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Marine renewable energy has the potential to form a key part of the renewable electricity generation arsenal that is required for society to prevent catastrophic global warming. Device reliability is one of the great engineering hurdles facing the sector; operating cost effectively in the harsh open ocean is very challenging. The nascent stage of the sector means that accurate reliability assessment of devices is difficult because of a lack of relevant data, uncertainty about loading conditions and a lack of consensus on device designs and architectures. This research develops and applies a suite of reliability prediction methods for use across the tidal turbine development life cycle. A standards based statistical approach is developed for the assessment of the entire turbine; the pitch system is found to have a particularly high failure rate. An empirical approach is developed and applied on a pitch system to allow for the relevant reliability influencing design parameters to be uncovered. A Bayesian framework is developed to enable new information to be incorporated into existing models; this is of particular interest to nascent technologies. Finally a Physics of Failure approach is developed for the pitch system roller bearing unit; measured flow data is used to predict the fatigue life and reliability via a hydrodynamic model. The methods presented along with the results will assist reliability assessment and prediction during tidal turbine design and early operation. Improved reliability assessment at each stage of turbine development can hopefully assist the industry to cross the 'valley of death' and become commercially viable.

The restructuring of the electricity sector forces utilities to operate their existing power networks with increasing flexibility and efficiency. Alternative cable rating methods, such as emergency and dynamic ratings, improve the utilisation of existing network cables, and provide cable-based networks with additional flexibility and resilience against uncertainties. However, such approaches may pose more stress on network cables and increase the risk of thermal overloading and ageing. This thesis advances the existing power system reliability assessment methodology by integrating a detailed cable system thermal-ageing model that considers cable design properties and environmental conditions into a traditional sequential Monte Carlo simulation. The main outcome of this integration was the comprehensive probabilistic evaluation of cable networks' reliability, together with each individual cable's thermal performance and ageing risk. The methodological framework integrating cable design and ageing provides operators with the additional tools to identify key network cables based on a quantified ageing index in order to perform optimised cable replacement and maintenance strategies. The framework has also helped utilities and operators to propose alternative cable operating practices to increase the loadability and flexibility of cable-based networks for better network performance, but also to avoid excessive risk of cable ageing. The methodological framework was applied on four test networks for four specific case studies. The first study presented an innovative methodology to capture the additional risk of cable failure and ageing when cables are operated beyond their limits. The second study further quantified the network reliability benefits and cable ageing risks when extended durations and increased ampacity of the emergency ratings are implemented. These two studies will inform the utilities of the benefits and risks of implementing emergency rating of UGCs and to help them to make decisions related to optimising their operational flexibility. The third study proposed an alternative flexible cable current rating method to optimise the loading capability of existing cable tie lines between wind farms and host utilities. It allows the wind farm developers to increase their generation capacities without upgrading their existing cable tie circuits and risking the cable lifetime. The last study evaluated the benefits and potential risks of emergency ratings of UGCs could give in a distribution cable network with EV connections. The proposed emergency rating approach enables for a higher penetration level of EVs while an acceptable level of cable ageing is generated.

Currently, the power network lacks vast communication capabilities, whereas the smart grid infrastructure relies on smart sensors and sophisticated communication technologies to operate. In addition, traditional power systems may not only require changes in order to upgrade their capacity, but they will require a considerable amount of instrumentation to monitor and control the reliable operation of the power system. Thus, the intelligent monitoring and control allowed by modern Information and Communication Technologies (ICT) have become of great importance in terms of achieving the fundamental purpose of the smart grid. However, failures of ICT can, therefore, affect the Grid's reliability, so quantifying the risk involved with ICT failures, their functionality and implementation should be more specifically defined; this is primarily due to the vast diversity of ICT applications. To this end, the main objective of this thesis is to identify the impact of ICT failures using two different approaches within the smart grid. (1) Through ICT component unavailability and (2) through ICT inaccurate measurements. The inclusion of ICT within the power network is employed, within the implementation of Flexible AC Transmission Systems (i.e. FACTS-ICT) and Time-Varying Line Rating (i.e. TVLR-ICT) systems (often referred to as dynamic line rating equipment) to maximise the ampacities of the existing transmission and distribution networks, by increasing their power transfer capabilities. In particular, this thesis is based on a modified sequential Monte Carlo (SMC) modelling approach that integrates the TVLR-ICT (indirect and direct systems) and FACTS-ICT capabilities within the overhead lines and substations of the network operating states. This approach allows quantification of the increased network risk associated with such implementation. The proposed method is implemented on the IEEE 14-Bus system, which represents a distribution network, and IEEE RTS-96 which provides a transmission network, equivalent to multiple types of generation connected to it. Both networks are further modified to permit the implementation of both smart grid technologies. New performance indices are developed to evaluate the contribution of smart technologies to power network reliability, as well as to capture the stochastic nature of component failures following chronological trends. The reliability studies show that the impact of ICT failures on network reliability is mainly related to quality, and therefore the performance of the ICT implemented (i.e. ICT reliability). When FACTS and TVLR systems possess an identical level of ICT reliability, FACTS-ICT failures on transmission networks provoke twice as much increase in expected energy not supplied (EENS) than TVLR-ICT failures for a level of network loading up to 2.0pu (evenly increased in active and reactive power for generation and demand). This is contrary to the case of the distribution network, which is significantly more affected by TVLR-ICT failures, causing more than five times EENS in a network operating up to 1.5pu. Nevertheless, the risks and benefits of FACTS and TVLR smart technologies along with their ICT performances on power network reliability are investigated and quantified, and therefore the effect of their ICT failures can be better controlled and alleviated, shaping utility decisions about future options towards increased network adequacy and flexibility.

