Yes / Bayesian Network (BN)-based methods are increasingly used in system reliability analysis. While BNs enable to perform multiple analyses based on a single model, the construction of robust BN models relies either on the conversion from other intermediate system model structures or direct analyst-led development based on experts input, both requiring significant human effort. This article proposes an architecture model-based approach for the direct generation of a BN model. Given the architectural model of a system, a systematic bottom-up approach is suggested, underpinned by failure behaviour models of components composed based on interaction models to create a system-level failure behaviour model. Interoperability and reusability of models are supported by a library of component failure models. The approach was illustrated with application to a case study of a steam boiler system. / The full text will be available at the end of the publisher's embargo: 18th Nov 2023

Although power system reliability analysis is a mature research area, there is a renewed interest in updating available network models and formulating improved reliability assessment procedures. The main driver of this interest is the current transition to a new flexible and actively controlled power supply system with a high penetration of distributed generation (DG) and energy storage (ES) technologies, wider implementation of demand-side management (DSM) and application of automated control, monitoring, protection and communication infrastructures. One of the aims of this new electricity supply network (’the smart grid’) is an improved reliability and power quality performance, realised through the delivery of an uninterrupted and high-quality supply of electrical energy. However, there is currently no integrated methodology to measure the effects of these changes on the overall system reliability performance. This PhD research aims to update the standard power system simulation engine with improved numerical software models offering new capabilities for the correct assessment of quality of supply in future electricity networks. The standard reliability analysis is extended to integrate some relevant power quality aspects, enabling the classification of short and long supply interruptions by the correct modelling of network protection and reconfiguration schemes. In addition, the work investigates the formulation and analysis of updated reliability indicators for a more accurate validation and benchmarking of both system and end-user performance. A detailed database with typical configurations and parameters of UK/European power systems is established, providing a set of generic models that can correctly represent actual distribution networks supplying a mix of residential, commercial and industrial demand for different load sectors. A general methodology for reducing system complexity by calculating both electrical and reliability equivalent models of LV and MV distribution networks is also presented. These equivalent models, based on the aggregation of individual component models, help to reduce calculation times while preserving the accuracy assessment of network’s reliability performance at bulk supply points. In addition, the aggregated counterparts (same and mixed-type) of different ’smart’ component models (DG, ES and DSM) are also included in the analysis, showing how their co-ordinated implementation and control could improve quality of supply. Conventional reliability assessment procedures are also extended in this thesis to include accurate reliability equivalent models, network contingency statistics, actual load profiles and empirical fault probability distributions, which are employed to assess the frequency and duration of interruptions in the supply system for different scenarios. Both analytical and probabilistic simulation techniques (Monte Carlo method) are developed to include up-to-date security of supply legislation, introducing a new methodology for calculating the standard set of indices reported annually to energy regulators.

