IET Electrical Systems in Transportation最新文献

The accurate estimation of the probability distribution of traction current harmonics in EMUs is crucial for preventing electromagnetic interference (EMI), managing high-speed railway signaling systems, and analyzing traction system power quality. Although kernel density estimation (KDE) has been a longstanding flexible, nonparametric estimation method, it faces significant challenges, including high computational demands and difficulties in selecting appropriate bandwidths. This article introduces the Gaussian mixture model (GMM) as a novel approach for the probabilistic estimation of traction current harmonics, thereby providing a scientific foundation for harmonic analysis. To improve the precision and efficiency of harmonic interference assessments, we initialize the GMM parameters and the expectation-maximization (EM) algorithm with the density-based spatial clustering of applications with noise (DBSCAN). The effectiveness of our method is confirmed through the Kolmogorov–Smirnov (K–S) goodness-of-fit test, root mean square error (RMSE), and R² statistics, demonstrating that our approach provides greater stability and faster computation than existing methods.

The rapid adoption of electric vehicles (EVs) poses new challenges for both transportation networks and power distribution systems. To address these issues, effective EV routing strategies are essential to minimize grid stress and ensure efficient energy utilization. This research proposes an optimal EV routing by incorporating user-centric parameters and coordination of demand response management (DRM) with distributed generation (DG), facilitating effective synergy between user preferences and grid operational reliability. A loopless route is formulated considering distance and travel time (TT) to minimize the routing cost using Yen’s algorithm (YA). The optimized route, integrated with DG–DRM, ensures the minimization of power loss (PL) and the maximization of customer benefit (CB) using population-based incremental learning (PBIL) algorithm. To enable effective coordination between EV routing and DRM in the distribution network, the Monte Carlo sampling method is employed to validate stochastic traffic and load variations. IEEE-33 bus and the Indian utility power system (IUPS) network comprising 17 busses, are taken as test systems. The proposed methodology is compared with other soft computing techniques, and the findings demonstrate its superiority by achieving a 12.3% reduction in routing cost (RRC), a 16.74% reduction in PL, and a 22.31% increase in CB.

In addressing the issue of electromagnetic interference in railway environments, research into the coupling pathways, amplitude distribution patterns, and attenuation characteristics of such interference is crucial for identifying practical solutions. Rails, a common channel for signaling and traction power supply systems, play a pivotal role in anti-interference design. However, due to their ferromagnetic material properties and irregular H-shaped cross-section, accurately defining their frequency-dependent impedance has been challenging, leading to inaccuracies in the electrical modeling of railway stations. This paper proposes a segmented modeling approach for rails, analyzing and deriving the internal impedance calculation formulas based on the distinct shapes of each rail segment. Taking the working mode of track circuits as an example, the influence of external circuit loop impedance is considered, culminating in the development of a computational model for the frequency-dependent impedance of rails. Using the 60 kg/m rail as an example, the model’s impedance calculation accuracy at specific frequency points deviates by less than 6% from standard errors. Additionally, leveraging the ANSYS platform, the finite element simulation method was employed to simulate the frequency-dependent internal impedance of rails below 100 kHz. The results showed a high degree of agreement with the internal impedance parameters derived from the model, thereby validating the model’s accuracy within the frequency range below 100 kHz under long rail conditions. This model enhances the precision of station modeling, particularly in improving the accuracy of interference amplitude distribution and attenuation characteristic calculations.

Due to their nonbackup characteristics and constant exposure to outdoor conditions, the performance of overhead contact lines (OCLs) will gradually degrade over time and further result in equipment defects or frequent failures. These issues significantly impact system availability and incur substantial repair costs. To tackle these issues, this paper proposes a stochastic colored Petri net (SCPN) model to evaluate the availability of OCLs and estimate the maintenance costs, simultaneously simulating the degradation, failure, inspection, and maintenance processes of critical components and the overall system. Firstly, this model encompasses the nature of a multiple-stage deterioration process and various maintenance actions available for OCLs. A four-state transition diagram is developed to capture the intricate dependencies involved. Moreover, a subnet is formulated using SCPN to represent the four-state transition for critical components, which are described by nine tuples. Additionally, a system model is developed by integrating the subnets of OCL components. To improve simulation speed, an accelerated Monte Carlo simulation algorithm is devised to handle the analytical solution for the complex integration associated with performance transitions. Finally, the proposed approach is demonstrated by its application to an actual high-speed railway line, showcasing its effectiveness in addressing the degradation and maintenance challenges of OCLs.

In the prediction of traction loads for electrified railways, conventional forecasting methods often focus exclusively on temporal correlations within historical data from individual substations. However, traction loads are profoundly affected by train schedules and exhibit substantial spatial interdependence across different substations. To address this limitation, this study proposes a hybrid model integrating a graph convolutional network (GCN) and a bidirectional long short-term memory (BiLSTM) network, which comprehensively incorporates both spatial and temporal dependencies to significantly improve ultrashort-term prediction accuracy. The proposed framework operates in several stages. First, spatial correlations among regional substations are captured using a GCN. To mitigate the risk of including spurious connections—often referred to as “pseudo-adjacency” relationships—the adjacency matrix is refined using Pearson correlation coefficients, thereby strengthening the model’s representation of meaningful spatial interactions. The spatial features extracted by the GCN at consecutive time steps are then organized into a temporal sequence and input into the BiLSTM module. To further enhance temporal modeling, an attention mechanism is incorporated to adaptively weigh the importance of hidden states, enabling the model to focus on the most relevant temporal information. This integrated approach results in a notable improvement in the accuracy of traction load power forecasting. Results from case studies demonstrate that the proposed model, with appropriately configured spatiotemporal parameters, achieves superior prediction accuracy. This finding underscores the necessity of incorporating spatiotemporal characteristics for traction load forecasting.

