In this letter, a VAQ-based DAC switching scheme is proposed to improve the power efficiency of SAR ADCs. The input signals are sampled onto the bottom-plates of the most significant bit (MSB) capacitors, thereby eliminating the reset energy. The reference voltage VCM rath than VREF is switched during the third-bit cycle, thereby significantly reducing the power consumption. Additionally, an energy-efficient one-sided switching technique is employed from the fourth-bit cycle. This proposed switching scheme achieves a 99.51% reduction in switching energy over the classic scheme.

Weather events can cause power outages and damage the grid infrastructure. Resilience is the ability of the power grid to quickly recover from disruptions such as weather events. Enhancing the resilience of the distribution systems is required for continued grid operation. A solution to enhance the resilience of a distribution system is to proactively deploy resources (e.g., backup generators, repair crews, additional conductors) in the most vulnerable regions to provide faster service restoration. In this paper, first, we propose a method to model the impact of extreme weather events on power grids. Then, we use Q-learning to identify the worst impact zones while considering possible propagating paths of extreme weather. Finally, we develop a game-theoretic approach to allocate resources to the worst impact zones at the minimum cost. Simulations are conducted on a modified IEEE 123-bus test system. The results prove the effectiveness of the proposed work on enhancing grid resilience.

Two neural networks with two features (SNR and mutual information) are applied to link adaptation that is targeted for meeting the WLAN standard. The usefulness of neural-network-based link adaptation is validated for the WLAN and also the performance (throughput and latency) is analysed to obtain some practical gain of link adaptation based on neural networks over the traditional approach.

In this paper, a novel reconfigurable angular cone antenna that employs a rotatable fan ground plane for reconfiguration is introduced. By regulating the number and orientation of external grounds, the radiation range is progressively broadened, enabling the antenna to exhibit diverse radiation modes. Experimental results demonstrate that the antenna can successfully switch between three modes and validate seven distinct beam directions. These beam directions include: (1) a single ground plane at 45°, 135°, 225°, and 315°; (2) two ground planes at ±45°; and (3) four ground planes creating an omnidirectional radiation pattern. Notably, the antenna boasts a broad bandwidth of 60.66% (covering 1.2-4.9 GHz; VSWR < 2.0) which cover 2G/3G/4G/5G communication bands, while maintaining an antenna efficiency exceeding 90%, marking a significant performance enhancement.

This paper firstly analyses the fault equivalent circuit of DG (Distributed Generation) as a high-frequency harmonic source, and then deduces the fault characteristics of the electrical quantities at both ends on the basis of high-frequency harmonic injection, pointing out the weaknesses: easily affected by the load capacity of the T-connected load. Afterwards, this paper draws the equivalent circuit diagram of the positive sequence component after the fault and derives the difference expression of the virtual electrical quantity at the opposite end under internal or external faults at different locations, and then analyses the fault characteristics of positive sequence under the industrial frequency. On this basis, this paper proposes a double-terminal protection scheme based on the combination of high-frequency harmonic injection and virtual positive sequence differential principle. Finally, the protection scheme is verified on the MATLAB/SIMULINK platform. And the proposed protection method can identify faults within 30ms, with a capability of enduring transient resistance up to 200 Ω, noise immunity reaching 200 dB, and being unaffected by the size of T-connected load capacity or the fault location.

This study introduces a novel strategy for mitigating False Data Injection (FDI) attacks within islanded DC microgrids, primarily focusing on enhancing the security of data transmission between buses. This method utilizes secondary control variables, local feedbacks, and event-triggered control to detect, identify, stabilize voltage during FDI attacks, and ultimately eliminate the attacks. The combination of these methods results in the establishment of a resilient control. Secondary control variables, through defining a variable as a detection index (DI) and impact of FDI attacks on per-unit current and estimated average voltage parameters, detect potential attacks. Subsequently, internal feedbacks ensure stability under attack conditions by increasing the gains of the local feedbacks. Moreover, the proposed event-triggered control identifies the magnitude and location of the attack by comparing two events before and after an attack. Finally, the identified attack is mitigated by removing it from the communication link. Furthermore, after mitigating the attack, the local feedbacks cause the system to return to its pre-attack state, restoring voltage regulation and current sharing to their previous levels. Simulations and experimental results demonstrate the effectiveness of the approach in stabilizing the system and mitigating various FDI attack scenarios, showcasing its potential for securing DC microgrids.

