A compact high-sensitivity probe for sensing up tangential E-field is proposed and measured in this paper. The probe is consisted of an electric dipole with patches used to induce the tangential E-field, a resonator made up of open circuit stubs (capacitor) and short circuit stubs (inductor), for achieving a specific resonance, and a compact coupled balun for transforming the differential- mode voltage into the single output voltage and impedance matching. The probe is fabricated on five-layer dielectric lamina. Compared with the referenced broadband probe, the measured |S12|of the probe is enhanced by 18.81 dB. The proposed probe in this paper is validated with simulation and measurement.

In order to resolve the contradiction between the rapid growth of wind turbines installed capacity and the lagging operation and maintenance technology. This article uses Supervisory Control And Data Acquisition (SCADA) system data to realize wind turbines fault diagnosis and early warning research. First, this article proposes a method for cleaning abnormal data in SCADA systems. It combines Density-Based Spatial Clustering of Applications with Noise (DBSCAN) and Median Absolute Deviation (MAD) to estimate the normal power range. It provides high-quality health data samples for further research, based on the results obtained. Then, for wind turbines fault diagnosis, this article designs a Lightweight Gradient Boosting Machine (LightGBM) model based on a Genetic Algorithm (GA). This model can effectively diagnose different types of faults in wind turbines. Finally, this article proposes a Multivariate State Estimation Technique (MSET) normal model based on the DBSCAN algorithm for fault warning in wind turbines. This article designs two threshold setting methods: fixed threshold and adaptive dynamic threshold. Fault warning is conducted by calculating the residuals between predicted and actual values.

In this letter, the issue of mitigating strong co-channel interference (CCI) in communication systems is addressed. Unlike conventional model-based methods, a novel data-driven scheme is proposed. A recurrent neural network (RNN) is trained to directly demodulate the desired signal under strong CCI. Instead of inputting the original received signal, in-phase and quadrature interference-robust features (IRF) are extracted through preprocess. The RNN is then trained offline to implement sequence labelling, with the IRF sequences and known code sequences of the desired signal as inputs and ground-truth labels. Meanwhile, a guard zone is introduced when loading the IRF sequences to enable better contextual information exploitation by the RNN demodulator. Online tests validated the low bit error rate (BER) of the RNN demodulator, under strong CCI. Moreover, the proposed scheme outperformed existing model-based and data-driven interference mitigation schemes in terms of the BER, especially in low signal-to-interference ratio region. Inspiringly, the proposed data-driven scheme generalized well to varied unseen test conditions.

A high speed and low power input/output buffer for time interleaving circuit is proposed in this letter. The buffer can be applied to high speed circuits operating at 20GS/s. This novel two-stage buffer is employed with bandwidth expansion and slew-rate enhanced techniques. An improved common-mode feedback circuit stabilizes the output common-mode voltage. This prototype buffer is fabricated in 45nm COMS process, and achieves 7.2bit ENOB at 10GHz input frequency with power consumption of 20.4mW, load of 0.3fF.

This research explores the desorption mechanism and thermal stability of octadecyl trichlorosilane (OTS, CH3(CH2)17SiCl3) coating on quartz slides and in alkali-metal vapor cells. We measured systematically the morphological thermal-changes, energy dissipation diversity and anti-relaxation characteristic of OTS coatings before and after exposure to Cs atoms by Fourier transform infrared spectroscopy (FTIR), water contact angle measurement, atomic force microscopy (AFM) imaging, collision energy dissipation analysis, and free induction decay (FID) of Cs atoms. The results show that the OTS coatings exhibit the best thermal stability under the specific process conditions, and the homogeneous and dense structure makes the adsorption of alkali metal atoms more stable, which effectively reduces surface energy dissipation and prolongs the relaxation time of Cs atoms. The study provides certain reference for efficient anti-relaxation coating fabrication and coated cell application.

Dynamic filtering is crucial in various applications, including imaging, communication, and biological detection. With advancements in micro-nano device fabrication, there is a growing demand for enhanced filtering performance. This paper presents a near-infrared single-wavelength tunable filter that utilizes polymer-dispersed liquid crystal combined with a Fabry-Perot resonant cavity. The device achieves a transmittance of 99% at 927 nm with a 24 nm full width at half maximum. Ultizing the electro - optical tunable properties of Dielectric layer, the transmission wavelength can be tuned from 913 to 969 nm by altering external voltage to it, resulting in a wavelength shift of 56 nm while maintaining high transmittance. This structure provides a reference for the dynamic control of light sensors and near-infrared dynamic imaging.

Adequacy is a crucial consideration in the planning and operation dispatching of the power system, especially in the power system with high penetration of renewable energy sources. Considering the multi-time scale characteristics of renewable energy power and the response characteristics of flexible regulation resources, a multi-time scale adequacy evaluation method based on empirical mode decomposition is proposed. In this method, a net load curve is decomposed into multiple component curves at a multi-time scale by the empirical mode decomposition (EMD) algorithm. As a result, the adequacy demand of the system at each time scale is obtained by waveform recognition. Moreover, the available adequacy resources of the system at different time scales are obtained according to the regulation models of flexible regulation resources. By analyzing the adequacy demand and available adequacy resources of the system at the same time scale, the adequacy evaluation indices at each time scale can be calculated and weighted to form the comprehensive indices. At last, taking a practical power system as a case, the adequacy evaluation indices at each time scale with different capacities of renewable energy sources and energy storage systems are compared and analyzed. Simulation results indicate the validity of the method.