This study presents a dual-active-bridge (DAB) LC resonant DC–DC converter for battery energy storage systems. The proposed converter adds an auxiliary bridge arm to the traditional full-bridge (FB) LC resonant converter as a boost arm. It features two efficient operating modes: a charging low-gain (CLG) mode and a discharging high-gain (DHG) mode. In CLG mode, the converter operates as an FB resonant PWM converter to achieve step-down functionality. In DHG mode, the boost arm is used for energy storage to increase the voltage gain. Both modes enable soft-switching operation across the entire load range. Additionally, the converter operates at a fixed switching frequency, simplifying the design of magnetic components. The converter has little magnetizing current and circulating current to increase the efficiency. The resonant capacitor in the converter has lower voltage stress and the transformer without air gap has lower leakage magnetic field, contributing to high power density. A prototype was developed, with batteries voltage of 40–60 V and high voltage DC bus of 360 V. Experimental results validate the feasibility of the proposed converter.

In order to improve the electromagnetic torque and stability of the drive motor for belt conveyors, and considering economic benefits, two ferrite assisted double-layer nonuniform Halbach array consequent-pole (DNHC-permanent magnet synchronous motors[PMVM]) are proposed: DNHC-PMVMA and DNHC-PMVMB. The main magnetic pole of DNHC-PMVM rotor adopts DNH rare earth permanent magnet, and ferrite is used as the auxiliary magnetic pole of stator and rotor. The rationality of the proposed structure is verified by comparing and analyzing PMVM, DNHC-PMVMA, and DNHC-PMVMB by finite method. In order to further optimize the motor structure, the cuckoo search (CS) grey wolf optimization (CSGWO)algorithm is improved, and the improvement strategies such as circle chaotic mapping are introduced. After multiobjective optimization test, it is proved that the comprehensive performance of improved CSGWO (ICSGWO) is better than that of CSGWO, GWO algorithm, particle swarm optimization (PSO), and other algorithms. Based on the response surface method (RSM), ICSGWO, and parameter scanning, the three motor structures are optimized, respectively. The finite element method is used to analyze the three optimized motors. The results show that the performance parameters such as electromagnetic torque and torque ripple are significantly improved, which verifies the effectiveness of the optimization method. Meanwhile, the performance parameters of DNHC-PMVM are significantly better than those of PMVM, which proves the superiority of the proposed structure.

This article discusses voltage level modifications in urban mass transit traction substations, focusing on DC railway substations, to reduce power consumption and improve energy efficiency. Substation voltage settings are usually adjusted by a skilled designer using practical judgment and design acumen. To maximize operations, all traction substation voltage levels are automatically adjusted to the same value. This arrangement works well and may not have affected the power supply system. This design often causes operations to deviate from optimal performance, perhaps reducing energy efficiency. This research seeks to determine the optimal traction substation voltage setting that minimizes total energy consumption of DC electric railways. A simulation-based approach is applied using train movement data and voltage variation scenarios. The proposed designs are linear, V-shaped, and fixed-voltage. Additionally, particle swarm optimization (PSO) is an effective way to find the best design. The Bangkok Transit System (BTS) Sukhumvit line is used for testing. Reduction by the linear framework, energy consumption may be 2.341% lower than the base case. By the PSO, the results in 30 trial test runs suggest a 6.107% energy consumption reduction from baseline.

This article introduces simultaneous control of oscillations in voltage and frequency within a single-area power system that includes hydrogen energy and electrical vehicles as source. The study focuses on the critical roles played by Automatic Voltage Regulator (AVR) and Automatic Generation Control (AGC) loops in maintaining frequency and voltage stability. The article incorporates renewable energy sources (RESs) in this investigation, like photovoltaic (PV) systems, fuel cells (FCs), and aqua electrolyzers (AEs) into the power grid. Energy storage and electric vehicle integration have also been included in the research to see how they affect the reduction of frequency and voltage oscillations. This study also examined the impact of communication time delays (Tds), which may be the cause of system instability in real-power systems. The proportional integral derivative (PID) controller is selected as a subsidiary controller for the combined study of AGC and AVR, and its efficacy in terms of operation is contrasted with classical I and PI controllers and other control techniques from the literature. A recently developed Secretary Bird Optimization (SBO) algorithm is selected for obtaining the parameters of the controller. This article contributes valuable insights into power system stability enhancement.

Electric vehicles (EVs) present an efficient solution for reducing greenhouse gas (GHG) emissions and enhancing grid power quality. They offer multiple advantages over traditional internal combustion engines (ICEs), including lower emissions, reduced dependance on oil, higher energy efficiency, quieter operation, zero emissions, and improved air quality by minimizing the release of toxic chemicals into the atmosphere. However, there is a lack of literature that comprehensively reviews the factors that can facilitate the assimilation of EV technology. Therefore, this paper provides a comprehensive review of EV technologies, focusing on the growth of global EV adoption and the various types of EVs, including all-EVs and hybrid EVs (HEVs). The comparative analysis of different HEV technologies is presented, covering full HEVs, mild HEVs, and plug-in HEVs (PHEVs). The paper also discusses the different classifications of HEVs based on electrification level and energy source, along with a comparative analysis of their configurations. Furthermore, the EV architecture is examined, with a specific focus on electric motors, battery management systems (BMSs), batteries, and charging technologies, including conductive and wireless charging systems. The challenges in EV charging and the associated charging standards are also addressed. The paper concludes by highlighting the need for the advancement of EV technologies and infrastructure to overcome the significant barriers to rapid EV adoption, while demonstrating how smart grid technologies enhance EV charging efficiency, grid resilience, and energy sustainability.