Traction Power Supply Equipment Research Articles

Development of the system of coordinated control of traction power supply equipment and electric rolling stock

The aim of the study is to develop a system of coordinated control of traction power supply equipment and electric rolling stock based on self-organization principles. The research methods of the study are based on multi-agent approach and include coordination of settings of static equipment of reactive power compensation, on-load tap changing of traction transformers, other voltage regulators in the contact network, microprocessor protections, modes of operation of electric locomotives in the interstation area. The obtained results made it possible to substantiate the model of self-organizing control system of traction power supply equipment and electric rolling stock, including the description of interaction and coordination principles, attributes, ontology, algorithms of the efficiency of local regulators of traction network and electric locomotives. The study presents the description of the information model of the traction power supply system and electric rolling stock to control modes and increase the throughput capacity of the power supply system by means of optimum allocation of traction resources. The results of the simulation modeling have confirmed the effectiveness of the proposed solutions.

Improvement of the information-measuring complex for diagnostics of traction power supply objects at high-speed traffic

This paper considers the improvement of the information-measuring complex for diagnostics and monitoring of traction power supply equipment under high-speed traffic, which has several features associated with a multilevel hierarchical structure, spatial distribution of objects, energy modes and functional relationships. The analysis of the transmission and conversion of measuring analogue information into a digital one, considering the maximum frequency of the primary signal spectrum components according to the sampling and quantization parameters to determine the time of the signal converted into a digital form is made. Functional dependencies and an algorithm for measuring information processing, considering the multilevel structure of traction power supply, which allow increasing the reliability and accuracy of diagnosing the main objects in high-speed traffic, are obtained.

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

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.

Research on Optimal Nonperiodic Inspection Strategy for Traction Power Supply Equipment of Urban Rail Transit Considering the Influence of Traction Impact Load

The inspection and maintenance work of traction power supply equipment (TPSE) is growing heavy with the construction and operation of a large number of urban rail transits (URTs). In order to improve the existing regular preventive maintenance mode and implement a “high-efficiency with low-cost” maintenance strategy, this article proposes an optimal nonperiodic inspection decision-making method for TPSE of URT considering the influence of impact load. First, the comprehensive degradation model of TPSE is established based on the linear Wiener process and the compound Poisson process. Second, the nonperiodic inspection model of TPSE in a single-maintenance period is established by introducing inspection planning functions for three types of degradation processes. With the lowest operating cost per unit time as the decision goal, the optimal maintenance plan is, thus, achieved. Finally, the proposed decision-making method is applied to the benchmark data taken from a URT administration in China related to a dry-type traction transformer in five years. The results show that the proposed method can help formulate efficient inspection strategies with the optimal cost. In addition, sensitivity analysis reflects the influence of different parameters on the optimization results, which helps to identify which parameter exerts the largest influence.

Optimization Method With Prediction-Based Maintenance Strategy for Traction Power Supply Equipment Based on Risk Quantification

A prediction-based maintenance (PBM) strategy can save resources while simultaneously guaranteeing the traction power supply system (TPSS) operates in a stable and reliable condition. Based on risk quantification, this paper presents a method to assess maintenance needs and optimize the PBM strategy for traction power supply equipment (TPSE). This method utilizes historical maintenance information and the fault record of the TPSE as inputs to a fault prediction model based on a Bayesian classifier. By comparing the prediction results with the actual equipment operation state, statistical parameters are obtained to calculate the reliability and economic indices for the maintenance strategies. The comprehensive maintenance risk is then quantified by integrating the dynamic inspection and fault risk costs for the TPSE. The minimum comprehensive risk is selected as the optimization objective, and the criterion for fault prediction spurs from the optimization results are used to form the PBM strategy. Practical maintenance information and fault record data of 27.5-kV vacuum circuit breakers for a TPSS are used to verify the proposed method. The results show that, by fully utilizing the current data, this method can predict equipment faults by considering multiple factors, and it can quantify the risk for a particular PBM strategy, which is also applicable to similar large-scale traction power supply facilities. To improve the PBM strategy, the approach optimizes fault prediction criteria to achieve a minimum comprehensive maintenance risk. The proposed method can provide effective data and confidence for decision makers in implementing PBM for TPSS.

