Onboard sensors, which constantly monitor the states of a system and its components, have made the predictive maintenance (PdM) of a complex system possible. To date, system reliability has been ...extensively studied with the assumption that systems are either single-component systems or they have a deterministic reliability structure. However, in many realistic problems, there are complex multi-component systems with uncertainties in the system reliability structure. This paper presents a PdM scheme for complex systems by employing discrete time Markov chain models for modelling multiple degradation processes of components and a Bayesian network (BN) model for predicting system reliability. The proposed method can be considered as a special type of dynamic Bayesian network because the same BN is repeatedly used over time for evaluating system reliability and the inter-time-slice connection of the same node is monitored by a sensor. This PdM scheme is able to make probabilistic inference at any system level, so PdM can be scheduled accordingly.
This paper develops a general framework for reliability assessment of multi-microgrid (MMG) distribution systems. It also investigates reliability impacts of coordinated outage management strategies ...in a MMG distribution network. According to the proposed reliability evaluation framework, which is based on sequential Monte Carlo simulation method, distribution system is divided into smaller sections/microgrids based on protection system configuration and operating measures are efficiently simulated considering different operation modes. In order to demonstrate the role of outage management strategy in reliability performance of MMG distribution systems, at first, the required features of an outage management strategy are identified. Then, suitable centralized and hierarchical schemes are introduced for operation of such systems during outage events. The proposed schemes, which are based on model predictive control approach, minimize total load curtailments in the system. Moreover, they are flexible and can effectively deal with multiple contingencies as well as uncertainties of outage duration. The developed reliability assessment framework is applied to a test system and performance of the presented outage management schemes are explored through extensive case studies. Obtained results suggest that implementation of an appropriate coordinated scheme is crucial to reliable operation of MMG distribution systems.
In this paper, an optimal reliability allocation (ORA) model of ±800 kV ultra-high voltage direct current (HVDC) transmission system is proposed. The model intends to provide the means to determine ...the availability requirements of components in the bidding process of ±800 kV ultra HVDC transmission projects. In this model, the component availability is optimized based on the prespecified system reliability. The objective function is composed of the component investment cost and the operation and maintenance cost. The ORA is formulated as a constrained nonlinear programming problem, and the genetic algorithm (GA) is used to find its solution. An improved dagger sampling (IDS) method is proposed to evaluate the reliability of ultra HVDC systems, and a bisection method is used to generate the initial population of the GA. An actual ultra HVDC project is used to conduct the case study. Results show that the proposed ORA model can minimize the component investment, operation, and maintenance costs, while achieving optimal allocation of the system reliability index to obtain the availability of components. In addition, the impact of the component spare on the ORA result is investigated. Performance comparison on the sampling efficiency of the IDS method and the direct Monte Carlo sampling method is also presented.
•A bayesian updating approach is used to obtain accurate reliability prediction.•A unified maintenance decision framework is proposed.•A two-stage heuristic algorithm is designed to seek optimal ...maintenance grouping.
Prognostic methods for remaining useful life and reliability prediction have been extensively studied in the past decade. However, the use of prognostic information and methods in maintenance decision-making for complex systems is still an underexplored area. In this paper, using a rolling-horizon approach, we develop a condition-based maintenance decision-framework for a multi-component system subject to a system reliability requirement. The system is inspected periodically and new degradation information on components is obtained upon inspection. The new degradation observations are used to update the posterior distributions of the failure model parameters via Bayesian updating, providing more accurate and customized predictive reliabilities. If the predictive system reliability is below the reliability requirement, a novel dynamic-priority-based heuristic algorithm is used to identify a group of components for preventive maintenance. Numerical results show that significant cost savings and improved system reliabilities can be obtained by using more accurate predictive information in maintenance decision-making. We further illustrate the modeling flexibility of the proposed framework by considering dynamic environmental information in decision-making and investigate the potential benefits of incorporating dynamic contexts.
Over the years, probabilistic nature of renewable energy sources (RES) and its influence on power system adequacy have been well studied. However, rather less attention has been paid to the impact of ...RES unit itself's and its power conversion system's (PCS') reliability, as well as their various connection topologies. This paper devises a comprehensive sensitivity study on how each of these elements can affect overall generating system reliability. given the plethora of RES configurations and components, it is of import to identify the most vulnerable element in RES. In this work, component importance is extended, for the first time, to generating capacity adequacy assessment (HLI). Measurement index is the centerpiece in reliability importance. New indices have to be introduced to facilitate the study. While the physical meaning of previously developed indices is lost, in this study indices are proposed based on traditional importance measures, of which the physical meaning are strictly retained and consistent with the definitions. With the proposed assessment technique, components in various RES configuration can be ranked according to their reliability importance. It is found in the numerical study that different importance measures (such as risk-achievement based measures and risk-reduction based measures) can result in different rankings. Studies on contributing factors of the reliability importance are also performed. As more and more RES gaining foothold in generating systems, the proposed technique assist to achieve targeted reliability level of the system, by easily identifying and prioritizing reliability improvement tasks among various units/components in the increasing complex system.
•A unified framework of static/dynamic reliability analysis is established based on direct probability integral method (DPIM).•New formula to determine smoothing parameter of Dirac function is ...suggested.•Two DPIM-based approaches for dynamic reliability analysis are proposed.•Example of nonlinear dynamic structure indicates superiority of unified framework.
