In recent years, with the rapid development of high-speed railways (HSRs), power interruptions or disturbances in traction power supply systems have become increasingly dangerous. However, it is often impossible to detect these faults immediately through single-point monitoring or collecting data after accidents. To coordinate the power quality data of both traction power supply systems (TPSSs) and high-speed trains (HSTs), a monitoring and assessing system is proposed to access the power quality issues on HSRs. By integrating train monitoring, traction substation monitoring and data center, this monitoring system not only realizes the real-time monitoring of operational behaviors for both TPSSs and HSTs, but also conducts a comprehensive assessment of operational quality for train-network systems. Based on a large number of monitoring data, the field measurements show that this real-time monitoring system is effective for monitoring and evaluating a traction-network system.

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A power-quality monitoring and assessment system for high-speed railways based on train-network-data center integration

Actual condition-based maintenance for traction power supply equipment (TPSE) is almost based on completely observable equipment state. However, it is unpractical to accurately reveal the equipment state due to the inescapably uncertainty of state assessment. In order to optimize the maintenance of TPSE, a maintenance model based on partially observable Markov decision process is proposed in this paper. Firstly, the degradation process of the TPSE is described by a four-state Markov process, and the state residence time and its transition probability of the equipment are obtained by equaling fault times in the statistical period. Then, the imperfect maintenance is considered in this paper. And the failure risk of the TPSE after maintenance is quantified for optimizing both the economic cost and the reliability of maintenance strategy. Finally, the practical fault record data of 27.5 kV vacuum circuit breakers for a traction power supply system (TPSS) are used to verify the proposed model. The results show that the maintenance model can provide guidance on decision-making for the maintenance under uncertainty, and the determination of maintenance schemes to optimize both TPSE reliability and operational cost. 

Condition-Based Maintenance for Traction Power Supply Equipment Based on Partially Observable Markov Decision Process

In the context of increasing construction of new railways and uninterrupted operation of existing railways in China, maintenance supervisors are facing great challenges to achieve as much maintenance workloads as possible in an efficient and economical way within the required maintenance cycle. Therefore, a cost-effectiveness oriented intelligent maintenance scheduling optimization method for the traction power supply system (TPSS) of high-speed railways (HSRs) is proposed. First, the skill levels of maintenance operators are evaluated by the skill evaluation model, and the workflow model of TPSS maintenance tasks is established to describe the relation between the operator deployment and the maintenance efficiency. Then the bi-objective optimization model which simultaneously maximizes maintenance workload and minimizes cost subject to available resources during a prescribed period is established. A modified bi-objective genetic algorithm is applied to obtain the Pareto solutions of the model and thus optimized maintenance scheduling strategies are formulated. A case study dealing with the maintenance scheduling optimization of a catenary system in China shows that, compared with the current scheme, the proposed strategy can effectively improve maintenance efficiency and meanwhile reduce cost. Furthermore, a prototype of an intelligent maintenance scheduling system (IMSS) for HSR TPSS is developed. The application of IMSS with field data indicates it can help operators formulate highly efficient yet economical maintenance strategies, and meanwhile motivate the transition of current maintenance decision-making policy from traditional experience-based strategies towards scientific “cost-effective” ones.

Cost-Effectiveness Oriented Intelligent Maintenance Scheduling Optimization for Traction Power Supply System of High-Speed Railway

In the context of increasing construction of new railways and uninterrupted operation of existing railways in China, maintenance supervisors are facing great challenges to achieve as much maintenance workloads as possible in an efficient and economical way within the required maintenance cycle. Therefore, a cost-effectiveness oriented intelligent maintenance scheduling optimization method for the traction power supply system (TPSS) of high-speed railways (HSRs) is proposed. First, the skill levels of maintenance operators are evaluated by the skill evaluation model, and the workflow model of TPSS maintenance tasks is established to describe the relation between the operator deployment and the maintenance efficiency. Then the bi-objective optimization model which simultaneously maximizes maintenance workload and minimizes cost subject to available resources during a prescribed period is established. A modified bi-objective genetic algorithm is applied to obtain the Pareto solutions of the model and thus optimized maintenance scheduling strategies are formulated. A case study dealing with the maintenance scheduling optimization of a catenary system in China shows that, compared with the current scheme, the proposed strategy can effectively improve maintenance efficiency and meanwhile reduce cost. Furthermore, a prototype of an intelligent maintenance scheduling system (IMSS) for HSR TPSS is developed. The application of IMSS with field data indicates it can help operators formulate highly efficient yet economical maintenance strategies, and meanwhile motivate the transition of current maintenance decision-making policy from traditional experience-based strategies towards scientific “cost-effective” ones. 

The traction power supply system is the key facility that provides electrified railways with power. However, since most equipment operates outdoors, the safe operation of the system is susceptible to meteorological factors. In order to quantify the system risks brought by severe weather, this paper proposes a risk assessment method for traction power supply system considering the influence of multiple meteorological factors. Firstly, the severe weather is modeled to describe its intensity and moving track. Then, by integrating the weather intensity distribution and the equipment failure rate function, physical field-based metrics are defined to characterize the spatiotemporal distribution and variation of system risk probability. On this basis, considering both the economic loss and operating time loss, system risks during each period are quantified. A case study is presented on the traction power supply system of a part of Beijing-Shanghai high-speed railway. It shows that the system risk exhibits remarkable spatiotemporal differences impacted by severe weather, which can be well captured by the proposed method. In the advent of adverse weather events, evaluation results enable on-site operators to timely grasp system risk conditions, identify system critical components, and thus take proactive measures to prevent or mitigate risks. 

Risk Assessment for Traction Power Supply System Considering the Influence of Multiple Meteorological Factors

Conventional maintenance mode for the traction power supply system (TPSS) is to perform scheduled regular maintenance activities for power supply equipment, while such maintenance mode may result in undue maintenance tasks and low efficiency due to different degradation processes of different sorts of equipment. To address this problem, this paper introduces a preventive opportunistic maintenance (POM) method for TPSS based on equipment reliability. Firstly, a POM model is established by considering the equipment reliability degradation process based on Weibull distribution. Then, by considering the total power outage time in the planned operation cycle of TPSS as the optimization objective, the optimal maintenance scheme of TPSS is formulated by iterative method of maintenance strategies. The proposed method is verified by introducing practical maintenance strategies and fault record data of the traction transformer, circuit breaker and disconnector in an actual TPSS of a railway administration. Results show that the presented method can make full use of the existing fault data to develop a POM scheme for TPSS. It can improve maintenance efficiency and reduce power outage time, providing guidance to formulate scientific maintenance strategies for TPSS.

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Chao Yang

Papers

A preventive opportunistic maintenance method for railway traction power supply system based on equipment reliability