In the analysis of the operational reliability of integrated electrical energy systems, traditional numerical algorithms for solving natural gas dynamics require a substantial amount of computation, making it challenging to complete the analysis within operational time scales. This paper proposes a novel method: replacing traditional numerical methods with a CNN-LSTM neural network algorithm. The algorithm first utilizes a Convolutional Neural Network (CNN) for feature extraction, followed by a Long Short-Term Memory network (LSTM) for feature sequence prediction. Through a sequence-to-sequence learning process, the model learns the mapping relationships between adjacent time steps from the data, constructing a dynamic surrogate model for the gas network. This dynamic surrogate model is further integrated with the power system load flow model, combined with Monte Carlo simulation and multi-state models, to comprehensively analyze the operational reliability of integrated electrical energy systems. In the validation phase, the proposed model was applied to a distribution network-level electric-gas integrated energy system. The validation results demonstrate that the model not only accurately simulates the complex characteristics of gas network dynamics but also significantly reduces the computation time for operational reliability.

The fault location on a transmission line plays a very important role in power system operation. The accurate and fast fault location results on the transmission lines are particularly important in minimizing operating costs and maximizing power system reliability. In this paper, the fault location problem is proposed to transform into the optimization problem solved by optimization algorithms. It is realized that most fault location procedures highly depend on transmission line parameters which are always time-varying under various operation conditions. The inaccuracy of the transmission line parameters greatly affects the fault location results. Thus, these parameters are proposed to estimate for variations. Moreover, when faults occur near the transmission line terminals, the fault currents are high. Then, a current transformer (CT) saturation and a secondary current distortion happen in the transmission system also affecting the accuracy of the fault location results. Instead of measuring the currents affected by the CT saturation, the currents are proposed to estimate for inputting the fault location procedure. In this paper, advanced artificial bee colony (ABC) algorithms are proposed to estimate the parameters and currents at the two terminals of the transmission line; and finally, locate faults on the transmission lines. The advanced ABC algorithms are the variants of the ABC algorithm proposed to improve its exploitation ability. This results in the enhancement of the convergence value and reduction of the iterations required. The numerical results and comparisons confirm the effectiveness of the proposal.

In response to the problem of high-frequency composite noise in ±800kV Converter Station built-in ultra-high frequency partial discharge detection, which interferes and masks partial discharge signals, and cannot obtain accurate partial discharge waveforms, this paper focuses on the wideband extended circuit model and proposes a wideband extended circuit noise control method based on fractional order filtering system. The effectiveness, stability, and reliability of this method in controlling high-frequency composite noise in UHF partial discharge detection were verified and calculated through two parameters, namely ”root mean square value (RMS)” and ”waveform range (R)”. Laboratory experiments, on-site environmental tests, and finite element Maxwell 3D electrostatic field module simulation calculations were conducted to verify the effectiveness, stability, and reliability of this method.

In the process of carbon roasting, the temperature control of the roasting chamber is extremely important, and the uniformity and accuracy of the temperature will affect the qualification rate and product quality of the roasted products, and also indirectly affect the production capacity in the production process. In this paper, multi-intelligence technology is introduced into the heating system of carbon roasting. A combustion intelligence unit is formed by controller, thermocouple (temperature sensor), burner, combustion chamber, etc. The coordinating intelligence is designed on the upper layer of multiple combustion intelligences to manage the coordinating combustion intelligences, and a multi-intelligence combustion system is formed in a chamber. Secondly, a linear-quadratic model was applied to the coordinating intelligences to design a coordination strategy to coordinate the temperature difference between the intelligences. Finally, the coordination strategy is simulated using MATLAB, and the results show that the intelligences can be adjusted to the same level in a short time.

