High-speed Railway Traction System Research Articles

Improved deep PCA and Kullback–Leibler divergence based incipient fault detection and isolation of high-speed railway traction devices

Enhancing the reliability of high-speed railway traction system is critical important to the safety of entire trains. Data-driven based FDD (Fault Detection and Diagnosis) schemes focused on electric locomotive traction system have received more and more attention. An improved Deep PCA (Principal Components Analysis) algorithm is presented in this paper, based on which, a KLD (Kullback–Leibler Divergence) based incipient FDD scheme is proposed at the same time. The main contributions are summarized as: (i) The improved Deep PCA algorithm by using the covariance matrix of the dataset (not the original dataset as references) can highlight more useful incipient fault information with much faster data decomposition; (ii) The diagnosis scheme based on KLD can improve the accuracy of non-Gaussian process; (iii) The study proposed in this paper can realize the update of fault database and the diagnosis of unknown type of incipient faults. The experiment results performed on TDCS-FIB (Traction Drive Control System-Fault Injection Benchmark) platform demonstrate the superiority of the proposed data-driven based incipient FDD scheme in comparison with the original Deep PCA algorithm.

Deep PCA-Based Incipient Fault Diagnosis and Diagnosability Analysis of High-Speed Railway Traction System via FNR Enhancement

In recent years, the data-driven based FDD (Fault Detection and Diagnosis) of high-speed train electric traction systems has made rapid progress, as the safe operation of traction system is closely related to the reliability and stability of high-speed trains. The internal complexity and external complexity of the environment mean that fault diagnosis of high-speed train traction system faces great challenges. In this paper, a wavelet transform-based FNR (Fault to Noise Ratio) enhancement is realised to highlight incipient fault information and a Deep PCA (Principal Component Analysis)-based diagnosability analysis framework is proposed. First, a scheme for FNR enhancement-based fault data preprocessing with selection of the intelligent decomposition levels and optimal noise threshold is proposed. Second, fault information enhancement technology based on continuous wavelet transform is proposed from the perspective of energy. Further, a Deep-PCA based incipient fault detectability and isolatability analysis are provided via geometric descriptions. Finally, experiments on the TDCS-FIB (Traction Drive Control System–Fault Injection Benchmark) platform fully demonstrate the effectiveness of the method proposed in this paper.

Implementation of Multi-Modular Converter with Droop characteristics for High Speed Railway Traction System

Multi Modular Converters (MMC) plays a significant role in high voltage power transmission networks due to its high scalability and modularity characteristics. MMC converters produce better efficiency & show superiority in harmonic performance in high voltage applications like HVDC and high speed AC-Traction power supply systems. Its bidirectional operation also aided to get high installation reliability comparatively. The complicated power quality problems such as voltage fluctuations, Harmonic issues and reactive power problems of railway tracks due to the rapid movement of the locomotives need to be mitigated. The loss of speed is caused due to the neutral sections (NS) which are present throughout the power line. It is necessary that the neutral sections should be eradicated especially for high speed railway networks. In this paper, a new traction power transmission network which comprises zero NS and comparatively low power quality problems is designed. Droop control strategy is implemented to improve power transfer capability between the two adjacent traction substations. The implementation of MMC with parallel converter concept using the DSP processor is executed for low voltage as a prototype model. The results obtained are confirmed that the proposed system is very much suitable for high speed railway networks. Index Terms: High speed traction, Parallel inverters, Droop characteristics, Indian traction power supply system.

Analysis and Comparison of Modular Railway Power Conditioner for High-Speed Railway Traction System

With the rapid development of modern electrified railway, negative-sequence current minimization is one of the most important considerations in the high-speed railway traction system. In the past, many multiple or multilevel topologies with high compensation capacity have been introduced for railway power conditioner (RPC). This paper presents a simplified quantitative comparison of five previous modular RPC topologies for negative sequence compensation in V/V and SCOTT traction systems, aiming for an optimal selection of the compensators. Performance criteria such as transformer requirement, voltage stress and current stress of a power switch, numbers of the power switches and capacitor are derived by analytical methods. Moreover, the numerical comparison of operating controllers is completed for modular RPCs. In addition, power losses of five modular RPCs are obtained by theoretical analysis, IPOSIM calculation as well as PSIM simulation. These calculations are validated via simulations results in PSIM. The main conclusion is that presented modular RPCs can be divided into general purpose RPC and special purpose RPC in terms of the behavior and efficiency. It is helpful to choose the appropriate topology for specific applications.

