The Bayesian network (BN) is a powerful model for probabilistic knowledge representation and inference and is increasingly used in the field of reliability evaluation. This paper presents a ...bibliographic review of BNs that have been proposed for reliability evaluation in the last decades. Studies are classified from the perspective of the objects of reliability evaluation, i.e., hardware, structures, software, and humans. For each classification, the construction and validation of a BN-based reliability model are emphasized. The general procedural steps for BN-based reliability evaluation, including BN structure modeling, BN parameter modeling, BN inference, and model verification and validation, are investigated. Current gaps and challenges in reliability evaluation with BNs are explored, and a few upcoming research directions that are of interest to reliability researchers are identified.
Reliability analysis is an important tool for assisting the design phase of a power electronic converter to fulfill its life-cycle specifications. Existing converter-level reliability analysis ...methods have two major limitations: 1) being based on constant failure rate models; and 2) lack of consideration of long-term operation conditions (i.e., mission profile). Although various studies have been presented on power electronic component-level lifetime prediction based on wear-out failure mechanisms and mission profile, it is still a challenge to apply the same method to the reliability analysis of converters with multiple components. Component lifetime prediction based on associated models provides only a <inline-formula><tex-math notation="LaTeX">B_{X}</tex-math></inline-formula> lifetime information (i.e., the time when X % items fail), but the time-dependent reliability curve is still not available. In this paper, a converter-level reliability analysis approach is proposed based on time-dependent failure rate models and long-term mission profiles. Two different methods to obtain the component-level time-to-failure are illustrated by a case study of dc/dc converters for a 5 kW fuel cell-based backup power system. The reliability analysis of the converters with and without redundancy is also performed to assist the decision making in the design phase of the fuel cell power conditioning stage.
The Internet of Things (IoT) aims to transform the human society toward becoming intelligent, convenient, and efficient with potentially enormous economic and environmental benefits. Reliability is ...one of the main challenges that must be addressed to enable this revolutionized transformation. Based on the layered IoT architecture, this article first identifies reliability challenges posed by specific enabling technologies of each layer. This article then presents a systematic synthesis and review of IoT reliability-related literature. Reliability models and solutions at four layers (perception, communication, support, and application) are reflected and classified. Despite the rich body of works performed, the IoT reliability research is still in its early stage. Challenging research problems and opportunities are then discussed in relation to current underexplored behaviors and future new aspects of evolving IoT system complexity and dynamics.
•The common cause failures is modeled and quantified by decomposed partial α factors;•Mixed uncertainties are quantified and expressed by the D-S evidence theory;•A hierarchical structure of system ...reliability is constructed by adding decomposed CCF events layer in evidential network;•Importance and imprecise sensitivities analysis methods are extended and developed;•The proposed method is effectively used to analyze the reliability of an auxiliary power supply system of a train.
Redundant design has been widely used in aerospace systems, nuclear systems, etc. which calls for particular attention to common cause failure problems in such systems with various kinds of redundant mechanisms. Besides, imprecision and epistemic uncertainties also need to be taken into account for system reliability modeling and assessment. In this paper, a comprehensive study based on the evidential network is performed for the reliability analysis of complex systems with common cause failures and mixed uncertainties. The decomposed partial α-factor is used to separate the contribution of independent parts and common cause parts of basic failure events. Mixed uncertainties are quantified and expressed by the D-S evidence theory, and the system reliability with uncertainties is modeled by evidential network. Furthermore, two layers, i.e. a decomposed event layer and coupling layer, are embedded into the evidential network of the system, and, as a result, the hierarchical structure of system reliability is constructed. The importance and sensitivities of various component types and their impact on system reliability are detected. The presented evidential network-based hierarchical method is applied to analyze the reliability of an auxiliary power supply system of a train and the results demonstrate the effectiveness of this method.
The random failure of components in a distribution network leads to power supply interruptions to electricity customers. Among different failure modes in the distribution network components, active ...failure is more frequent and requires the circuit breaker operations to isolate faulty segments. Active failure of a breaker causes the operation of a backup breaker, thus, exposing the larger segment of the network to outages. This paper proposes a new analytical methodology to identify breaker active failure events involving different order of contingencies. This methodology introduces an active breaker incidence (ABI) matrix to capture the active failure of breakers leading to load point failures. The ABI matrix is concatenated to the incidence matrix of the minimal path to form a new incidence matrix which reflects the information of all failure events including active failure of circuit breakers. These failure events are then utilized to evaluate the reliability indices. The proposed methodology is illustrated in a test distribution network. A study conducted on the IEEE Gold Book Standard Network shows that that the methodology effectively identifies and includes breaker failure events to evaluate the reliability performance, and that the proposed methodology can be utilized to make investment decisions in modern industrial distribution systems.
