. Until recently, the reliance on past empirical data has been the industry-standard practice to develop maintenance policies that provide the minimum level of system reliability. Because such empirically-derived policies are vulnerable to unforeseen or fast-changing external factors, recent advancements in the study of topic on maintenance, which is known as optimal maintenance problem, has gained considerable interest as a legitimate area of research. An extensive body of applicable work is available, ranging from those concerned with identifying maintenance policies aimed at providing required system availability at minimum possible cost, to topics on imperfect maintenance of multi-unit system under dependencies. Nonetheless, these existing mathematical approaches to solve for optimal maintenance policies must be treated with caution when considered for broader applications, as they are accompanied by specialized treatments to ease the mathematical derivation of unknown functions in both objective function and constraint for a given optimal maintenance problem. These unknown functions are defined as reliability measures in this thesis, and theses measures (e.g., expected number of failures, system renewal cycle, expected system up time, etc.) do not often lend themselves to possess closed-form formulas. It is thus quite common to impose simplifying assumptions on input probability distributions of components' lifetime or repair policies. Simplifying the complex structure of a multi-unit system to a k-out-of-n system by neglecting any sources of dependencies is another commonly practiced technique intended to increase the mathematical tractability of a particular model. This dissertation presents a proposal for an alternative methodology to solve optimal maintenance problems by aiming to achieve the same end-goals as Reliability Centered Maintenance (RCM). RCM was first introduced to the aircraft industry in an attempt to bridge the gap between the empirically-driven and theory-driven approaches to establishing optimal maintenance policies. Under RCM, qualitative processes that enable the prioritizing of functions based on the criticality and influence would be combined with mathematical modeling to obtain the optimal maintenance policies. Where this thesis work deviates from RCM is its proposal to directly apply quantitative processes to model the reliability measures in optimal maintenance problem. First, Monte Carlo (MC) simulation, in conjunction with a pre-determined Design of Experiments (DOE) table, can be used as a numerical means of obtaining the corresponding discrete simulated outcomes of the reliability measures based on the combination of decision variables (e.g., periodic preventive maintenance interval, trigger age for opportunistic maintenance, etc.). These discrete simulation results can then be regressed as Response Surface Equations (RSEs) with respect to the decision variables. Such an approach to represent the reliability measures with continuous surrogate functions (i.e., the RSEs) not only enables the application of the numerical optimization technique to solve for optimal maintenance policies, but also obviates the need to make mathematical assumptions or impose over-simplifications on the structure of a multi-unit system for the sake of mathematical tractability. The applicability of the proposed methodology to a real-world optimal maintenance problem is showcased through its application to a Time Limited Dispatch (TLD) of Full Authority Digital Engine Control (FADEC) system. In broader terms, this proof-of-concept exercise can be described as a constrained optimization problem, whose objective is to identify the optimal system inspection interval that guarantees a certain level of availability for a multi-unit system. A variety of reputable numerical techniques were used to model the problem as accurately as possible, including algorithms for the MC simulation, imperfect maintenance model from quasi renewal processes, repair time simulation, and state transition rules. Variance Reduction Techniques (VRTs) were also used in an effort to enhance MC simulation efficiency. After accurate MC simulation results are obtained, the RSEs are generated based on the goodness-of-fit measure to yield as parsimonious model as possible to construct the optimization problem. Under the assumption of constant failure rate for lifetime distributions, the inspection interval from the proposed methodology was found to be consistent with the one from the common approach used in industry that leverages Continuous Time Markov Chain (CTMC). While the latter does not consider maintenance cost settings, the proposed methodology enables an operator to consider different types of maintenance cost settings, e.g., inspection cost, system corrective maintenance cost, etc., to result in more flexible maintenance policies. When the proposed methodology was applied to the same TLD of FADEC example, but under the more generalized assumption of strictly Increasing Failure Rate (IFR) for lifetime distribution, it was shown to successfully capture component wear-out, as well as the economic dependencies among the system components.

The reliability of protection systems has emerged as an important topic because protection failures have critical influence on the reliability of power systems. The goal of this research is to develop novel approaches for modeling and analysis of the impact of protection system failures on power system reliability. It is shown that repairable and non-repairable assumptions make a remarkable difference in reliability modeling. A typical all-digital protection system architecture is modeled and numerically analyzed. If an all-digital protection system is indeed repairable but is modeled in a non-repairable manner for analysis, the calculated values of reliability indices could be grossly pessimistic. The smart grid is emerging with the penetration of information-age technologies and the development of the Special Protection System (SPS) will be greatly influenced. A conceptual all-digital SPS architecture is proposed for the future smart grid. Calculation of important reliability indices by the network reduction method and the Markov modeling method is illustrated in detail. Two different Markov models are proposed for reliability evaluation of the 2-out-of-3 voting gates structure in a generation rejection scheme. If the model with consideration of both detectable and undetectable logic gate failures is used as a benchmark, the simple model which only considers detectable failures will significantly overestimate the reliability of the 2-out-of-3 voting gates structure. The two types of protection failures, undesired-tripping mode and fail-to-operate mode are discussed. A complete Markov model for current-carrying components is established and its simplified form is then derived. The simplified model can appropriately describe the overall reliability situation of individual components under the circumstances of complex interactions between components due to protection failures. New concepts of the self-down state and the induced-down state are introduced and utilized to build up the composite unit model. Finally, a two-layer Markov model for power systems with protection failures is proposed. It can quantify the impact of protection failures on power system reliability. Using the developed methodology, we can see that the assumption of perfectly reliable protection can introduce errors in reliability evaluation of power systems.

