Various uncertainties exist in engineering practice, which brings adverse effects to the reliability of complicated engineering systems. Considering the case that interval and fuzzy uncertainties ...exist simultaneously, a new reliability estimation model is proposed based on the level cut strategy and volume ratio theory. The new reliability model is better than the traditional one which is very conservative. Moreover, a sequential optimization and reliability assessment (SORA) approach for multidisciplinary systems under hybrid interval and fuzzy uncertainties is developed to decouple the reliability analysis from the deterministic multidisciplinary design optimization (MDO). In the framework of SORA, the deterministic MDO and reliability analysis are executed sequentially, thus the efficiency can be improved. For the multidisciplinary uncertainty analysis, the first order Taylor expansion method and the interval vertex method are formulated. The calculation of the safety possibility under the volume ratio theory and the calculation of the shifting distance are deduced. Both numerical and engineering examples are employed to demonstrate the validity of the proposed method.
•An efficient system reliability analysis method is proposed by combining structure function and active learning kriging model.•The point with the highest probability of incorrect prediction is ...selected at each iteration.•The whole system is treated as a general component to select new sample points.•The magnitude effect among components have no influences on the proposed method.
Surrogate models are useful for reducing the computational burden in real applications. Structural reliability analyses based on active learning kriging models, such as efficient global reliability analysis (EGRA) and an active learning method to combine kriging and MCS (AK–MCS), have been widely proposed. However, these methods are mainly suitable for component reliability analyses. In general, the reliability analysis of practical engineering problems is mostly performed at the system level with multiple failure models. Two representative system reliability methods, i.e., an adaptation of the AK–MCS method for system reliability (AK–SYS) and system reliability analysis through active learning kriging model with truncated candidate region (ALK–TCR), are very useful for system reliability analysis with only random variables. However, these methods select training points from the perspective of component responses and are difficult to implement for complex systems. Therefore, the balance between applicability, accuracy and efficiency can be further improved. In this study, an efficient reliability method for structural systems with multiple failure modes is proposed to further extend the AK–SYS and ALK–TCR. A new learning function based on the system structure function, which efficiently take into account the influence of the different components and their logical arrangement through the use of the system's structure function, is developed to select the added points adaptively from the perspective of the system. Based on the proposed learning function, surrogate models are accurately constructed. Compared to AK–SYS and ALK–TCR, the proposed method has the following three main advantages: (1) the new learning function selects the added points from the perspective of the system to fully and directly utilize the predicted information of all the components; (2) the magnitude effect, which refers to the several orders of magnitude existing among the responses of components, have no influence on the proposed method; and (3) the proposed method is robust and has high applicability for complex systems. Four numerical examples are investigated to show the applicability and efficiency of the proposed method, and the results indicate that the proposed method is effective for system reliability analysis.
•To review the state-of-the-art and future developments on adoption of BN models in wind energy;•To identify relevant academic publications, best practice documents and software user guides;•To ...identify and evaluate various applications of BNs in wind energy;•To discuss the applications of BNs to risk management, degradation analysis, fault diagnosis, reliability analysis, and O&M planning and updating;•To analyse a number of case studies to show the applicability of BNs in practice.
Wind energy farms are moving into deeper and more remote waters to benefit from availability of more space for the installation of wind turbines as well as higher wind speed for the production of electricity. Wind farm asset managers must ensure availability of adequate power supply as well as reliability of wind turbines throughout their lifetime. The environmental conditions in deep waters often change very rapidly, and therefore the performance metrics used in different life cycle phases of a wind energy project will need to be updated on a frequent basis so as to ensure that the wind energy systems operate at the highest reliability. For this reason, there is a crucial need for the wind energy industry to adopt advanced computational tools/techniques that are capable of modelling the risk scenarios in near real-time as well as providing a prompt response to any emergency situation. Bayesian network (BN) is a popular probabilistic method that can be used for system reliability modelling and decision-making under uncertainty. This paper provides a systematic review and evaluation of existing research on the use of BN models in the wind energy sector. To conduct this literature review, all relevant databases from inception to date were searched, and a total of 70 sources (including journal publications, conference proceedings, PhD dissertations, industry reports, best practice documents and software user guides) which met the inclusion criteria were identified. Our review findings reveal that the applications of BNs in the wind energy industry are quite diverse, ranging from wind power and weather forecasting to risk management, fault diagnosis and prognosis, structural analysis, reliability assessment, and maintenance planning and updating. Furthermore, a number of case studies are presented to illustrate the applicability of BNs in practice. Although the paper details information applicable to the wind energy industry, the knowledge gained can be transferred to many other sectors.
