电气化铁路的负荷特性及电能质量分析

本文从电气化铁路的负荷特性出发,分析了电力机车的电气特性、列车的牵引特性及牵引变电所的负荷特性,并结合电气化铁路牵引供电方案,分析了电气化铁路的供电需求及电能质量特性,提出应结合铁路负荷特性和电网条件,完善电气化铁路供电方案和电能质量综合治理措施。     

Reliability evaluations of railway power supplies by fault-tree analysis

High reliability of railway power systems is one of the essential criteria to ensure quality and cost-effectiveness of railway services. Evaluation of reliability at system level is essential for not only scheduling maintenance activities, but also identifying reliability-critical components. Various methods to compute reliability on individual components or regularly structured systems have been developed and proven to be effective. However, they are not adequate for evaluating complicated systems with numerous interconnected components, such as railway power systems, and locating the reliability critical components. Fault tree analysis (FTA) integrates the reliability of individual components into the overall system reliability through quantitative evaluation and identifies the critical components by minimum cut sets and sensitivity analysis. The paper presents the reliability evaluation of railway power systems by FTA and investigates the impact of maintenance activities on overall reliability. The applicability of the proposed methods is illustrated by case studies in AC railways     

Genetic algorithm optimisation of the maintenance scheduling of generating units in a power system

A new method for optimisation of the maintenance scheduling of generating units in a power system is developed. Maintenance is scheduled to minimise the risk through minimisation of the yearly value of the loss of load expectation (LOLE) taken as a measure of the power system reliability. The proposed method uses genetic algorithm to obtain the best solution resulting in a minimal value of the annual LOLE value for the power system in the analysed period. The operational constraints for generating units are included in the method. The proposed algorithm was tested on a Macedonian power system and the obtained results were compared with the results received from the approximate methodology. The results show the improved reliability of a power system with the maintenance schedule obtained by the new method compared to the results from the approximate methodology.     

Reliability‐based Optimal Maintenance Scheduling by Considering Maintenance Effect to Reduce Cost

A new reliability‐based optimal maintenance scheduling method is presented that considers the effect of maintenance in reducing costs. An ordering list of element maintenance effects with various maintenance‐interval types is constructed. By means of this ordering list, reliability‐based optimal maintenance scheduling for simple reliability structures and composite reliability systems is then carried out. The properties of the proposed method, such as the evaluation of maintenance cost reduction, the simplicity of the proposed method by sacrificing system availability within the allowance method, the operation decision based on the optimal maintenance schedule, etc., are discussed. With simulations, the effectiveness of the proposed method is verified. Copyright © 2005 John Wiley & Sons, Ltd.     

Optimal Scheduling of Track Maintenance on a Railway Network

To ensure the safety and continued operation of the railway network system, many maintenance and renewal activities are performed on the track every month. Unplanned maintenance activities are expensive and would cause low service quality. Therefore, the track condition should be monitored, and when it has degraded beyond some acceptable limit, it should be scheduled for maintenance before failure. An optimal timetable of the maintenance activities is needed to be scheduled, planning the monthly workload, to reduce the effect on the transportation service and to reduce the potential costs. Considering the uncertainties of the deterioration process, the safety of transportation service, the lifetime loss of the replaced track, the maintenance cost and the travel cost, this article advances an optimisation model for the maintenance scheduling of a regional railway network. An enhanced genetic algorithm approach is proposed to search for a solution producing maintenance schedule such that the overall cost is minimised in a finite planning horizon. A case study is given to demonstrate the application of the method. The case study results were derived by using an enhanced genetic algorithm method, which is specifically developed to deal with the characteristics of the railway maintenance problem. Copyright © 2012 John Wiley & Sons, Ltd.     

Optimized work control process to improve safety and reliability in a risk-based and deregulated environment

This paper provides an overview of strategic models to assist power generating plants to improve their work control processes. These models include mechanisms to continually keep the process up to date. Included in the work control process are elements for system cost/performance analysis, life-cycle maintenance planning, on-line scheduling and look-ahead techniques, and schedule implementation to conduct work on the asset. The paper also discusses how risk management associated with work control issues that effect the safety and reliability, as well as O and M costs, is integrated into this strategy.The work control process is a pervasive and critical element in the successful implementation of operations and work management programs. While providing a method to implement maintenance activities in a cost-effective manner, the work control process improves plant safety and system reliability.     