In the operation, planning and design of the transmission system it is of greatest concern to quantify the reliability security margin to unwanted conditions. The deterministic N-1 criterion has traditionally provided this security margin to reduce the consequences of severe conditions such as widespread blackouts. However, a deterministic criterion does not include the likelihood of different outage events. Moreover, experience from blackouts shows, e.g. in Sweden-Denmark September 2003, that the outages were not captured by the N-1 criterion. The question addressed in this thesis is how this system security margin can be quantified with probabilistic methods. A quantitative measure provides one valuable input to the decision-making process of selecting e.g. system expansions alternatives and maintenance actions in the planning and design phases. It is also beneficial for the operators in the control room to assess the associated security margin of existing and future network conditions. This thesis presents a method that assesses each component's risk to an insufficient transfer capability in the transmission system. This shows on each component's importance to the system security margin. It provides a systematic analysis and ranking of outage events' risk of overloading critical transfer sections (CTS) in the system. The severity of each critical event is quantified in a risk index based on the likelihood of the event and the consequence of the section's transmission capacity. This enables a comparison of the risk of a frequent outage event with small CTS consequences, with a rare event with large consequences. The developed approach has been applied for the generally known Roy Billinton Test System (RBTS). The result shows that the ranking of the components is highly dependent on the substation modelling and the studied system load level. With the restriction of only evaluating the risks to the transfer capability in a few CTSs, the method provides a quantitative ranking of the potential risks to the system security margin at different load levels. Consequently, the developed reliability based approach provides information which could improve the deterministic criterion for transmission system planning.

Monte Carlo simulation techniques have emerged as widely accepted computing tools in tackling many problems in modern structural mechanics. Apart from developments in computational hardware, which have undoubtedly made simulation strategies practically feasible, the success of Monte Carlo simulations has also resulted equally significantly from the methodological developments aimed at controlling sampling variance of the Monte Carlo estimates. The study reported in the present thesis is aimed at developing and validating Monte Carlo simulation based approaches with inbuilt variance reduction capabilities to deal with problems of time variant reliability modeling, random vibration testing, and updating reliability models for statically/dynamically loaded instrumented structures. The relevant literature has been reviewed in Chapter 1. Time variant reliability analysis of randomly parametered and randomly driven non-linear vibrating systems has been tackled by combining two Monte Carlo variance reduction strategies into a single framework (Chapter 2). The first of these strategies is based on the application of the Girsanov transformation to account for the randomness in dynamic excitations and, the second approach is fashioned after the subset simulation method to deal with randomness in system parameters. A novel experimental test procedure to estimate the reliability of structural dynamical systems under excitations specified via random process models has been proposed (Chapter 3). The samples of random excitations to be used in the test are modified by the addition of an artificial control force. An unbiased estimator for the reliability is derived based on measured ensemble of responses under these modified inputs based on the tenets of Girsanov’s transformation. The study observes that an acceptable choice for the control force (that can reduce the sampling variance of the estimator) can be made solely based on experimental techniques. This permits the proposed procedure to be applied in the experimental study of time variant reliability of complex structural systems which are difficult to model mathematically. Illustrative example consists of a multi-axes shake table study on bending-torsion coupled, geometrically non-linear, five-storey frame under uni/bi-axial, non-stationary, random base excitation. The first order reliability method (FORM) and inverse FORM have been extended to handle the problem of updating reliability models for existing, statically loaded structures based on measured responses (Chapter 4). The proposed procedures are implemented by combining Matlab based reliability modules with finite element models residing on the Abaqus software. Numerical illustrations on linear and non-linear frames are presented. A solution strategy within the framework of Monte Carlo simulation based dynamic state estimation method and Girsanov’s transformation for variance reduction has been developed to tackle the problem of updating the reliability of instrumented structures based on measured response under random dynamic loading (Chapter 5). For linear Gaussian state space models, the solution is developed based on continuous version of the Kalman filter, while, for non-linear and (or) non-Gaussian state space models, bootstrap particle filters are adopted. Results from laboratory testing of an archetypal five storey bending-torsion coupled frame under seismic base motions form the basis of one of the illustrative examples. A set of three annexures contain details of numerical methods for discretizing Ito’s differential equations (Annexure 1), working of the Girsanov transformation through Kolmogorov’s equations (Annexure 2) and tools for interfacing Matlab and Abaqus codes (Annexure 3).