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Dynamic interactions among the AC railway power supply network and power electronic converters feeding the trains can result in low-frequency oscillation (LFO) of the catenary voltage, leading to a power outage of the substation and the shutdown of train traffic. To determine the low-frequency stability of the railway traction power systems, the impedance of the power supply network and the total differential admittance of the trains are required. This paper addresses the development of an analytical small-signal model of the train input admittance. For this purpose, small-signal models of each dynamic element involved are obtained. Specifically, the small-signal vector transformation from the actual dq-frame to the estimated dq̂-frame is presented to model the dynamics due to errors in the coordinate rotation of the single-phase four-quadrant converter (4QC) control system. Furthermore, the quadrature signal generator second-order generalized integrator (QSG-SOGI) model is calculated in the synchronous frame. The developed admittance model is intended to accurately predict various types of instabilities and serve as a powerful tool for conducting sensitivity analyses. The validation of the proposed models will be carried out through numerical simulations involving the power supply network and train systems.

Monitoring and fault detection can be critical for efficient, safe and reliable operation of electric drive systems. Unfortunately, developing accurate physics-based models for these tasks is difficult due to unknown machine parameters and incomplete knowledge of the physical phenomena occurring within the system. Machine Learning (ML) methods can learn the system’s behavior from data without requiring explicit models. However, expert knowledge of the system is still crucial to extract useful features before applying ML models. This paper presents an overview of the use of ML and data visualization methods for condition monitoring of inverter fed induction motors. More specifically, stator winding temperature estimation and insulation degradation are considered. The analyzed methods make use of the signals normally available in electric drives. Time and frequency-based approaches are considered. The developed methods are assessed on an experimental test bench. The paper is intended to bridge ML and electric drive domains. The desired outcome of this work is to provide useful guidelines for researchers in the electric drives field who aim to apply modern ML and data visualization techniques for monitoring and fault detection.

The performance of Permanent Magnet Synchronous Machines (PMSMs) is significantly influenced by the temperature of the Permanent Magnets (PMs). Therefore, accurate knowledge of PM temperature is necessary for control and monitoring purposes. As temperature rises, the magnetic flux strength of PMs, and consequently the torque production capability of PMSMs, diminishes. Moreover, there is a risk of irreversible demagnetization of the PMs. In the case of Variable Leakage Flux PMSMs (VLF-PMSMs), the temperature impacts the machine’s variable leakage property, potentially compromising the accuracy of torque control. The current state-of-the-art PM temperature estimation methods are unsuitable for VLF-PMSMs due to their variable leakage PM flux characteristics. This paper addresses the limitations of existing PM temperature estimation methods by incorporating the variable leakage PM flux property of VLF-PMSMs. The proposed method utilizes PM flux linkage derived from the machine’s response to a small-amplitude, low-frequency, quasi-square-wave current signal. This signal is superimposed onto the fundamental excitation, enabling online temperature estimation without altering the machine’s operation.

Governments have recently been dedicating relevant funds to cope up with the inevitable transition to sustainable mobility aiming for a greener transportation sector. This scenario is backed up by the deteriorating global energy crisis, which is predicted to hasten the transition to sustainable energy. Focus has been given to railway systems being globally considered as a tractor project for promoting the use of green and renewable energy by helping build the required infrastructure. As a result, a high tendency for integrating onboard energy storage systems in trains is being observed worldwide. This article provides a detailed review of onboard railway systems with energy storage devices. In-service trains as well as relevant prototypes are presented, and their characteristics are analyzed. A comprehensive study of the traction system structure of these vehicles is introduced providing an overview of all the converter architectures used, categorized based on the type of onboard energy storage device on the train. The current situation of hydrogen fuel cells in railway systems is presented as well, highlighting consistent tendencies. This article also provides a glimpse into commercial battery and fuel cell products used on operating trains.

Torsional vibration is an oscillation phenomenon occurring at driven railway vehicle wheelsets. As the resulting dynamic stresses can be significantly larger than the maximum static motor torque, axle and press fit are at risk of failure. To prevent dangerous vibration events and with these, press fit and axle from failure, traction drive manufactures nowadays used to implement vibration suppression algorithms in drive controls. In this paper, the effectiveness of such suppression algorithms is analyzed. Furthermore, as a pilot survey, we analyze to what extend traction controls influence the excitation of torsional vibration.

The use of the common mode current for stator winding insulation condition assessment has been extensively studied. Two main approaches have been followed. The first models the electric behavior of ground-wall insulation as an equivalent RC circuit; these methods have been successfully applied to high-voltage high-power machines. The second uses the high frequency of the common mode current which results from the voltage pulses applied by the inverter. This approach has mainly been studied for the case of low-voltage, inverter-fed machines, and has not yet reached the level of maturity of the first. One fact noticed after a literature review is that in most cases, the faults being detected were induced by connecting external elements between winding and stator magnetic core. This paper presents a case study on the use of the high-frequency common mode current to monitor the stator insulation condition. Insulation degradation occurred progressively with the machine operating normally; no exogenous elements were added. Signal processing able to detect the degradation at early stages will be discussed.