When acting as the shared energy storage and participating in electricity market services, the schedulable capacity that shared electric vehicle (shared EV) will provide to the grid needs in future time needs to be predicted accurately. This research first proposes a method to construct a schedulable capacity dataset for shared EVs based on publicly available shared vehicle rental service data. Secondly, a schedulable capacity evaluation model based on model-agnostic meta-learning, convolutional neural network, long short-term neural network and attention mechanism (MAML-CNN-LSTM-Attention) is proposed. Through the model, the aggregated schedulable capacity of shared EVs in different functional communities for the coming 60 minutes is predicted. Model uses MAML to fine-tune the meta-prediction network through multi-task training to quickly adapt to feature changes caused by different travel habits of different functional communities; CNN-LSTM is used to learn spatial features of schedulable capacity and efficiently extract high-dimensional temporal features from historical sequences; Attention mechanism is used to further improve model prediction accuracy. Simulations show that the model proposed in this paper outperforms other existing models and can reliably predict the schedulable capacity for different date types and functional areas, providing useful decision aids for shared EV operators to participate in market services.

With the global energy transition and the development of smart manufacturing, modern power systems are characterized by a high penetration of renewable energy and power electronic devices, leading to a declining resilience against voltage sags. Accurate post-event source identification and event inversion estimation form the foundation for managing and mitigating voltage sag issues, which is of great significance for enhancing high-quality power supply in the grid. This paper proposes a method for voltage sag source identification and inversion estimation based on disturbance energy and pattern matching, aiming to achieve precise localization and full-process inversion estimation of voltage sag events, thereby providing key technical support for refined grid management and high-quality power supply. First, by analyzing the changes in energy flow direction at each monitoring point before and after a fault, upstream and downstream identification of the sag source is achieved. Combined with judgment results from multiple monitoring points, the exact location of the voltage sag is accurately determined, overcoming the challenge of insufficient source identification accuracy caused by multi-directional power flow from renewable energy sources in active looped systems. Second, pattern matching technology is introduced to construct a voltage sag event inversion estimation model. Using the Monte Carlo simulation method, a voltage sag source identification pattern library is generated. The measured data from limited monitoring points are intelligently matched with the pre-generated pattern library based on characteristics, enabling post-event inversion of voltage sag events and determining the voltage sag severity at unmonitored nodes. Finally, the IEEE 30-bus system is used as a case study to simulate and verify the effectiveness and accuracy of the proposed method in precise voltage sag source localization and inversion estimation.

With the increasing penetration of renewable energy, virtual power plants reduce the impact on the power grid by integrating massive distributed resources for unified management. However, the optimal scheduling of a large number of distributed resources in virtual power plants has become a new problem in recent years. Therefore, aiming at the real-time optimal scheduling problem in the optimal scheduling of virtual power plant, this letter regards the virtual power plant as a multi-agent system, and proposes a novel real-time active power dispatch scheme of virtual power plant based on distributed model predictive control, so that each agent can not only calculate its own optimization function relatively independently, but also fully refer to the neighbor information. Simulation results show the feasibility and effectiveness of the proposed method.

One of the major bottlenecks of the traditional Current Transformer Energy Harvester (CTEH) is the instable output induced by the wide-range variations of the current in transmission lines. In this work, a novel CTEH capable of generating a stable output is demonstrated by using a core fabricated with saturable magnetic material. The stable output of the CTEH is enabled by the constant voltage-time product in its saturable characteristic. The proposed CTEH is implemented with a resistive load representing the load of electronic devices. When the current in the primary side of the CTEH’s increases from 1A to 1000A, the maximum power on the load can reach about 0.5W, demonstrating the feasibility of using the CTEH with the saturable magnetic core as a quasi-stable power supply.

Feature extraction from the normalized transformation domain is a key technique in target detection. Traditional normalization approaches assume that matrix elements follow a normal distribution, but any deviations from this assumption can lead to significant systematic errors. This article presents a novel method that modifies the normalization process in the fractional Fourier transform (FRFT) domain by incorporating a threshold mechanism to counteract the effects of non-normal distributions. Three modified FRFT features are then extracted from this modified FRFT domain. Furthermore, we propose a target detection method that utilizes these three adjusted features. Experimental results based on measured data indicate that the modified FRFT features exhibit superior classification capabilities for sea clutter and targets compared to the original ones. Additionally, the experiments also demonstrate that under the same conditions, the proposed detection method outperforms traditional FRFT feature detector and the tri-feature based detector.

