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Commutation failures (CFs) including repetitive CFs (RCFs) are frequently occurring faults in line commutated converter based high voltage direct current (LCC-HVDC) systems. Consequently, sending end system will suffer transient voltage disturbances (TVD) with a characteristic of “first reduce then rise and may repeat”. Due to the complex interaction process of TVD, the electromagnetic process behind this phenomenon cannot be easily explained, which imposes great challenges on the development of mitigation strategies. Given this, this paper provides a deep insight into the mechanism and the impact factors of the TVD caused by CFs and RCFs. The numerical relationship between the transmitting power and the TVD is derived. Research results revealed that the transient process after fault can be segmented into five stages. In each stage, the control interactions among HVDC controllers and power interactions at the PCC are analyzed. Besides, we discussed the TVD under RCFs in detail. Several indices are further proposed to facilitate the characterization of the TVD, and main control loops of HVDC that have impacts on the characteristics of TVD are thereby studied. Finally, the mathematical relationship between wind farm power and the TVD is derived to quantify the impact of wind farms on the TVD. [ABSTRACT FROM AUTHOR]

Vehicular ad hoc network (VANET) underpins most intelligent transportation systems and given the open nature of the wireless communication environment, edge nodes in the VANET are subject to varying levels of attacks. While there are a large number of conditional privacy-preserving authentication protocols in the literature, most of these protocols (whose security is guaranteed by classical cryptographic primitives) are not designed to resist quantum attacks. In this paper, we propose a lattice-based conditional privacy-preserving authentication protocol for VANETs, designed to simultaneously achieve mutual authentication and privacy protection. We also show that our protocol is secure in the random oracle model, relying on the small integer solution problem. Additionally, we demonstrate the potential utility of our protocol by evaluating its performance. [ABSTRACT FROM AUTHOR]

Multispectral and hyperspectral image fusion (MS/HS fusion) aims to fuse a high-resolution multispectral (HrMS) and a low-resolution hyperspectral (LrHS) images to generate a high-resolution hyperspectral (HrHS) image, which has become one of the most commonly addressed problems for hyperspectral image processing. In this paper, we specifically designed a network architecture for the MS/HS fusion task, called MHF-net, which not only contains clear interpretability, but also reasonably embeds the well studied linear mapping that links the HrHS image to HrMS and LrHS images. In particular, we first construct an MS/HS fusion model which merges the generalization models of low-resolution images and the low-rankness prior knowledge of HrHS image into a concise formulation, and then we build the proposed network by unfolding the proximal gradient algorithm for solving the proposed model. As a result of the careful design for the model and algorithm, all the fundamental modules in MHF-net have clear physical meanings and are thus easily interpretable. This not only greatly facilitates an easy intuitive observation and analysis on what happens inside the network, but also leads to its good generalization capability. Based on the architecture of MHF-net, we further design two deep learning regimes for two general cases in practice: consistent MHF-net and blind MHF-net. The former is suitable in the case that spectral and spatial responses of training and testing data are consistent, just as considered in most of the pervious general supervised MS/HS fusion researches. The latter ensures a good generalization in mismatch cases of spectral and spatial responses in training and testing data, and even across different sensors, which is generally considered to be a challenging issue for general supervised MS/HS fusion methods. Experimental results on simulated and real data substantiate the superiority of our method both visually and quantitatively as compared with state-of-the-art methods along this line of research. [ABSTRACT FROM AUTHOR]

This paper investigates a new DC voltage sag compensating scheme by using hybrid energy storage (HES) technology involved with one superconducting magnetic energy storage (SMES) unit and one battery energy storage (BES) unit. Two fault simulations of power supply fluctuation and sensitive load fluctuation are carried out to evaluate the dynamic voltage and power smoothing behaviours. The results obtained show that this HES-based DC voltage sag compensator has the ability to suppress the discharging / charging current overshoots for the BES unit and to enlarge the voltage compensating time for the SMES simultaneously. [ABSTRACT FROM AUTHOR]