Fire in building structures represents a risk both to life and property that cannot be fully eliminated. It is the aim of fire safety engineering to reduce this risk to an acceptable level through the application of scientific and engineering principles to evaluate the risk posed by fire and to determine the optimal set of protective measures. This is increasingly being achieved through performance-based design methods. Performance-based design sets out performance requirements, typically related to life safety and control of property losses, and the designer is free to choose the most suitable approach to meet these requirements. Accurate performance-based design requires the evaluation of the risks to a structure through the evaluation of the range of hazards that may occur and the resulting structural responses. The purpose of this research is to develop simplified methodologies for the reliability based design of steel framed structures in fire. These methodologies are incorporated into a software package, FireLab, which is intended to act as a tool for practicing engineers to aid in learning and applying performance-based design. FireLab is a Matlab based program that incorporates a number of different models for analysing the response of structural elements exposed to fire. It includes both deterministic and probabilistic analysis procedures. A range of simple fire models are presented for modelling compartment fires. A set of heat transfer processes are discussed for calculating the temperature distribution within common structural elements exposed to fire. A variety of structural models are discussed which may be used to model the effects of fire on a structure. An analytical model for the analysis of composite beams has been implemented in the software program. Interfaces between the software and 2 separate third party programs have also been created to allow for the analysis of composite beams using the finite element method. Analytical methods for the analysis of composite slabs under thermo-mechanical load have been implemented in the software. These methods account for the additional load carrying capacity that slabs have in fire due to the positive effects of tensile membrane action. A numerical analysis method for the vertical stability of structures subjected to multi-floor fires has been implemented using the direct stiffness method. This method uses an elastic 2nd order solution in order to check the stability of a column under the fire induced horizontal loads from sagging floors. These models of potential failure scenarios provide the basis for the probabilistic analysis methods. A variety of methods for reliability analysis are evaluated based on ease of use, accuracy and efficiency. A selection of these methods has been implemented in the software program. A selection of sample cases are examined in order to illustrate the procedures and to evaluate the important input variables. These methods provide the probability of failure of a structure under specific loads. The probability of failure is a useful parameter in comparing the level of safety between various design options. A more comprehensive framework is developed for the evaluation of the probable costs due to fire associated with a given design. This framework is based on an existing framework from earthquake engineering. It involves calculating the statistical spread of both the magnitude and likelihood of occurrence of fire and the resulting structural responses. The damage that occurs from the structural response may be then estimated. Finally, given the likely level of damage that will occur it is possible to estimate the cost of the damage either in terms of monetary cost of repair or downtime due to repair works. This method is applied to a variety of design options for a typical office building in order to illustrate the application of the framework.

The reliability of structures subjected to time-invariant or time-variant random loads is considered herein. This is an important field of engineering, as it provides the framework for assessing whether newly designed or existing structural systems meet their design requirements in a given lifetime, or whether they experience what is termed “structural failure”. An important aspect of reliability analysis is the study of structures subjected to multiple time-varying loads. For this class of systems, it is well-known that by modelling the loads as (time-variant) random processes, the reliability may be evaluated by considering the outcrossing of a vector process out of a safe domain. However, due to the possibility that the loads may not be fully-dependent, all loads may not necessarily contribute to structural failure. To account for this the treatment of vector-outcrossings may need to allow for the possibility of outcrossings being caused by individual loads, as distinct from combinations of all loads. The procedure used to analyse combinations of loads depends on the stochastic process model used to represent the loads. Two well-known load models have been presented in the literature—they are referred to herein as the ‘on-off’ model and the ‘standard’ model. The ‘on-off’ model typically assumes loads are non-negative, and are either ‘on’ (eg their value is non-zero) or ‘off’ (eg their value is strictly zero). They can contribute to failure only when they are ‘on’. This model is represented by a somewhat artificial ‘composite’ probability distribution, obtained by modifying the original load probability density function (pdf) so that a ‘finite’ non-zero probability represents explicitly the possibility that the load is ‘off’. To implement this model in time-variant analysis, it is necessary to consider all possible combinations of loads being ‘on’ and ‘off’. In contrast, the ‘standard’ model (which is the more commonly used) typically allows loads to be negative; it is also typically represented solely by the original load pdf, and therefore effectively assumes each load is always ‘on’. To allow for the possibility of one or more loads not to cause failure, herein the value of such loads is held ‘constant’ at the time of failure, when the value of all loads actually causing failure is allowed to change. Use of the ‘standard’ model is examined herein. The “Directional Simulation in the Load Space (DS-LS)” approach is a tool used to perform reliability analysis. It is particularly suitable for time-variant analysis, as it allows loads to be represented as random processes, and to be modelled properly. DS-LS has so far been shown to work well for relatively simple structures subjected to one or more time-invariant random loads, and has been used to examine vector outcrossings in systems comprising either discrete or continuous loads. To enable the proper consideration of load combinations, and to provide some improvements in the formulation of the technique, a generalisation of the DS-LS approach is proposed herein. The generalisation is achieved in two stages. The first involves modifying the time-invariant and time-variant DS-LS formulation to allow for the possibility of positioning the origin of DS-LS not only in the ‘safe’ region of the load space (which the formulation currently requires) but in the ‘failure’ region, or even ‘exactly’ on the boundary separating the safe and failure regions. The modifications are necessary because for even simple structures, the ‘exact’ location of the safe and failure region is not always known explicitly ‘a priori’. The second involves developing the time-variant DS-LS formulation to consider explicitly outcrossings caused by combinations of one or more loads, during analysis of systems comprising stationary continuous gaussian loads. To do this, the direction of the load process vector is ‘fixed’ at each point of outcrossing, to physically represent the particular combination of loads causing the outcrossing. By considering each possible load combination, all loads not causing an outcrossing are then held constant during radial integration, thereby modelling those that do not contribute to each outcrossing. The proposed formulation differs from most load combination analysis techniques (which, evidently, simplify the analysis) as it is analytically ‘exact’, and it considers explicitly all possible combinations of loads. The concepts and formulations proposed herein may provide further understanding of reliability analysis performed by DS-LS (or other techniques) and may aid their future development. / PhD Doctorate