Generally, the static and dynamic reliabilities of structures are addressed separately in the existing methods except the computationally expensive stochastic sampling-based approaches. This study establishes a unified framework of reliability analysis for static and dynamic structures based on the direct probability integral method (DPIM). Firstly, the probability density integral equations (PDIEs) of performance functions for static and dynamic structures are presented based on the principle of probability conservation. The DPIM decouples the physical mapping (i.e., performance function) of structure and PDIE, and involves the partition of probability space and the smoothing of Dirac delta function. This study proposes a new adaptive formula of smoothing parameter based on kernel density estimation. Then, the improved DPIM is utilized to obtain the probability density function (PDF) of performance functions by solving the corresponding representative values and the PDIE successively. Furthermore, the reliability of static structure is calculated by integrating the PDF of performance function within safety domain. To overcome the difficulty of evaluating first passage dynamic reliability, the two approaches, namely the DPIM-based absorbing condition (DPIM-AC) and the DPIM-based extreme value distribution (DPIM-EVD), are also proposed. Finally, three engineering examples with stochastic parameters and random excitation indicate the desired efficiency and accuracy of the established framework for unified reliability analysis. Specifically, the challenging issue of dynamic reliability assessment for nonlinear structural system is attacked based on DPIM rather than Monte Carlo simulation or other sampling-based method. The proposed method is beneficial for propagation analysis of aleatory or/and epistemic uncertainties, as well as for stochastic model updating.
To achieve high reliability, the urban distribution networks are mesh-constructed and radial-operated, in which the outage load can be restored to adjacent feeders via tie-lines after faults. ...Conventionally, iterative optimization-simulation methods and heuristics are adopted for distribution network planning, which cannot guarantee global optimality. Besides, existing reliability-constrained planning model cannot explicitly assess the reliability indices for mesh distribution networks, so the resulted plan scheme may be overly invested. In this paper, we propose a novel multistage expansion planning model for mesh distribution networks, in which reliability assessment is explicitly implemented as constraints. The different investment/reliability preferences for buses are also customized. Specifically, post-fault load restoration between feeders through tie-lines is modeled as a case of post-fault network reconfiguration. The planning model is then cast as an instance of mixed-integer linear programming and can be effectively solved by off-the-shelf solvers. We use a 54-node system to test the performance of proposed model. Simulation results show the effectiveness and flexibility of this methodology.
•A new multi-state system model with state transition dependency is developed.•Copula functions are used to implicitly characterize state transition dependency.•Reliability measures for the new ...multi-state system model are given.•Likelihood functions for continuous and discontinuous inspection data are derived.
As multi-state system reliability models are capable of characterizing the multi-state nature of engineered systems in deteriorating process, they have received considerable concerns in the past few decades. Most reported works on multi-state system reliability modeling are, however, based on the premise that transitions among states of a system/component are stochastically independent. Sometimes, a system may experience the same environmental/working conditions when deteriorating from a better state to worse ones, and thus, state transitions of a system/component could be stochastically dependent. In this paper, a new reliability model for multi-state systems/components with state transition dependency is put forth. The dependency among state transitions is implicitly characterized by copula functions which offer a great flexibility of linking arbitrary marginal distributions together to construct a multivariate distribution. The reliability function of a population of multi-state systems/components and the conditional reliability function of each individual multi-state system/component given a set of observations are derived. The likelihood functions for model parameter estimation are formulated for two cases, i.e., continuous and discontinuous inspection strategies, and model selection criteria are customized to identify the most preferable model among candidates. Two illustrative examples, together with comparative studies, are presented to demonstrate the effectiveness of the proposed method.
Large-scale adoption of gas-fired power plants (GPPs) significantly accelerates the integration of power systems and natural gas systems (NGSs). Because the operation of NGS has a significant impact ...on the reliability of power system, it is important to simultaneously evaluate the reliabilities of power system and NGS. Moreover, due to the stochastic failures and various operating characteristics of components, the integrated gas and power systems (IGPS) considering multiple states can be viewed as a multi-state system. Therefore, a multi-state model is proposed for reliability evaluation of IGPS based on universal generating function (UGF) techniques. First, a framework is proposed to evaluate the reliability of NGS: multi-state models for different components in the NGS are developed as the corresponding UGFs. These UGFs are aggregated utilizing the gas flow calculation operator to obtain the multi-state model of NGS. Moreover, a gas-to-power calculation operator is developed to convert the multi-state model of gas injection at each node to the power output models of GPPs in power system. In this manner, the impacts of NGS could be incorporated into the reliability evaluation of power system. Furthermore, nodal reliability indices for both the NGS and the power system are proposed to evaluate the reliability performances of IGPS. The proposed methods are validated using the integrated gas and power test system.
•A kernel density function based uncertainty quantification model is constructed.•A series system reliability model is built after decoupling time sequence.•Two-dimensional kernel density function is ...used to quantify failure correlation.•With time discretization, time-dependent system reliability analysis is finally performed.•Higher accuracy and efficiency of the proposed method have been validated by a case study.
This paper proposes a sequential time-dependent reliability analysis method by considering time sequence and correlation of failure processes for the lower extremity exoskeleton under uncertainty, which will provide an approach to improving the comfort and safety for the wearer. A kernel density function based uncertainty quantification method is provided for precisely quantitatively estimating the time-dependent reliability of joints and the position of the end-effector firstly. After decoupling time sequence and failures correlation due to error propagation, the original reliability problem is then transferred to a series time-dependent reliability model. The time-dependent system reliability analysis is finally realized by calculating conditional probability. A case study is implemented to testify the effectiveness of the proposed method.
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