The fast-response energy compensating feature from a superconducting magnetic energy storage (SMES) device is favored for suppressing instantaneous voltage fluctuations, while the self-triggering current limiting feature from a superconducting fault current limiter (SFCL) suits to suits to achieve automatic load protections in the electric vehicle (EV) charging stations. This paper investigates a new DC voltage quality enhancing scheme by using a combined superconducting power technology employing SMES and SFCL devices. Preliminary simulations demonstrate the feasibility of this SMES-SFCL-based voltage quality improver to avoid the voltage fluctuations during the EV load transients and suppress the fault current effectively during the short circuits simultaneously. [ABSTRACT FROM AUTHOR]

Commutation failure is one of the common faults in the line commutated converter high voltage direct current (LCC-HVDC) transmission system, which will lead to a transient voltage disturbance with the characteristic of “decrease first and then increase” at the feed end. Such a transient voltage may cause severe dynamic responses on the wind generator adjacent to the converter station, resulting in the tripping of large-scale wind turbines and even cascading failure. By analyzing the principle of the transient voltage, this paper investigates the response mechanism of doubly-fed induction generator (DFIG), involving the variation of electrical parameters and fault ride through (FRT) ability. Further verifications through simulations and experiments prove that under the disturbance of commutation failure, DFIG may suffer greater transient shocks compared to a single sag or swell disturbance event. Finally, some suggestions are put forward to mitigate the transient voltage and improve the transient response of DFIG. [ABSTRACT FROM AUTHOR]

Energy-resolved computed tomography (ErCT) with a photon counting detector concurrently produces multiple CT images corresponding to different photon energy ranges. It has the potential to generate energy-dependent images with improved contrast-to-noise ratio and sufficient material-specific information. Since the number of detected photons in one energy bin in ErCT is smaller than that in conventional energy-integrating CT (EiCT), ErCT images are inherently more noisy than EiCT images, which leads to increased noise and bias in the subsequent material estimation. In this work, we first deeply analyze the intrinsic tensor properties of two-dimensional (2D) ErCT images acquired in different energy bins and then present a ${F}$ ull- ${S}$ pectrum-knowledge-aware Tensor analysis and processing (FSTensor) method for ErCT reconstruction to suppress noise-induced artifacts to obtain high-quality ErCT images and high-accuracy material images. The presented method is based on three considerations: (1) 2D ErCT images obtained in different energy bins can be treated as a 3-order tensor with three modes, i.e., width, height and energy bin, and a rich global correlation exists among the three modes, which can be characterized by tensor decomposition. (2) There is a locally piecewise smooth property in the 3-order ErCT images, and it can be captured by a tensor total variation regularization. (3) The images from the full spectrum are much better than the ErCT images with respect to noise variance and structural details and serve as external information to improve the reconstruction performance. We then develop an alternating direction method of multipliers algorithm to numerically solve the presented FSTensor method. We further utilize a genetic algorithm to tackle the parameter selection in ErCT reconstruction, instead of manually determining parameters. Simulation, preclinical and synthesized clinical ErCT results demonstrate that the presented FSTensor method leads to significant improvements over the filtered back-projection, robust principal component analysis, tensor-based dictionary learning and low-rank tensor decomposition with spatial-temporal total variation methods. [ABSTRACT FROM AUTHOR]

The total variation (TV) is a powerful regularization term encoding the local smoothness prior structure underlying images. By combining the TV regularization term with low rank prior, the 3D total variation (3DTV) regularizer has achieved advanced performance in general hyperspectral image (HSI) processing tasks. Intrinsically, 3DTV assumes i.i.d. sparsity structures on all bands of the gradient maps calculated along the spectrum and space of an HSI. This, however, largely deviates from the real-world cases, where the gradient maps generally have different while correlated gradient map structures across all bands. To alleviate this issue, we propose an enhanced 3DTV (E-3DTV) regularization term beyond the conventional. Instead of imposing sparsity on gradient maps themselves, the new term calculates sparsity on the subspace bases on gradient maps along all bands of an HSI, which naturally encodes the correlation and difference among all these bands, and thus more faithfully reflects the insightful configurations of an HSI. The E-3DTV term can easily replace the conventional 3DTV term and be embedded into an HSI processing model to ameliorate its performance. We made such attempts on two typical related tasks: HSI denoising and compressed sensing. The superiority of our proposed method is substantiated by extensive experiments on synthetic and real HSI data, visually and quantitatively on both tasks, as compared with current state-of-the-arts. The code of our algorithm is released at https://github.com/andrew-pengjj/Enhanced-3DTV.git. [ABSTRACT FROM AUTHOR]