串联生产系统智能维护调度实验平台研究

结合工业工程专业学习需求,设计了考虑有限缓存的串联生产系统维护调度实验平台。实验平台由维护调度计算模块和维护调度演示模块组成。维护调度计算模块基于Matlab,在考虑有限缓存的串联生产系统维护调度模型的基础上,提供3种不同的策略对维护进行调度;维护调度演示模块基于Flexsim。基于该平台,可完成维护调度参数分析、维护调度策略分析、维护调度仿真等实验,并可在此平台基础上建立维护调度相关参数优化模型。     

An optimization based decision support system for integrated planning and scheduling on dedicated freight corridors

Empty wagon redistribution, train formation, routing and scheduling are complex problems for large railways, many of which currently have or are planning dedicated freight railway corridors (DFC). DFC operations due to their unique characteristics require research and new models for better operations planning. The rolling-stock, being expensive assets, need to be utilised in an optimal manner while meeting service quality levels. Motivated by Indian DFC, we present an integer programming formulation of the dynamic problem of empty distribution and train scheduling in DFC and discuss associated modelling issues. By unifying the separate problems into a single and dynamic model, we have developed a framework for more effective rolling stock utilisation. Based on this optimization model, an interactive decision support system is proposed for better decision-making on rolling-stock allocation and train scheduling. Extensive experiments and systematic analyses for a case of Indian DFC highlight the potentialities and effectiveness of the proposed DSS for DFC operations planning and management.     

A digital filter-based approach to the remote condition monitoring of railway turnouts

Railway operations in Europe have changed dramatically since the early 1990s, partly as a result of new European Union Directives. Performance targets have become more and more exacting, due to reductions in state support for railways and the need to increasing traffic. More intensive operations also place greater demands on the hardware of the railway. This is true for both rolling stock and infrastructure subsystems and components, particularly so in the case of the latter where the time available for maintenance is being reduced. The authors of this paper focus on the railway infrastructure, and more specifically on points. These are critical elements whose reliability is key to the operation of the whole system. Using intelligent monitoring systems, it is possible to predict problems and enable quick recovery before component failures disrupt operations. The authors have studied the application of remote condition monitoring to point mechanisms and their operation, and have identified algorithms which may be used to identify incipient failures. In this paper, the authors propose a Kalman filter for the linear discrete data filtering problem encountered when using current sensor data in a point condition monitoring system. The reason for applying Kalman filtering in this study was to increase the reliability of the model presented to the rule-based decision mechanism.     

Decision-making on multi-mould maintenance in production scheduling

The reliability of a critical tool like a mould on a machine affects the productivity seriously in many manufacturing firms. In fact, its breakdown frequency is even higher than machines. The decision-making on when mould maintenance should be started become a challenging issue. In the previous study, the mould maintenance plans were integrated with the traditional production schedules in a plastics production system. It was proven that considering machine and mould maintenance in production scheduling could improve the overall reliability and productivity of the production system. However, the previous model assumed that each job contained single operation. It is not workable in other manufacturing systems such as die stamping which may contain multiple operations with multiple moulds in each job. Thus, this study models a new problem for multi-mould production-maintenance scheduling. A genetic algorithm approach is applied to minimise the makespan of all jobs in 10 hypothetical problem sets. A joint scheduling (JS) approach is proposed to decide the start times of maintenance activities during scheduling. The numerical result shows that the JS approach has a good performance in the new problem and it is sensitive to the characteristic of the setup time defined.     

Reliability-based performance simulation for optimized pavement maintenance

Roadway pavement maintenance is essential for driver safety and highway infrastructure efficiency. However, regular preventive maintenance and rehabilitation (M&R) activities are extremely costly. Unfortunately, the funds available for the M&R of highway pavement are often given lower priority compared to other national development policies, therefore, available funds must be allocated wisely. Maintenance strategies are typically implemented by optimizing only the cost whilst the reliability of facility performance is neglected. This study proposes a novel algorithm using multi-objective particle swarm optimization (MOPSO) technique to evaluate the cost-reliability tradeoff in a flexible maintenance strategy based on non-dominant solutions. Moreover, a probabilistic model for regression parameters is employed to assess reliability-based performance. A numerical example of a highway pavement project is illustrated to demonstrate the efficacy of the proposed MOPSO algorithms. The analytical results show that the proposed approach can help decision makers to optimize roadway maintenance plans.     