Offshore oil and gas exploitation is principally conducted using dry or wet tree systems, otherwise called the subsea Xmas tree system. Due to the shift to deeper waters, subsea production system (SPS) has come to be a preferred technology with attendant economic benefits. At the centre of the SPS is the subsea control module (SCM), responsible for the proper functioning and monitoring of the entire system. With increasing search for hydrocarbons in deep and ultra-deepwaters, the SCM system faces important environmental, safety and reliability challenges and little research has been done in this area. Analysis of the SCM reliability then becomes very fundamental due to the huge cost associated with failure. Several tools are available for this analysis, but the FMECA stands out due to its ability to not only provide failure data, but also showcase the system’s failure modes and mechanisms associated with the subsystems and components being evaluated. However, the technique has been heavily challenged in various literatures for several reasons. To close this gap, a novel multi-criteria approach is developed for the analysis and ranking of the SCM failures modes. This research specifically focusses on subsea tree-mounted electro-hydraulic (E-H) SCM responsible for the underwater control of oil and gas production. A risk identification of the subsea control module is conducted using industry experts. This is followed by a comprehensive component based FMECA analysis of the SCM conducted with the conventional RPN technique, which reveals the most critical failure modes for the SCM. A novel framework is developed using multi-criteria fuzzy TOPSIS methodology and applied to the most critical failure modes obtained from the FMECA evaluation using unconventional parameters. Finally, a validation of these results is performed using a stochastic input evaluation and SCM failure data obtained from the offshore industry standard reliability database, OREDA.

Structural Reliability treats uncertainties in structural design systematically, evaluating the levels of safety and serviceability of structures. During the past decades, it has been established as a valuable design tool for the description of the performance of structures, and lately stands as a basis in the background of the most of the modern design standards, aiming to achieve a uniform behaviour within a class of structures. Several methods have been proposed for the estimation of structural reliability, both deterministic (FORM and SORM) and stochastic (Monte Carlo Simulation etc) in nature. Offshore structures should resist complicated and, in most cases, combined environmental phenomena of greatly uncertain magnitude (eg. wind, wave, current, operational loads etc). Failure mechanisms of structural systems and components are expressed through limit state functions, which distinguish a failure and a safe region of operation. For a jacket offshore structure, which comprises of multiple tubular members interconnected in a three dimensional truss configuration, the limit state function should link the actual load or load combination acting on it locally, to the response of each structural member. Cont/d.

For many problems, especially nonlinear systems, the reliability assessment must be done in the time domain. Monte-Carlo simulation (MCS) can accurately assess the reliability of the system. However, its computational cost is highly expensive for the complex dynamic system. Importance Sampling (IS) method is a more efficient method than standard MCS for the reliability assessment of a system. It has been applied to dynamic systems when the excitation is defined by a Power Spectral Density (PSD) function. The central idea of the IS method is about generating sample time histories using a sampling PSD and introducing the likelihood ratio to each replication to give the unbiased estimator of the probability of failure. Another more efficient method than MCS for the reliability assessment of the dynamic system is the Separable Monte-Carlo (SMC) method. However, this method has been applied to linear dynamic systems as following. It starts with the step of drawing frequencies from PSD of excitation, calculation of system responses to each frequency, and storing them in a database. Then the stored frequencies and the respective responses are chosen randomly with the replacement for each replication to find the system response to the linear combination of the respective sinusoidal functions. Therefore, SMC can assess the reliability of the system with a proper database. The size of the database would depend on the shape of the PSD function and the complexity of the system. This research proposed a new method by combining IS with SMC to assess the reliability of linear dynamic systems. In this method, the database of the proposed method formed by using a sampling PSD is used to estimate the reliability of the system for the true spectrum The proposed method is more efficient than both IS or SMC methods individually in terms of both computational time and accuracy. The proposed method is demonstrated using a 10-bar truss.