Wavelet Entropy-Based Traction Inverter Open Switch Fault Diagnosis in High-Speed Railways

In this paper, a diagnosis plan is proposed to settle the detection and isolation problem of open switch faults in high-speed railway traction system traction inverters. Five entropy forms are discussed and compared with the traditional fault detection methods, namely, discrete wavelet transform and discrete wavelet packet transform. The traditional fault detection methods cannot efficiently detect the open switch faults in traction inverters because of the low resolution or the sudden change of the current. The performances of Wavelet Packet Energy Shannon Entropy (WPESE), Wavelet Packet Energy Tsallis Entropy (WPETE) with different non-extensive parameters, Wavelet Packet Energy Shannon Entropy with a specific sub-band (WPESE3,6), Empirical Mode Decomposition Shannon Entropy (EMDESE), and Empirical Mode Decomposition Tsallis Entropy (EMDETE) with non-extensive parameters in detecting the open switch fault are evaluated by the evaluation parameter. Comparison experiments are carried out to select the best entropy form for the traction inverter open switch fault detection. In addition, the DC component is adopted to isolate the failure Isolated Gate Bipolar Transistor (IGBT). The simulation experiments show that the proposed plan can diagnose single and simultaneous open switch faults correctly and timely.

Bayesian Network Based Fault Prognosis via Bond Graph Modeling of High-Speed Railway Traction Device

Reliability of the traction system is of critical importance to the safety of CRH (China Railway High-speed) high-speed train. To investigate fault propagation mechanism and predict the probabilities of component-level faults accurately for a high-speed railway traction system, a fault prognosis approach via Bayesian network and bond graph modeling techniques is proposed. The inherent structure of a railway traction system is represented by bond graph model, based on which a multilayer Bayesian network is developed for fault propagation analysis and fault prediction. For complete and incomplete data sets, two different parameter learning algorithms such as Bayesian estimation and expectation maximization (EM) algorithm are adopted to determine the conditional probability table of the Bayesian network. The proposed prognosis approach using Pearl’s polytree propagation algorithm for joint probability reasoning can predict the failure probabilities of leaf nodes based on the current status of root nodes. Verification results in a high-speed railway traction simulation system can demonstrate the effectiveness of the proposed approach.

Optimal harmonic filters design of the Taiwan high speed rail traction system of distributer generation system with specially connected transformers

This paper presents a method for combining a particle swarm optimization with nonlinear time-varying evolution and orthogonal arrays (PSO-NTVEOA) in the planning of harmonic filters for the high speed railway traction system of Distributed Generation (DG) system with specially connected transformers in unbalanced three-phase power systems. The objective is to minimize the cost of the filter, the filters loss, the total harmonic distortion of currents and voltages at each bus simultaneously. An orthogonal array is first conducted to obtain the initial solution set. The set is then treated as the initial training sample. Next, the PSO-NTVEOA method parameters are determined by using matrix experiments with an orthogonal array, in which a minimal number of experiments would have an effect that approximates the full factorial experiments. This PSO-NTVEOA method is then applied to design optimal harmonic filters in Taiwan HSR traction system, where both rectifiers and inverters with IGBT are used. From the results of the illustrative examples, the feasibility of the PSO-NTVEOA to design an optimal passive harmonic filter of Taiwan HSR system is verified and the design approach can greatly reduce the harmonic distortion. Three design schemes are compared that V–V connection suppressing the 3rd order harmonic and Scott connection for the harmonic improvement is better than those of V–V and Le Blanc connection.

IPMSM이 적용된 차세대 고속철도 견인시스템의 전류 및 속도 제어기 설계

본 논문에서는 차세대 고속철도에 적용하기 위한 IPMSM 견인시스템의 전류 및 속도제어기를 설계한다. 차세대 고속철도는 동력분산식 시스템으로 개발되며 고효율화를 위해 IPMSM 적용 견인전동기를 사용하였고 전동기 제어를 위해 벡터제어 기법을 사용한다. 이 때 벡터제어 루프내 제어기 이득 값은 IPMSM의 과도특성과 속도제어 능력에 영향을 미치기 때문에 알맞은 이득 설계가 요구된다. 본 논문에서는 IPMSM 적용 차세대 고속철도용 견인전동기의 제어기를 설계하여 Matlab/Simulink 기반 모의시험 프로그램을 개발하고 이를 이용한 시험 결과를 분석하였다.

Mayr's Equation-Based Model for Pantograph Arc of High-Speed Railway Traction System

This letter presents an extended Mayr's equation-based model for the pantograph detachment arc of the high-speed railway traction system. The proposed model is composed of Mayr's model for arc behavior control and time-controlled switches for starting pantograph detachment and reattachment events, which are developed by MODELS language and TACS in Electromagnetic Transients Program/Alternate Transients Program. With the combination of a traction motor drive circuit, an effective simulation is performed to observe the harmonic spectrum of pantograph current that is caused by pantograph detachment arcs. By comparing this with the actual measurements for validation, it shows that the proposed arc model is useful for estimating the harmonic frequency range of the pantograph arc current and for power-quality studies.