Multitasking scheduling problems with a rate-modifying activity

Motivated by the behavioral phenomena that occur while human operators are carrying out tasks, we study multitasking scheduling problems with a rate-modifying activity. In the problems, the processing of a selected task suffers from interruptions by other tasks that are available but unfinished, and the human operators regularly engage rest breaks during work shifts allowing them to recover or mitigate some of the negative effects of fatigue. The objectives are to respectively minimize: makespan, total completion time, maximum lateness, and due-date assignment related cost by determining when to schedule the rate modifying activity and the optimal task sequence in the presence of multitasking. Scheduling models and algorithms are proposed to solve the problems. The numerical examples are presented to illustrate the theorems and algorithms.     

Emission-constrained optimal generation scheduling incorporating equity principles

An annual emission-constrained generation scheduling model that combines equity principles with cost optimisation is developed. By employing this annual generation scheduling model, an estimation of the daily emission allowance of each generation unit is obtained. A daily emission-constrained generation scheduling model, based on cost optimisation, is proposed. The equity-related issues in the daily scheduling model are discussed. The Shapley value is employed in the daily scheduling problem to allocate the operating cost reduction among the units. Characteristics of the allocation game of operating cost reduction in daily scheduling are discovered, and several methods are proposed to overcome the combinatorial explosion problem in the calculation of the Shapley value. The effectiveness of the proposed models is shown with simulation results on a test power system.     

A derived heuristics based multi-objective optimization procedure for micro-grid scheduling

With the availability of different types of power generators to be used in an electric micro-grid system, their operation scheduling as the load demand changes with time becomes an important task. Besides satisfying load balance constraints and the generator's rated power, several other practicalities, such as limited availability of grid power and restricted ramping of power output from generators, must all be considered during the operation scheduling process, which makes it difficult to decide whether the optimization results are accurate and satisfactory. In solving such complex practical problems, heuristics-based customized optimization algorithms are suggested. However, due to nonlinear and complex interactions of variables, it is difficult to come up with heuristics in such problems off-hand. In this article, a two-step strategy is proposed in which the first task deciphers important heuristics about the problem and the second task utilizes the derived heuristics to solve the original problem in a computationally fast manner. Specifically, the specific operation scheduling is considered from a two-objective (cost and emission) point of view. The first task develops basic and advanced level knowledge bases offline from a series of prior demand-wise optimization runs and then the second task utilizes them to modify optimized solutions in an application scenario. Results on island and grid connected modes and several pragmatic formulations of the micro-grid operation scheduling problem clearly indicate the merit of the proposed two-step procedure.     

Optimal maintenance scheduling of power producers considering unexpected unit failure

A novel unit maintenance scheduling (UMS) problem formulation for a generation producer is presented, to maximise its benefit while thoroughly considering the risk associated with unexpected unit failures. First, the unit failure is characterised by a more practical bathtub-shaped failure behaviour from the modified superposed power law process. Its parameters are estimated from the historical data by solving a nonlinear least-squares-fitting problem via Gauss-Newton iteration method. On the basis of the unit failure analysis, the new UMS formulation is solved by a combination of linear programming and genetic algorithms (GAs), and its impact on the producer-s benefit is analysed in detail, including expected profit of selling energy, expected renewal cost of damaged components and maintenance cost. Compared with the current models, the proposed UMS model takes into consideration the influences of market factors as well as unexpected unit failures to strike the right balance between profits and costs related with potential unit failures. Numerical examples on a four-unit producer are utilised to demonstrate the effectiveness of the proposed scheme.     

The development of a crack propagation model for railway wheels and rails

Rolling contact fatigue (RCF) and wear of railway wheels and rails are the main phenomena that affect their maintenance costs. When crack propagation and wear rates can be predicted, maintenance planning can be optimised, and cost‐effective measures can be developed. Several RCF models exist, but none which can be used in combination with vehicle dynamics simulations and can predict the actual crack depth. This study shows the development of a crack propagation model that can be applied for both railway wheels and rails. Two unknown material parameters in the model were calibrated against crack measurements in a curve on the Dutch railways over a period of 5 years. Two different RCF models were used to calculate the stress magnitudes for the propagation model. The propagation model can be used in combination with vehicle‐track dynamics simulations and shows promise in predicting the actual crack depth and/or surface length. Further research is needed to determine the model's validity for other operational conditions.     