High-Mast Illumination Poles (HMIPs) are tall and slender poles that provide lighting around highway interchanges and large open public spaces and arenas. Recent collapses have raised safety concerns about these sizable illumination poles. Investigations concerned with such failures have sometimes revealed the existence of severe fatigue-induced cracks at the shaft-to-base connection at the bottom of the poles. These cracks are believed to exist due to the galvanization process prior to deployment. Also, an unanticipated increase of fatigue demand from vortex-induced vibration is believed to exacerbate the failure process. Although no fatigue-related collapses have been reported in Texas, the Texas Department of Transportation (TxDOT) initiated a series of studies directed toward assessing the reliability of HMIPs in the state. One area of interest in these studies is to characterize the fatigue resistance of pre-cracked poles, that were designed in accordance with current specifications. A second area of interest is to characterize typical fatigue loading of HMIPs resulting from wind excitation. Fatigue experiments on the bottom portion of newly made and galvanized HMIPs are conducted to characterize the S-N curve that describes the fatigue resistance of the tested subject. The S-N curve is specified by parameters estimated from experimental stress ranges employed and associated cycles to failure. S-N curves for pre-cracked HMIPs are compared with those specified for uncracked HMIPs. The fatigue demand due to wind excitation is evaluated using five monitored HMIPs throughout Texas. Stress range data collected are analyzed for their equivalent fatigue load (EFL) to describe the fatigue demand under various wind loading environments. The comprehensive information collected from the field provides an assessment of the expected fatigue demand, on each HMIP studied at the various sites, for different wind speeds and directions. These data are utilized with site-specific wind rose information to estimate the fatigue reliability of HMIPs or, effectively, the probability of safe performance over the planned service life. In principle, even for sites not instrumented, HMIPs can be evaluated using site-specific wind data together with the fatigue demand for different wind speeds and directions derived from the five field tests. Results from the reliability analyses are used to propose inspection schedules for in-service HMIPs with a view toward optimizing costs while maintaining safety and acknowledging fatigue damage accumulation.

Abstract: Alberta’s surface mining sector is one of the largest energy-consuming industries in Canada, and so oil sands mining equipment performance has a significant impact on the economy. To achieve more sustainable oil sands mining production in Alberta, one of the influential factors is the improvement of the reliability of mining equipment. Through these reliability improvements, costs, energy consumption, and greenhouse gas (GHG) emissions can be mitigated. Energy consumption and equipment reliability have considerable risk associated with some main subsystems, and this risk must be determined in order to calculate the effect on expected operating cost. When mining equipment reliability improves, not only can costs associated with maintenance be reduced, but also energy consumption. As well, emissions quality can be improved through better maintenance, which in turn mitigates GHG emissions. The objective of this research is to develop a demand tree, the reliability modeling for oil sands mining equipment, and make a link between energy consumption and reliability. To determine how much energy, cost, and GHG emissions can be reduced through improving equipment reliability, techniques of equipment risk assessment and reliability were studied. In addition, “reference scenarios” for improving the reliability in mining equipment were identified and developed. A probabilistic Bayesian belief network (BBN) method was used for the reliability analysis. The integrated energy-reliability (E-R) model developed for oil sands mining equipment provides a detailed reliability-energy analysis. This model helps to understand the relationship between energy and reliability, and clarifies the amount of energy consumption and energy saving possible through improving the reliability of equipment. The E-R model was developed for four discrete states of reliability: State 1, the mining equipment is fully operational (reliability equals 1); State 2, the equipment operates under expected reliability (as defined by manufacturer); State 3, the equipment operates under low or limited reliability (also known as partial reliability); and State 4, the equipment fails. Partial reliability was calculated for the major subsystems of the mining equipment used in surface mining of bituminous sands, and their associated energy consumption, based on the Markov degraded multi-state model under three states, which are described as: State 1, the system operates under expected reliability; State 2, the system operates under low or limited reliability; this is also known as partial reliability; and State 3, the system fails. LEAP software was used to calculate final energy consumption by each main subsystem for the study period of forty years. It was assumed that the emissions changed only due to change in energy consumption, although partially reliable equipment may have higher specific emissions as well. The E-R model outcomes suggest that energy demand for equipment at current production rates will be reduced by an average of 603.5 million GJ, 1,151.40 million GJ, 1,125.53 million GJ, and 1,732.73 million GJ by year 2050 for states 1, 2, 3, and 4, respectively. Average annual as-spent cost savings of 60 Billion Canadian Dollars, 78 Billion Canadian Dollars, 99 Billion Canadian Dollars, and 158 Billion Canadian Dollars were obtained by year 2050 for operating in states 1, 2, 3, and 4. In addition, GHG emissions will be mitigated by an average of 27 million Metric Tons CO2 equivalent, 77 million Metric Tons CO2 equivalent, 75 million Metric Tons CO2 equivalent, and 105 million Metric Tons CO2 equivalent by year 2050 for states 1, 2, 3 and 4.