To evaluate the strategy of reliability enhancement for an existing industrial power system , the Benefit-Cost ratio of all possible improvement scenarios have to be investigated . This thesis presents a quantitative and systematic method to solve the Benefit-Cost ratio of network restructure for reliability enhancement . This method can provide a simple and effective tool for planning reliability improvement in the industry power systems. Up to now , many methodologies have been developed to solve the service reliability for distribution power systems . In this thesis , the reliability indices of industrial power systems are calculated to evaluate the service quality . According to the result of reliability analysis and the corresponding cost of loss of load for each load bus , the annual power outage cost is derived for each scenario of system restructure. By integrating the power outage cost and the corresponding investment cost , the optimal reliability enhancement is determined by the best strategy with the Benefit-Cost ratio.

This dissertation addresses the modeling of wind turbine systems (WTS) and wind farms. The WTS reliability model provides the generation state space of a WTS. The generation states are derived from the combinations of the wind states from given wind data and the condition states of each component in a WTS. Wake effect is accounted when there are neighboring WTSs. The results of the reliability model of a WTS are associated with the generation states of the WTS, which include the probability, transition rates to other states, frequency of transitions to other states, and duration. The reliability model of the wind farm is derived by combining the wind states, WTS states and the distribution line states. The results of the reliability model of a wind farm are associated with the generation states of the wind farm, which include the probability, transition rates to other states, frequency to other states, and duration. The reliability model of the wind turbine system and the reliability model of the wind farm presented in this dissertation bring contribution to the planning and operation of bulk power systems with wind farm integration. The developed models can provide the system operator with clear reliability indices in terms of generation states of wind turbine systems and wind farms along with their probability, duration and frequency of transitions.

The steady downscaling of CMOS technology has led to the development of devices with nanometer dimensions. Contemporaneously, maturity in technologies such as chemical self-assembly and DNA scaffolding has influenced the rapid development of non-CMOS nanodevices including vertical carbon nanotube (CNT) transistors and molecular switches. One main problem in manufacturing defect-free nanodevices, both CMOS and non-CMOS, is the inherent variability in nanoscale fabrication processes. Compared to current CMOS devices, nanodevices are also more susceptible to signal noise and thermal perturbations. One approach for developing robust digital systems from such unreliable nanodevices is to introduce defect- and fault-tolerance at the architecture level. Structurally redundant architectures, reconfigurable architectures and architectures that are a hybrid of the previous two have been proposed as potential defect- and fault-tolerant nanoscale architectures. Hence, the design of reliable nanoscale digital systems will require detailed architectural exploration. In this dissertation, we develop probabilistic methodologies and CAD tools to expedite the exploration of defect- and fault-tolerant architectures. These methodologies and tools will provide nanoscale system designers with the capability to carry out trade-off analysis in terms of area, delay, redundancy and reliability. During execution, the next state of a digital system is only dependent on the present state and the digital signals propagate in discrete time. Hence, we have used Markov processes to analyze the reliability of nanoscale digital architectures. Discrete Time Markov Chains (DTMCs) have been used to analyze logic architectures and Markov Decision processes (MDPs) have been used to analyze memory architectures. Since structurally redundant and reconfigurable nanoarchitectures may consist of millions of nanodevices, we have applied state space partitioning techniques and Belief propagation to scale these techniques. We have developed three toolsets based on these Markovian techniques. One of these toolsets has been specifically developed for the architectural exploration of molecular logic systems. The toolset can generate defect maps for isolating defective nanodevices and provide capabilities to organize structurally redundant fault-tolerant architectures with the non-defective devices. Design trade-offs for each of these architectures can be computed in terms of signal delay, area, redundancy and reliability. Another tool called HMAN (Hybrid Memory Analyzer) has been developed for analyzing molecular memory systems. Besides analyzing reliability-redundancy trade-offs using MDPs, HMAN provides a very accurate redundancy-delay trade-off analysis using HSPICE. SETRA (Scalable, Extensible Tool for Reliability Analysis) has been specifically designed for analyzing nanoscale CMOS logic architectures with DTMCs. SETRA also integrates well with current industry-standard CAD tools. It has been shown that multimodal computational models capture the operation of emerging nanoscale devices such as vertical CNT transistors, instead of the bimodal Boolean computational model that has been used to understand the operation of current electronic devices. We have extended an existing multimodal computational model based on Markov Random Fields (MRFs) for analyzing structurally redundant and reconfigurable architectures. Hence, this dissertation develops multiple probabilistic methodologies and tools for performing nanoscale architectural exploration. It also looks at different defect- and fault-tolerant architectures and explores different nanotechnologies. / Ph. D.