Reliability assessment for traction power supply system based on Quantification of Margins and Uncertainties

The traction power supply is a crucial part of high-speed rail network. A failure of the traction power supply system may lead to delay or outage of high-speed trains that will not only have direct impact on the entire operation schedule but will also cause tremendous losses. Therefore, the reliability assessment of the traction power supply system is extremely important to ensure the transportation quality of high-speed trains and improve the economy. This paper presents a reliability evaluation method for the traction power supply system based on Quantification of Margins and Uncertainties (QMU). Firstly, the characteristics of the traction power supply equipment and system are combined to establish the observation list and then the performance channel for the equipment and system is determined. Finally, the confidence factor is calculated and the reliability of the equipment and system quantified by the quantification of the performance margin and uncertainty. In order to validate the effectiveness of the proposed method, simulations are conducted using practical operation data from a China traction power supply system. The simulation results demonstrate that the proposed reliability assessment method can account for the uncertainty and quantify the system reliability index. The proposed method has good applicability and can be used to effectively evaluate the reliability of the traction power supply system.

Failure Risk Interval Estimation of Traction Power Supply Equipment Considering the Impact of Multiple Factors

Quantifying the failure risk of traction power supply equipment (TPSE) is of significant importance to the safe and stable operation of the traction power supply system. This paper proposes a method for conducting failure risk interval estimation for the TPSE while accounting for the impact of multiple factors. This method consists of two parts, i.e., the estimation of failure probability interval, and the analysis and quantification of the failure consequences: 1) based on the historical statistical data of equipment failure, a credal network model of TPSE is constructed. By combining the imprecise Dirichlet model and the inference algorithm of credal network, the probability interval of equipment failure is estimated for a given operational condition and 2) the risk-based failure mode and effects analysis table is established for qualitatively analyzing the failure risk of TPSE. This, coupled with the consideration of equipment failure level, enables the quantification of failure consequences from the perspectives of both economic loss and time loss. Finally, the estimation of equipment’s failure risk interval is obtained by integrating the failure probability interval and the quantitative consequences. A case study is conducted utilizing the failure samples of contact mesh equipment derived from the practical power supply section, so as to verify the validity and usability of the proposed risk interval estimation method and the corresponding indices.

High-Speed Railway Traction Power Supply Equipment Condition-Based Maintenance Decision Model Based on Reliability

Traction power supply equipments are the core part of the high-speed railway, so the maintenance of the power supply system is crucial for the security of the railway. Based on the research on the durable years of the traction power supply equipments, this paper puts forward the model of testing the traction power supply equipments of the reliable high speed railway. Some practical researches have proved that the model testing the traction power supply equipments of the reliable high speed railway can make the right decisions of detecting the failures of the equipments, so that a lot of time can be saved and much or insufficient maintenance can be avoided.

High-Speed Railway Traction Power Supply Equipment Condition-Based Maintenance Decision Model Based on Abrasion Analysis

According to abrasion process of machine equipment parts of overhead line system for traction power supply and characters of wear-out failures, considering on factor changes of all kinds of production conditions in practical opera- tion, set permit of abrasion loss in coordinating with normal distribution, and abrasion loss of parts as random variables, and then establish the relation between maintenance costs and random variable of abrasion loss with the principle of low- est maintenance cost, thus concluding with the optimum maintenance abrasion value. Then the functional relation between abrasion loss and running time can be concluded with the gray model, thus the decision model in the optimum period with the lowest maintenance cost can be concluded.

Searching the Cause and Elimination Method of the Tripping of Traction Power Supply

The tripping of traction power supply is the direct embodiment of the bad running condition of the traction power supply. Equipment which threat the safe operation of traction power supply equipment. So quickly organizing to find out the cause of the tripping of traction power supply and find the real reason is of great significance to eliminate hidden dangers and guide accident repair.