Validation of high-power converters at the development stage often cannot be performed using the real load. A testbench consisting of the Equipment Under Test (i.e., tested power converter) and an auxiliary power converter, preferably using power recirculation, can be used instead. A challenge for correct testing using this approach comes from the fact that the harmonic content of the currents can significantly differ between the testbench and the real system. This might strongly affect the loading of power switching devices compromising the reliability of the validation process. This paper proposes a methodology to select the operating conditions of both tested and auxiliary converters in the testbench to resemble a given operating condition of the actual system. Since identical behavior is not generally possible, a cost function will be used to trade-off among different factors (i.e., fundamental current magnitude and phase, harmonic content, etc.). The proposed approach evaluates a large number (hundreds of thousands) of testbench operating points in a few seconds and selects those which minimize the cost function. In a later stage, the selected points can be validated using detailed dynamic simulation. The selected conditions can then be used to test the converter in the testbench emulating the real system behavior in a given operating point condition. The proposed methodology was developed for railway electric drives but can be extended to other applications.

Dynamic interactions among the AC railway traction network and power electronics converters feeding the trains have been reported to cause low-frequency oscillations (LFO) of the catenary voltage and current. This can result in railway system instability, eventually leading to a power outage and the shutdown of the train traffic. To avoid LFO, control of train power electronic converters must be properly designed and tuned. Experimental verification of control performance regarding the LFO phenomenon in the railway traction network is not easy. Alternatively, the railway traction network can be emulated using a power electronic converter, which would feed the train power converter under test. This paper addresses the design of a network emulator able to reproduce the dynamic behavior of the actual network at low frequencies, including LFO. Three different options will be considered for the network emulator. Their performance will be studied first by means of simulations. Finally, the selected solution will be verified on a downscale test bench.

Torsional vibrations phenomena are self-excited vibrations that occur in the wheelset of railway powertrains due to the counter-phase oscillation of both wheels. Long-lasting events of this type may lead to the catastrophic failures. Therefore, torsional vibration suppression and mitigation methods have drawn significant attention from the railway industry in the recent few years. Conventional vibration suppression methods reduce motor torque once the oscillation is detected. However, this can result in trip delays. Design of methods which do not compromise the traction capability is challenging. This paper proposes a novel torsional vibration suppression method using a Proportional-Resonant (PR) controller. The proposed method is insensitive to mechanical drive-train parameter variation neither requires adding new sensors to the wheelset. The method requires previous knowledge of the natural frequency of the wheelset torsional mode but this significantly reduces the implementation complexity suffered by other anti-vibration methods. Furthermore, the method will be shown to provide reduced sensitivity to slip velocities and wheel-rail conditions.

Although there is a growing interest in developing fast charging methods to enhance the Electric Vehicles’ appeal, the main energy supply is still the single-phase outlet. While single-phase charging may be simpler than three-phase, there are challenges in the integration of the propulsion and charging systems without adding inductive grid filters. Such integration would bring benefits like the simplification and reduction of components, costs, volume or weight. This can be achieved by using the windings of the propulsion machine as grid filters, but the use of the electric motor as a grid inductive filter leads to the generation of pulsating torque during the charging state. This translates into vibration, noise and eventually, damage to the motor and other components of the system. In this work, the authors propose a control strategy that considerably reduces the peaks of pulsating torque generated in any rotor position while using the machine wingdings as filter. Simulation tests have been carried out to validate the control strategy, taking into account the system efficiency. Finally, experiments are conducted to prove that the reduction of the torque pulsation is achieved.

This paper discusses the benefits of using silicon carbide (SiC) devices in a three-stage modular power electronic transformer. According to the requirements to be fulfilled by each stage, the second one (the DC/DC isolation converter) presents the most estimable improvements to be gained from the use of SiC devices. Therefore, this paper is focused on this second stage, implemented with a SiC-based dual active bridge. Selection of the SiC devices is detailed tackling the efficiency improvement which can be obtained when they are co-packed with SiC antiparallel Schottky diodes in addition to their intrinsic body diode. This efficiency improvement is dependent on the dual active bridge operation point. Hence, a simple device loss model is presented to assess the efficiency improvement and understand the reasons for this dependence. Experimental results from a 5-kW Dual Active Bridge prototype have been obtained to validate the model. The dual active bridge converter is also tested as part of the full PET module operating at rated power.

This paper analyzes control strategies for induction motors in railway applications. The paper will focus on drives operating with a low switching to fundamental frequency ratio and in the overmodulation region or six-step operation, as these are the most challenging cases. Modulation methods, efficient modes of operation of the drive and the implications for its dynamic performance, and machine design will also be discussed. Extensive simulation results, as well as experimental results, obtained from a railway traction drive, are provided.

Torque estimation in permanent magnet synchronous machines and synchronous reluctance machines is required in many applications. Torque produced by a permanent magnet synchronous machine depends on the permanent magnets’ flux and d q -axes inductances, whereas torque in synchronous reluctance machines depends on the d q -axes inductances. Consequently, precise knowledge of these parameters is required for proper torque estimation. The use of high frequency signal both for permanent magnets’ flux and d q -axes inductances estimation has been recently shown to be a viable option. This paper reviews the physical principles, implementation and performance of high-frequency signal injection based torque estimation for permanent magnet synchronous machines and synchronous reluctance machines.