The reliability analysis and reliability-based optimization of laminated circular cylinders under axial buckling instability are studied. Structural reliability is measured in terms of Hasofer-Lind reliability index. The response surface models are used in both the calculation of the reliability index and the reliability-based optimization. In reliability analysis, both deterministic and probabilistic sensitivity factors are investigated; the results show that the reliability index is most sensitive to the applied load and Young's modulus of the material. Two cases are considered in the optimization study. In the first case, the cylinder weight is minimized subject to a reliability constraint whereas in the second case, cylinder reliability is maximized subject to a weight constraint. In addition, two different optimization techniques are studied. In the first technique, a global response surface model of the buckling response based on 3000 Monte Carlo simulations is used for the design optimization whereas in the second technique, multiple local regression models, with each based on approximately 20 simulations, are used in sequential search of an optimum design. An optimum design is found. The results based on sequential application of multiple local regression models are close to those from global optimization while the former is much more efficient in terms of computational cost.

Reliability is a key aspect of power system design and planning. In this research we present a reliability analysis algorithm for large scale, radially operated (with respect to substation), reconfigurable, electrical distribution systems. The algorithm takes into account equipment power handling constraints and converges in a matter of seconds on systems containing thousands of components. Linked lists of segments are employed in obtaining the rapid convergence. A power flow calculation is used to check the power handling constraints. The application of distributed generators for electrical distribution systems is a new technology. The placement of distributed generation and its effects on reliability is investigated. Previous reliability calculations have been performed for static load models and inherently make the assumption that system reliability is independent of load. The study presented here evaluates improvement in reliability over a time varying load curve. Reliability indices for load points and the overall system have been developed. A new reliability index is proposed. The new index makes it easier to locate areas where reliability needs to be improved. The usefulness of this new index is demonstrated with numerical examples. / Master of Science

Due to limitation of economics and legislation, the power system is not allowed serious accident on modern social. In order to enhance system reliability, many types of special protection systems (SPS) have been implemented by utilities around the world. One of the main concerns in the design of an SPS is whether the designed system can achieve the reliability requirement. Currently, the literature that discusses the SPS reliability issue is scarce. In this thesis, a comparison of several techniques suitable for performing reliability assessment of SPS is presented. Discussed reliability models include using reliability block diagram, fault tree analysis, Markov modeling and Monte Carlo simulations. In order to understand the uncertainty effects of input data on the calculated system reliability, Monte Carlo Sampling method is utilized in this study to take the input parameters uncertainty into account in the system modeling. To deal with the problem of not being able to reach the reliability requirement after uncertainty analysis, a sensitivity analysis is proposed to analyze the importance of the components involved in the system. Sensitivity analysis can be used to identity the most effective component in the enhancement the SPS reliability. A Taipower SPS is used in this thesis to explain the proposed reliability assessment methods.