Multi-energy systems (MESs) make it possible to satisfy consumer's energy demand using multiple coupled energy infrastructures, thus increasing the reliability of the energy supply compared to ...separate energy systems (SESs). To accurately and efficiently assess and improve the reliability of MESs, this paper proposes a MES reliability and vulnerability assessment method using energy hub (EH) model. The energy conversion, transmission and storage in MESs are compactly and linearly described by EH model, making reliability and vulnerability assessment of MESs tractable. Indices for MES vulnerability assessment are proposed to find the key components for improving MES reliability. Multi-parametric linear programming (MPLP) with a self-adaptive critical region set is proposed to reduce the computational burden caused by iteratively solving LP problems for a large number of samples during the assessment process. The results of a case study show that the proposed reliability and vulnerability assessment method is able to effectively evaluate the energy supply reliability of different energy sectors in MES as well as find the critical component of an MES from reliability perspective to support its planning. The proposed algorithm, i.e., MPLP with a self-adaptive critical region set, can improve the computational efficiency by an order of magnitude compared to the traditional LP method.
•Epistemic uncertainty and CCFs are synthesized in reliability analysis of MSSs•D-S evidence theory is used to express the epistemic uncertainty in system•A modified β factor parametric model is ...introduced to model the multiple CCF groups•Developed method is shown to be efficient and practical.
With the increasing complexity and size of modern advanced engineering systems, the traditional reliability theory cannot characterize and quantify the complex characteristics of complex systems, such as multi-state properties, epistemic uncertainties, common cause failures (CCFs). This paper focuses on the reliability analysis of complex multi-state system (MSS) with epistemic uncertainty and CCFs. Based on the Bayesian network (BN) method for reliability analysis of MSS, the Dempster-Shafer (DS) evidence theory is used to express the epistemic uncertainty in system through the state space reconstruction of MSS, and an uncertain state used to express the epistemic uncertainty is introduced in the new state space. The integration of evidence theory with BN which called evidential network (EN) is achieved by adapting and updating the conditional probability tables (CPTs) into conditional mass tables (CMTs). When multiple CCF groups (CCFGs) are considered in complex redundant system, a modified β factor parametric model is introduced to model the CCF in system. An EN method is proposed for the reliability analysis and evaluation of complex MSSs in this paper. The reliability analysis of servo feeding control system for CNC heavy-duty horizontal lathes (HDHLs) by this proposed method has shown that CCFs have considerable impact on system reliability. The presented method has high computational efficiency, and the computational accuracy is also verified.
Reliability analysis of deteriorating structural systems requires the solution of time-variant reliability problems. In the general case, both the capacity of and the loads on the structure vary with ...time. This analysis can be approached by approximation through a series of time-invariant reliability problems, which is a potentially effective strategy for cases where direct solutions of the time-variant reliability problem are challenging, e.g. for structural systems with many elements or arbitrary load processes. In this contribution, we thoroughly review the formulation of the equivalent time-invariant reliability problems and extend this approximation to structures for which inspection and monitoring data is available. Thereafter, we present methods for efficiently evaluating the reliability over time. In particular, we propose the combination of sampling-based methods with a FORM (first-order reliability method) approximation of the series system reliability problem that arises in the computation of the lifetime reliability. The framework and algorithms are demonstrated on a set of numerical examples, which include the computation of the reliability conditional on inspection data.
In this paper, a reliability evaluation model and a bilevel unreliability allocation model for the ±500-kV HVdc transmission system with the double-circuit lines on the same tower (DCLST) are ...proposed. The reliability evaluation indices associated with this DCLST HVdc system are also defined. The reliability model is based on the characteristics of common starting point and terminal point structure and double-circuit HVdc coordination operation. And it is developed by integrating HVdc subsystem reliability models together. A multistate Markov model for the dc line (DCL) subsystem is built by considering the common-cause outage of four DCLs on the same tower. The reliability evaluation model of the ac filter (ACF) subsystem is constructed by considering the derated capacity operation state caused by ACF failures. On the other hand, a bilevel unreliability allocation model is constructed based on the proportion-allocation criterion. The allocation model is embedded into the reliability evaluation model so that all reliability indices can be allocated to each component after only one reliability evaluation calculation. As a result, the impact of each component on the system reliability and weak components of the system can be explicitly and unambiguously identified. An actual ±500-kV HVdc transmission project with the DCLST is used for case study to validate the effectiveness of the proposed models and indices.