Data Analysis for Condition‐Based Railway Infrastructure Maintenance

Condition assessment is crucial to optimize condition‐based maintenance actions of assets such as railway infrastructure, where a faulty state might have severe consequences. Hence, railways are regularly inspected to detect failure events and prevent the inspected item (e.g. rail) to reach a faulty state with potentially safety critical consequences (e.g. derailment). However, the preventive measures (e.g. condition‐based maintenance) initiated by the inspection results may cause traffic disturbances, especially if the expected time to a faulty state is short. The alarm limits are traditionally safety related and often based on geometrical properties of the inspected item. Maintenance limits would reduce the level of emergency, producing earlier alarms and increasing possibilities of planned preventive rather than acute maintenance. However, selecting these earlier maintenance limits in a systematic way while balancing the risk of undetected safety‐critical faults and false alarms is challenging. Here, we propose a statistically based approach using condition data of linear railway infrastructure assets. The data were obtained from regular inspections done by a railway track measurement wagon. The condition data were analysed by a control chart approach to evaluate the possibility for earlier detection of derailment hazardous faults using both temporal and spatial information. The study indicates that that the proposed approach could be used for condition assessment of tracks. Control charts led to earlier fault warnings compared to the traditional approach, facilitating planned condition‐based maintenance actions and thereby a reduction of track downtime. Copyright © 2014 The Authors. Quality and Reliability Engineering International published by John Wiley & Sons, Ltd.     

基于多代理系统的微电网多目标优化调度

微电网在并网情况下的多目标稳定运行是微电网运营和建设的基本要求。从并网型微电网的经济目标和环保目标出发,建立微电网多目标优化模型,其中经济目标考虑分布式电源的燃料费用、运行维护费用、启停费用和与大电网的能量交互费用,环保目标考虑污染气体的排放量。提出一种基于多代理系统（multi-agent system,MAS）的分时电价机制下储能装置的调度策略,并且采用改进的NSGA-Ⅱ算法优化可控微源和储能装置的出力。以某地区微电网单元为例进行仿真,验证了所建模型和改进算法的有效性。试验结果表明,改进的NSGA-Ⅱ算法性能更优,所提出的策略可以控制储能装置一个周期内的充放电次数,延长使用寿命。     

An approach to integrating surveillance and maintenance tasks to prevent the dominant failure causes of critical components

Surveillance requirements and maintenance activities in a nuclear power plant aim to preserve components' inherent reliability. Up to now, predictive and preventive maintenance mainly concerned plant staff, but the US Nuclear Regulatory Commission Maintenance Rule released in July 1991 will have significant impact on how nuclear power plants perform and document this maintenance. Reliability Centered Maintenance (RCM) is a systematic methodology to establish maintenance tasks for critical components in plant with a high degree of compliance with the goals of the Rule. RCM pursues the identification of applicable and efficient tasks to prevent these components from developing their dominant failure causes, and, in turn, towards achieving proper levels of components availability with low cost. In this paper, we present an approach for identifying the most suitable set of tasks to achieve this goal, which involves the integration of maintenance activities and surveillance requirements for each critical component based on the unavailability and cost associated with each individual task which is performed on it.     

Integrated reconfiguration and age-based preventive maintenance decision making

The use of manufacturing system reconfiguration in conjunction with maintenance operations has not been previously reported in the literature. This research attempts to incorporate reconfiguration into Preventive Maintenance (PM) actions for improved system performance in terms of reduced total cost. This paper presents an Integrated Reconfiguration and Age-Based Maintenance (IRABM) policy and applies it to a parallel-serial manufacturing system. The expected total cost of implementing the IRABM policy is estimated and minimized through a simulation-based heuristic optimization procedure. Using this method, it is possible to systematically identify the conditions under which the integration of reconfiguration into maintenance is cost effective. In addition, numerical examples demonstrate that the manufacturing system could have a higher probability of fulfilling production requirements at a lower cost under the IRABM policy compared to the conventional age-based PM policy. The influences of the input parameters associated with reconfiguration, production, and reliability on the performance of IRABM policy also are studied.