In power distribution systems, sometimes, system reliability indices are available, where some components' reliability parameters are unknown. This letter presents the inverse reliability evaluation ...(IRE) problem in radial distribution systems to find unknown components' parameters from the known system reliability indices. To this end, a nonlinear system of equations is presented and solved. The solutions are analyzed in the RBTS bus 2 to verify the applicability of the proposed approach and to show the importance of the IRE problem.
As photovoltaic (PV) generation has been one of the major renewable energy sources around the world, its PV capacity has also increased. When the large-scale PV systems are integrated into the ...distribution network, the complexity of the assessment process of the distribution network reliability will increase hazardously. In order to accurately assess this reliability in the distribution network combined with the PV generation, a reliability assessment procedure is proposed. In order to accurately evaluate the impact of the failure of conventional power equipment on reliability, the time-varying failure rate of conventional power equipment is modeled, taking into account the aging period. Then, in order to accurately evaluate the reliability improvement with PV systems integration, the new procedure is proposed highlighting the following contributions: 1) five new indices are added. 2) PV output is modeled so that not only the radiation intensity but also the failure and degradation of PV modules are represented. 3) time-varying islanding operation is considered and integrated. A case study using real-life distribution network topology and data in China is applied to verify that the newly proposed reliability indices display more sensitivity, and the proposed procedure significantly improves the accuracy of the reliability assessment.
The reliability analysis of structural systems with multiple failure modes and mixed variables is a critical problem because of complex nonlinear correlations among failure modes (or components), ...huge computational burden of time-consuming implicit functions, and complex failure regions. In this paper, aleatory and epistemic uncertainties are considered simultaneously, and an efficient adaptive kriging-based reliability method is proposed for structural systems with multiple failure modes and mixed variables. Two new learning functions are developed as guidelines for selecting new training samples at each iteration. The proposed learning functions and corresponding stopping criteria are directly linked to system probability of failure; this allows the proposed method to select new training samples efficiently To determine the lower and upper bounds of system probability of failure, the limit-state functions in the entire uncertainty space of interest are accurately constructed while avoiding complicated nested optimizations. The proposed method has the following advantages: (1) the learning functions and stopping criteria are directly linked to system probability of failure, and the structure importance of components is also considered; (2) it requires fewer samples to achieve accurate results, and can be applied to small system probability of failure; (3) it is easy to use for extremely complex systems (e.g., bridge systems); (4) it can be applied to a system with multiple failure modes and mixed variables (e.g., mixture of random and p-box variables). The capabilities and efficiency of the proposed method are validated through four numerical examples; results show that it has high applicability and accuracy.
•The proposed learning functions are directly linked to system probability of failure.•Structure importance is considered in the component refinement process.•The proposed method is effective for the large and complex systems.•The proposed method allows a good balance between accuracy and efficiency.•The proposed method is effective for structural systems with mixed variables and time consuming simulations.
The ever-increasing integration of non-dispatchable distributed generation, i.e., renewable energy sources (RES), arises new challenges in the field of power system's reliability. Distribution ...network reconfiguration (DNR) is a cost-effective approach for the distribution system operator (DSO) that wishes to enhance system's reliability without infrastructure upgrades. This paper introduces a novel path-based mixed-integer second-order cone programming model to optimally solve the reliability-oriented DNR problem. The DSO's objectives that are optimized are: a) improvement of distribution system's reliability indices and b) minimization of power losses. The proposed model is enriched with a scenario-based stochastic programming formulation that considers multiple levels of load and RES production. The standard 33-nodes distribution system and a real-world 83-nodes distribution system are employed to prove the efficiency and applicability of the model. Firstly, the multi-objective nature of the reliability-oriented DNR problem is investigated by conducting a sensitivity analysis, which reveals a trade-off region between reliability indices and power losses. Moreover, the obtained results show different global optimal solutions when the variability of load and RES production is considered. This highlights the importance of considering a scenario-based approach for load and RES production when solving the reliability-oriented DNR problem.