Reliability analysis has been in place for decades, and its results are important for proper planning and operation of utility companies. Engineers must be able to quantify the current reliability of a system, as well as its potential improvement facing different modifications, in order to make informed planning decisions. Meanwhile, system operation has its performance measured through yearly reliability indices. The base of this method of analysis is the failure rate of the system components. In the traditional method, this probability of failure is determined by the components' manufacturer and is considered to be constant. However, it is reasonable to assume that the operation of the system has an effect on the likelihood of random failures to happen to the components. This study proposes a different modeling of failure rate, taking the system state variables into consideration. The probability of having system voltages or currents beyond the acceptable limits is added to the random probability of failure. With this new consideration, an IEEE test system has seven of its reliability indices quantified for comparison. The inclusion of the newly modeled failure rate lead to a worsening of 11.07% in the indices, on average. A second analysis is performed considering a third scenario, with PV and wind based micro sources present in the microgrid system, and an improvement of 0.71% on the indices is noticed, compared to the second scenario. Finally, the effects of storage systems in the microgrid are investigated through a fourth scenario, in which two 2MWh battery systems are introduced, and an improvement of 3.05% is noticed in the reliability indices.

Surrogate models are commonly used in place of prohibitively expensive computational models to drive iterative procedures necessary for engineering design and analysis such as global optimization. Additionally, surrogate modeling has been applied to reliability based design optimization which constrains designs to those which provide a satisfactory reliability against failure considering system parameter uncertainties. Through surrogate modeling the analysis time is significantly reduced when the total number of evaluated samples upon which the final model is built is less than the number which would have otherwise been required using the expensive model directly with the analysis algorithm. Too few samples will provide an inaccurate approximation while too many will add redundant information to an already sufficiently accurate region. With the prediction error having an impact on the overall uncertainty present in the optimal solution, care must be taken to only evaluate samples which decrease solution uncertainty rather than prediction uncertainty over the entire design domain. This work proposes a numerical approach to the surrogate based optimization and reliability assessment problem using solution confidence as the primary algorithm termination criterion. The surrogate uncertainty information provided is used to construct multiple distributed surrogates which represent individual realizations of a lager surrogate population designated by the initial approximation. When globally optimized upon, these distributed surrogates yield a solution distribution quantifying the confidence one can have in the optimal solution based on current surrogate uncertainty. Furthermore, the solution distribution provides insight for the placement of supplemental sample evaluations when solution confidence is insufficient. Numerical case studies are presented for comparison of the proposed methodology with existing methods for surrogate based optimization, such as expected improvement from the Efficient Global Optimization algorithm.

Multi-Criteria Decision Making (MCDM) problems are often associated with tradeoffs between performances of the available alternative solutions under decision making criteria. These problems become more complex when performances are associated with uncertainty. This study proposes a stochastic MCDM procedure that can handle uncertainty in MCDM problems. The proposed method coverts a stochastic MCDM problem into many deterministic ones through a Monte-Carlo (MC) selection. Each deterministic problem is then solved using a range of MCDM methods and the ranking order of the alternatives is established for each deterministic MCDM. The final ranking of the alternatives can be determined based on winning probabilities and ranking distribution of the alternatives. Ranking probability distributions can help the decision-maker understand the risk associated with the overall ranking of the options. Therefore, the final selection of the best alternative can be affected by the risk tolerance of the decisionmakers. A Group Decision Support System (GDSS) is developed here with a user-friendly interface to facilitate the application of the proposed MC-MCDM approach in real-world multiparticipant decision making for an average user. The GDSS uses a range of decision making methods to increase the robustness of the decision analysis outputs and to help understand the sensitivity of the results to level of cooperation among the decision-makers. The decision analysis methods included in the GDSS are: 1) conventional MCDM methods (Maximin, Lexicographic, TOPSIS, SAW and Dominance), appropriate when there is a high cooperation level among the decision-makers; 2) social choice rules or voting methods (Condorcet Choice, Borda scoring, Plurality, Anti-Plurality, Median Voting, Hare System of voting, Majoritarian iii Compromise ,and Condorcet Practical), appropriate for cases with medium cooperation level among the decision-makers; and 3) Fallback Bargaining methods (Unanimity, Q-Approval and Fallback Bargaining with Impasse), appropriate for cases with non-cooperative decision-makers. To underline the utility of the proposed method and the developed GDSS in providing valuable insights into real-world hydro-environmental group decision making, the GDSS is applied to a benchmark example, namely the California‘s Sacramento-San Joaquin Delta decision making problem. The implications of GDSS‘ outputs (winning probabilities and ranking distributions) are discussed. Findings are compared with those of previous studies, which used other methods to solve this problem, to highlight the sensitivity of the results to the choice of decision analysis methods and/or different cooperation levels among the decision-makers