The development of information and communication technologies and the deregulation of power systems have made the flexible demand participation in bidirectional interaction with power grid possible. ...The flexible demand can be represented as the flexible reserve provider (FRP) to provide operating reserve through load curtailment and shifting for assisting power system operation. However, the chronological characteristics of curtailment and shifting of FRP may impact the reliability of power systems. Moreover, the uncertainties from customers' participation performances, random failures of information and communication system, and different load types may also influence system operation. In this paper, a novel operating reliability evaluation model for power systems with FRP is proposed utilizing reliability network equivalent (RNE) and time-sequential simulation (TSS) techniques. The RNE technique is developed to include the reserve capacities of FRP incorporating both chronological characteristics and uncertainties. Optimal operation dispatch for system contingencies considering co-optimization of generation and FRP deployment amount is formulated over the entire study period. The TSS method is utilized to assess the operating reliability of restructured power systems. The proposed approaches are validated using the modified IEEE RTS.
The reliability evaluation of the power communication network is beneficial for the improvement of the stable operation of the power system and the robustness of the power grid. However, the existing ...reliability evaluation models of the power communication network cannot meet the current situation of timeliness performance, due to rapidly increasing scale and complexity of information across varying services. In this study, we used the complex network theory to analyze the structure of the power communication network. Then we constructed the evaluation index of node (link) reliability of the power communication network based on community reliability. Compared with the traditional reliability indexes, our index not only considers the influence of the environment of the node (link) on the single structure of the power communication network, but also possesses the reliability evaluation rate of the node (link), which have the opportunities for improving the performance of the reliability evaluation of the wide-area power communication network. To verify the rationality of the index, we developed random, low reliability, and high-betweenness deliberate attacks to attack the designated node (link), and compared the network efficiency before and after the attack. Based on the simulation results, it can verify the rationality and superiority of our proposed evaluation index.
Reliability evaluation of power distribution systems is a well-studied and understood problem, but topology analysis efficiency and failure feature description are still limiting the practical ...application of reliability evaluation in distribution systems. One approach the authors proposed in this study is to incorporate the machine-learning technique into the simulation-based reliability evaluation method, and assess the system reliability in an empirical fashion. In this study, a framework for performing the machine-learning-based reliability evaluation, and corresponding modelling processes are first established. Then, by inducing a supervised learning algorithm named perceptron, a state-space classification-based (SSC) method for system state assessment, the core procedure of reliability evaluation, is proposed. On this basis, a reliability evaluation algorithm combining the SSC-based system state assessment and sequential Monte Carlo simulation is proposed, where the workload of topology analysis required in conventional reliability evaluation methods can be released. Furthermore, extensive case studies are conducted on the Roy Billinton Test System (RBTS) bus 2 system and an actual distribution system to verify the proposed models and algorithms. Results show that the proposed framework supports a more efficient reliability evaluation pattern while ensuring the evaluation accuracy.
This article aims to incorporate the reliability model of power electronic converters into power system reliability analysis. The converter reliability has widely been explored in device- and ...converter-levels according to physics of failure analysis. However, optimal decision-making for design, planning, operation, and maintenance of power electronic converters require system-level reliability modeling of power electronic-based power systems. Therefore, this article proposes a procedure to evaluate the reliability of power electronic-based power systems from the device-level up to the system-level. Furthermore, the impact of converter failure rates including random chance and wear-out failures on power system performance in different applications such as wind turbine and electronic transmission lines is illustrated. Moreover, because of a high calculation burden raised by the physics of failure analysis for large-scale power electronic systems, this article explores the required accuracy for reliability modeling of converters in different applications. Numerical case studies are provided employing modified versions of the Roy Billinton Test System (RBTS). The analysis shows that the converter failures may affect the overall system performance depending on its application. Therefore, an accurate converter reliability model is, in some cases, required for reliability assessment and management in modern power systems.
•The concept of FORM importance measures are generalized to handle challenging problems.•GRIM overcome challenges in problems with multiple critical subdomains or large curvature.•Using regional ...participation factors, the relative importance is evaluated per each critical region.•GRIMs are computed using a Gaussian mixture model fitted to the density in the failure domain.•Various numerical examples successfully demonstrate merits and applicability of GRIMs.
In structural reliability analysis, it is often desirable to evaluate the relative contributions of random variables to the variability of the limit-state function in the failure domain. Based on the relative contributions, one can effectively reduce the dimension of the reliability problem or obtain useful insight and information. However, existing reliability importance measures, which are available as a by-product of reliability analysis by first-order reliability method (FORM), may not capture the contributions of random variables accurately when the limit-state surface shows a large curvature around the design point or multiple critical subdomains exist in the failure domain. To address the issue, this paper proposes a Generalized Reliability Importance Measure (GRIM) that can deal with multiple critical failure regions, large curvatures of limit-state surfaces and the correlation between the input random variables. By introducing Gaussian mixture and the regional participation factor, the failure domain is divided into several subdomains, and the relative contributions of random variables in each critical domain are evaluated. To facilitate the computations of GRIMs, the cross-entropy-based adaptive importance sampling technique (CE-AIS-GM) is employed to identify the locations of critical subdomains, their relative contributions and corresponding importance vectors. Eight numerical examples covering a variety of component and system reliability problems demonstrate the proposed method and its merits. The test results confirm robust performance against the number of important regions or the dimension. The proposed GRIMs and computational procedure are expected to provide more reliable measures for a wide range of component and system reliability problems. The supporting source code and data are available for download at https://github.com/TyongKim/GRIM.
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