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Accurate and rapid impedance measurement of high-speed train (HST) is crucial for small-signal stability analysis in the train-network system. Although using wideband disturbance can effectively reduce measurement time, its limited and inflexible spectral controllability will lead to inferior disturbance performance, compromising measurement accuracy, and potentially jeopardizing safety. To address this issue, this article proposes an HST impedance measurement approach based on adaptive multifrequency voltage disturbance. This approach enables precise control of the disturbance frequency band as well as adaptive adjustment of both disturbance frequency resolution and amplitude. First, an odd-even segmented frequency injection strategy is proposed to avoid the HST's frequency overlapping effect interfering with wideband impedance measurement. Then, an adaptive frequency resolution adjustment method is proposed to improve measurement accuracy in the frequency segments with heavy impedance fluctuation. Furthermore, a controllable multifrequency voltage injection device is designed for HST impedance measurement, which can inject desired wideband voltage disturbance whose spectrum energy is congregated at designated multiple frequencies with average amplitude. Particularly, the disturbance amplitude can be reduced automatically by adjusting the regulatory factor of modulated signal when capacitor voltage fluctuation exceeds the threshold, thereby enhancing measurement safety. Finally, the effectiveness of the proposed approach is well validated through real-time hardware-in-the-loop experiment.

Accurate impedance measurements of the train and traction network are crucial for small-signal stability analysis of railway train-network system (RTNS). Although impedance measurement methods for four-quadrant converters (4QCs) in electric trains based on harmonic voltage disturbance injection have been proposed, few studies have investigated the impact of integrating a harmonic generator on RTNS stability. To address this issue, this article proposes a wideband harmonic disturbance generator (WHDG) and evaluates its impact on RTNS stability. The WHDG primarily comprises the back-to-back converter-based cascaded H-bridge (CHB) structure and a wideband coupling transformer. This generator can produce multifrequency perturbations with uniformly distributed spectrum energy. Subsequently, an accurate output impedance model is established based on the detailed topology and parameters of the WHDG. The model accounts for the impact of the dc impedance of the front-stage rectifier on the post-stage inverter. The close alignment between the modeling and simulation results demonstrates the accuracy of the deduced impedance model. Furthermore, an impedance-matching analysis of the RTNS with integrated WHDG is performed, indicating that the internal impedance of the WHDG weakens the stability of the tested RTNS. Finally, the effectiveness of the proposed WHDG is validated via a hardware-in-the-loop (HIL) experimental platform, and the impedance-matching analysis results are verified.

In recent years, the field of unmanned aerial vehicle (UAV) tracking has grown rapidly, finding numerous applications across various industries. While the discriminative correlation filters (DCF)-based trackers remain the most efficient and widely used in the UAV tracking, recently lightweight convolutional neural network (CNN)-based trackers using filter pruning have also demonstrated impressive efficiency and precision. However, the performance of these lightweight CNN-based trackers is still far from satisfactory. In the generic visual tracking, emerging vision transformer (ViT)-based trackers have shown great success by using cross-attention instead of correlation operation, enabling more effective capturing of relationships between the target and the search image. But to best of the authors’ knowledge, the UAV tracking community has not yet well explored the potential of ViTs for more effective and efficient template-search coupling for UAV tracking. In this article, we propose an efficient ViT-based tracking framework for real-time UAV tracking. Our framework integrates feature learning and template-search coupling into an efficient one-stream ViT to avoid an extra heavy relation modeling module. However, we observe that it tends to weaken the target information through transformer blocks due to the significantly more background tokens. To address this problem, we propose to maximize the mutual information (MI) between the template image and its feature representation produced by the ViT. The proposed method is dubbed TATrack. In addition, to further enhance efficiency, we introduce a novel MI maximization-based knowledge distillation, which strikes a better trade-off between accuracy and efficiency. Exhaustive experiments on five benchmarks show that the proposed tracker achieves state-of-the-art performance in UAV tracking. Code is released at: https://github.com/xyyang317/TATrack.