The estimation of the failure probability for complex systems is a crucial issue for sustainability. Reliability analysis methods are needed to be developed to provide accurate estimations of the ...safety levels for the complex systems and structures of today. In this paper, a novel hybrid framework for the reliability analysis of engineering systems and structures is extended to reduce the computational burden. The proposed hybrid framework is named as SVR–CFORM and consists of coupling two parts: the first is an enhanced first-order reliability method (FORM) using nonlinear conjugate map (CFORM); the second is an artificial intelligence technique called support vector regression (SVR). The conjugate FORM (CFORM) is adaptively formulated to improve the robustness of the original iterative FORM algorithm, whereas the SVR technique is used to enhance the efficiency of the reliability analysis by reducing the computational burden. The performance of the proposed SVR–CFORM formulation is compared in terms of efficiency and robustness with several FORM formulas (i.e. HL–RF, directional stability transformation method, conjugate HL–RF and finite step length) through different numerical/structural reliability examples. Results indicate that the proposed SVR–CFORM formulation is more accurate and efficient than other reliability methods. Based on the comparative analysis results, the SVR technique can highly reduce the computational costs and accurately model the response of complex performance functions, while the iterative CFORM formulation found to provide stable and robust reliability index results compared to the others reliability methods.
•A novel FORM based reliability method is developed for robust analyses of nonlinear problems.•The conjugate FORM is combined with SVR as a robust and efficient MPP search method.•The proposed SVR–CFORM is compared with several reliability methods.•The low-computational cost with stable results is captured using SVR–CFORM compared to other FORM formulas.
With the aim to increase the competitiveness of solar energy, the high reliability of photovoltaic (PV) inverters is demanded. In PV applications, the inverter reliability and lifetime are strongly ...affected by the operating condition that is referred to as the mission profile (i.e., solar irradiance and ambient temperature). Since the mission profile of PV systems is location-dependent, the inverter reliability performance and lifetime can vary considerably in practice, that is, from the reliability perspective, PV inverters with the same design metrics (e.g., component selection) may become over or underdesigned under different mission profiles. This will increase the overall system cost, e.g., initial cost for overdesigned cases and maintenance cost for underdesigned cases, which should be avoided. This article, thus, explores the possibility to adapt the control strategies of PV inverters to the corresponding mission profiles. With this, similar reliability targets (e.g., component lifetime) can be achieved even under different mission profiles. Case studies have been carried out on PV systems installed in Denmark and Arizona, where the lifetime and the energy yield are evaluated. The results reveal that the inverter reliability can be improved by selecting a proper control strategy according to the mission profile.
With the growing penetration of power electronic converters in power systems, the issue of reliability becomes more critical than ever before. This paper proposes a hierarchical reliability framework ...to evaluate the electric power system reliability from the power electronic converter level to the overall system level. On the converter level, the reliability model of a power electronic converter is developed based on the power electronic devices it is composed of, for which various hourly based input profiles and converter topologies are considered. On the system level, reliability metrics such as expected energy not served (EENS) and loss of load expectation (LOLE) are estimated through a non-sequential Monte Carlo simulation. Machine learning regression models, such as support vector regression (SVR), and random forests (RF) are implemented to bridge the nonlinear reliability relationship between two levels. The proposed framework is demonstrated through the modified IEEE Reliability Test System (RTS) 24-bus network. Numerical results show power converter reliability should be considered as an important factor when evaluating overall system reliability performance.
•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.
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