Linear solvers and eigensolvers are the heart of HPC scientific applications. Among them, iterative projection methods are preferred to direct algorithms for large problems because of their lower memory usage, but they are prone to roundoff errors. Using an enhanced working precision inside the linear computing kernels mitigates this issue and accelerates convergence. Today, to go beyond 80 bits of precision, the only option is to use software libraries which are very slow. We introduce the VaRiable and extended Precision Accelerator (VRP), a RISC-V accelerator implemented on a System-on-Chip (SoC) using GF22FDX technology. The VRP supports Floating Point (FP) computations with a range of significand bits from 2 to 512. This accelerator delivers an average 19.25x application speedup compared to the well-known MPFR software library running on a 2400 MHz Intel Xeon processor. Additionally, extended precision facilitates the convergence of linear solvers for problems that would otherwise fail to converge and reduces energy-to-solution.

This paper proposes an approach to evaluate the loading limits of distribution networks due to increasing EV connections. It focuses on two parameters: after-diversity maximum demand (ADMD) and maximum daily energy demand (MDED). Using actual EV charging data from the UK, Monte Carlo simulations generate daily charging profiles, identifying ADMD, MDED, and seasonal variations. ADMD, MDED, and per-hour maximum EV charging demands are combined with UK residential load profiles before EV connection. Their maximum demands are assessed against thermal rating limits, establishing the network’s hosting capacity (HC) for uncontrolled EV charging. To determine the maximum safe number of connected EVs, different scheduling methods for controlled EV charging are compared, considering per-hour maximum demand values, thermal limits, and MDED. This defines the network’s HC for fully controlled EV charging. The approach is demonstrated on the IEEE 33-bus test network. Pre-EV residential demands are obtained from a UK MV substation, and ambient data is collected from a UK Met Office weather station. Results provide a range of network HC values for uncontrolled and controlled EV charging, representing lower and upper limits. These limits correlate with firm and non-firm network HC concepts and guide optimal network upgrades for exceeding these limits.

This study proposed a matched field source localization method based on tensor decomposition. By considering the advantages of tensors in multidimensional data processing, a three-dimensional tensor signal model of space-time-frequency is constructed, and the signal subspace is estimated using high-order singular value decomposition (HOSVD). The source position is estimated by matching the measured data tensor signal subspace with the replica field tensor signal subspace. The S5 event data of SWellEx-96 is processed by the proposed tensor-based matched-field processing (TMFP). The comparison with the results of conventional matched field processing (MFP) shows that TMFP has a better suppression effect on ambient noise under low SNR and better source localization performance.

To solve the problem of high impedance line-to-ground fault (HILGF), a solution using isolation transformers for subnetwork divisions was previously implemented in the power distribution network. As a result of that, the network has been experiencing ferroresonance more often. We have furthered our understanding of the situation by modelling and simulations under the Power System Computer-Aided Design for Electromagnetic Transients and Direct Current (PSCAD/EMTDC) based on its real parameters. The ferroresonance originates from the network response to the HILGF clearing. Subnetwork division has certainly improved the network fault damping factor, but the line capacitance reduction it caused has exposed the system to ferroresonance. To mitigate the ferroresonance, high-frequency components are removed from the zero-sequence current using the morphological filter; then through edge detection, it is used as the input signal to the control module. The control module then takes the ferroresonance mitigation action intelligently. Testing this proposed solution has given promising results that testify to its effectiveness in real-time application.

To solve the problem of negative-sequence and reactive power in electrified railway, this paper proposes a comprehensive compensation method based on V/v transformers and electromagnetic single-phase var compensator (ESVC) for co-phase power supply. First, based on the principles of static var generator (SVG) and single-phase rotary phase shifting transformer (SRPST), the topology and model of ESVC have been established, and the compensation mechanism has been analyzed. Second, the topological structure and mathematical model of co-phase power comprehensive compensation device (CPCD) are put forward based on the principles of V/v transformers and ESVC, and the operation mode of CPCD in multiple scenarios is designed. Then the strategy of CPCD with double closed loop control is analyzed: corresponding compensation mode is selected according to negative-sequence unbalance degree and expected power factor value. In this strategy, the compensated current output by ESVC is taken as the quantity of external loop control, and the rotation angle of ESVC is taken as the quantity of internal loop control to realize double-closed loop control of CPCD. Finally, the comprehensive CPCD compensation model is built on the simulation platform and validated the accuracy of the mathematical model and the effectiveness of the control strategy.

Entropically Secure Encryption (ESE) offers unconditional security with shorter keys compared to the One-Time Pad. In this paper, we present the first implementation of ESE for bulk encryption. The main computational bottleneck for bulk ESE is a multiplication in a very large finite field. This involves multiplication of polynomials followed by modular reduction. We have implemented polynomial multiplication based on the gf2x library, with some modifications that avoid inputs of vastly different length, thus improving speed. Additionally, we have implemented a recently proposed efficient reduction algorithm that works for any polynomial degree. We investigate two use cases: X-ray images of patients and human genome data. We conduct entropy estimation using compression methods whose results determine the key lengths required for ESE. We report running times for all steps of the encryption. We discuss the potential of ESE to be used in conjunction with Quantum Key Distribution (QKD), in order to achieve full information-theoretic security of QKD-protected links for these use cases.