•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.
Modern systems are operating under dynamic environments and the components therein often exhibit positively correlated lifetimes. Moreover, due to various practical reasons such as the load sharing ...mechanism, it is not uncommon that lifetimes of some components dominate the others within the same system. In this study, we first propose a statistical model for system reliability evaluation by jointly considering the correlated component lifetimes and the lifetime ordering constraints. In specific, the effects of the dynamic environments are incorporated by modelling the cumulative hazard function as an exponential dispersion process and the lifetime ordering constraints are modelled by truncating the support of the joint lifetime distribution. We then discuss the statistical inference based on the proposed model. The point estimates of the model parameters as well as the lifetime quantiles are obtained by the maximum likelihood method, and the confidence intervals are constructed by using the generalized pivots. Extensive simulation show that the proposed interval estimation procedures can achieve accurate coverage even under small sample sizes. Two real examples are used to illustrate the proposed modelling and estimation framework.
•A general framework for system reliability modelling and estimation is proposed.•Dynamic operating environments and lifetime ordering constraints are incorporated.•Exponential dispersion process is developed to model the cumulative hazard function.•The generalized pivotal quantities are constructed for interval estimation.•Accurate system reliability estimation is achieved even under small sample sizes.
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.
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 identification of reliability-critical input vectors (RCIVs) is vital in the assessment and prediction of reliability boundaries for logic circuits. This article introduces an approach grounded ...in association rule analysis (ARA) to swiftly and efficiently identify RCIVs in both combinational and sequential circuits. The utilization of the ARA model for validating the circuit's associated primary inputs enhances accuracy while simultaneously reducing the complexity of RCIVs identification. Orienting the generation of new samples with associated inputs expedites the identification process. Quantifying circuit complexity enables the adaptive assignment of algorithmic parameters to circuits of diverse sizes. The construction of input sets facilitates a precise evaluation of the reliability of individual input vectors in sequential circuits. Experimental results on benchmark circuits illustrate that this approach achieves a mean accuracy of 0.9952, with Monte Carlo (MC) method serving as the reference, for small and medium-sized circuits, and require only 20.71% of MC's time overhead. The average coverage of 0.9884 surpasses the reference method by 1.8 times. The stability is 4.35 times higher with the random method on large scale circuits with 224624 gates and 6,642 primary inputs. Circuit designers can swiftly ascertain the average reliability and reliability boundaries of a circuit by using this approach for RCIVs identification. By applying optimizations of the identified RCIVs to expedite convergence and mitigate fluctuations, the influence of these RCIVs can be minimized in reliability evaluation and testing.
This article reviews recent works applying machine learning (ML) techniques in the context of energy systems' reliability assessment and control. We showcase both the progress achieved to date as ...well as the important future directions for further research, while providing an adequate background in the fields of reliability management and of ML. The objective is to foster the synergy between these two fields and speed up the practical adoption of ML techniques for energy systems reliability management. We focus on bulk electric power systems and use them as an example, but we argue that the methods, tools, etc. can be extended to other similar systems, such as distribution systems, microgrids, and multienergy systems.
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).
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