Improving the computational efficiency of reliability assessment is a long-term goal for researchers. Regarding this issue, an impact-increment-based decoupled reliability assessment approach (IID) ...is proposed in this study. First, a new formulation of reliability indices is derived by decoupling the reliability index of composite systems into two parts: generation adequacy and transmission reliability. Then, an impact-increment-based state enumeration reliability assessment method is implemented to both the parts, which are more efficient than tradition methods. Thereafter, a reduction technique for the higher-order contingency states is developed for the transmission reliability evaluation. With this technique, the number of analysed contingencies can be significantly reduced, which improves the computational speed significantly. Finally, the overall framework of the proposed IID approach is demonstrated. Case studies are performed on the RTS-79, RTS-96 and a provincial system of China. Results indicate that the proposed method has superior performance to the traditional reliability assessment methods in terms of accuracy and computational speed, particularly on large-scale composite systems.
Modern aircrafts are evolving toward more electric aircraft (MEA), resulting in greater reliance on the electrical system for safe flight. On-board power system of MEA integrates a large number of ...power electronic converters, and it is reported that semiconductor devices and electrolytic capacitors in power converters are the most vulnerable links impacted by loading conditions; thus, reliability becomes a critical concern in an MEA power system. This paper proposes a hierarchical approach for systematic reliability modeling and evaluation for the on-board power system of MEAs. It consists of three hierarchical levels (HLs): component level (HL1), subsystem level (HL2), and system level (HL3). In HL1, failure rates of power electronic components are modeled considering relevant inner structure and loading conditions; in HL2, the reliability of individual subsystems such as converters are constructed; in HL3, the system reliability is quantified based on the network architecture and reliability of the subsystems. The impacts of different parts (components/subsystems) on the overall system are assessed effectively with the identification of the vulnerable parts. This also provides a guideline for reliability enhancement by using thermal control techniques, adding redundancies or performing maintenance on the vulnerable parts to ensure the satisfactory of system reliability requirements. The proposed method is demonstrated on the future MEA power system architectures (hybrid ac-dc architecture and HVdc architecture).
•Complex system reliability analysis using path testing and decomposition techniques.•Predict/analyze the complete system reliability without testing the entire system.•Accurate software architecture ...quality prediction during design phase.•Less complexity achieved using sequential path execution.•Predicting entire software system reliability with highest test coverage.
With the increasing needs of the people in this generation, a large number of highly featured quality software systems need to be developed in all the domains. Nowadays complexity of the software system is gradually increased because of its large size. With this nature, the traditional software development process unable to produce higher quality software system within limited resources. So that, the traditional software development process has been moved to the reuse based component based software development (CBSD) which reduces the time and resource of software development. Testing is the important process in the software development life cycle to ensure the reliability or quality of software systems. Lots of reliability models have been developed to predict the software system reliability in the earlier stages of development. But these existing reliability analysis models are insufficient to estimate the reliability of component based software system (CBSS) within the limited resources. To solve this issue, the new approach was introduced by many researchers based on software architecture to estimate the reliability of component based software system. Based on that, we have proposed new framework centered on path testing to predict the reliability of the CBSS. Here we have chosen three test paths (simple, medium and complex structure) from the system for reliability estimation instead of taking all the paths. Then independent simple paths have been identified from the chosen medium and complex path to reduce the complexity of reliability estimation. All the simple paths are executed sequentially to estimate its reliability. Actual software system reliability will be predicted based on the estimated path reliability. The ATM case study has been taken to validate the proposed framework. The result obtained from this experiment is compared with the standard baseline models CUORM, LCBRM and Chao-Jung to prove the accuracy and efficiency of our proposed model. The result shows that, our proposed framework has the acceptable accuracy compared to the other models.
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