Low Probability of Intercept(LPI) periodic sequences with low autocorrelation sidelobes are desired in many application, especially in LPI radar systems. In this letter, we propose an algorithm to design LPI periodic sequences that counteract cyclic spectrum analysis while maintaining low autocorrelation sidelobe characteristics. We begin by simplifying the weighted cyclic frequency sidelobe (WCFS) level into a quadratic form, and then minimize the WCFS level to better approximate the cyclic spectrum of Gaussian white noise. To ensure the accuracy and detection capability of the solution, we introduce a Peak-to-Average Power Ratio (PAPR) condition for the power spectrum, which is subsequently transformed into a frequency constraint. Finally, leveraging the cyclic algorithm(CA), we introduce a monotonic WCFS minimization algorithm to address the aforementioned optimization problem. The simulation results presented in this letter demonstrate the correctness and effectiveness of our proposed algorithm.

We reported a graphene field-effect transistor (GFET) based on CVD Ge-based graphene and primarily investigated the low-temperature electrical characteristics. The self-alignment technique was used to fabricate GFET to reduce parasitic effects and improve transconductance and cut-off frequency. To further explore the electrical properties, we studied the direct current and radio frequency characteristics of the GFET over a temperature range from 4.2 K to 300 K, considering the temperature-dependent resistivity of intrinsic Ge. The DC characteristic of the GFET for 110 nm gate length, particularly the transconductance performance, exhibits a tiny variation of only ~ 5% across this temperature range. However, the cut-off frequency experiences a considerable increase, improving several tens of times when the temperature decreases to 4.2 K, with a maximum value of 3.49 GHz. This work illustrates a meaningful advancement in applying GFETs in the lowtemperature, high-frequency domain.

It is difficult to identify the arc fault effectively when the loads in the user-side are more complicated, blocking the development of low-voltage monitoring and pre-warning inspection. In this paper, series arc fault signals are acquired according to IEC 62606. The main time-frequency features can be strengthened more effectively by the generalized S-transform with bi-Gaussian window, meanwhile the power spectrum density (PSD) determination allows for the detection of imperceptible high-frequency harmonics energy reflections, increasing the rate of arc fault diagnosis and suitable for the arc fault monitoring of nonlinear loads. The final samples are trained and classified by two-dimensional Convolutional Neural Network (CNN) and the overall accuracy of identification is 98.13%, of which involves various domestic loads, providing a reference for the follow-up arc fault monitoring and inspection research.

This paper sets out to develop an efficient probabilistic optimal power flow (POPF) algorithm to assess the influence of wind power on power grid. Given a set of wind data at multiple sites, the marginal distribution is fitted by a newly developed generalized Johnson system, the dependence structure of wind speeds is matched by the flexible Liouville copula. In order to lower the computational burden for solving POPF model, a Lattice sampling technique is developed to generate wind samples at multiple sites, and a Logistic mixture model is proposed to fit distributions of POPF outputs, which can quantify the effect of wind speed uncertainty on power grid. Finally, the proposed methods are illustrated by case studies on the IEEE 118-bus system.

To create synergies between offshore wind integration, trading capacities, and operational flexibility, interconneting HVDC links to multi-terminal networks is highly desired. However, its technical realisation remains a major challenge – especially in a multi-vendor environment. In particular, it is crucial to prevent DC faults from leading to an intolerable loss of power infeed to the connected AC grid(s). To restrict this loss of power infeed, this paper proposes a concept based on state-of-the-art equipment only – i.e., without dependence on DC circuit breakers (DCCB) – for linear HVDC networks. In this concept, the DC-side interconnection is preventively decoupled via DC high-speed switches (DC-HSS) whenever the cumulative wind infeed exceeds the frequency containment reserve of the adjacent onshore AC grid. At all other times, the multi-terminal network is operated in a coupled manner. The decoupling sequence is realised via the converters’ (VDC/P)-droop controls and a supervisory control. Both the decoupling sequence and the resulting DC fault behaviour in decoupled state are validated via EMT simulations of a four-terminal network, and challenges as well as the expandability to larger networks are discussed. The proposed concept could help to de-risk and accelerate the development of first offshore multi-terminal HVDC networks at reasonable costs.

A novel enlarging fractional bandwidth (FBW) technique for acoustic-wave-lumped-element resonator (AWLR) -based bandpass filters is present. The new technique is based on a series of matching inductors that replace the parallel or series acoustic wave (AW) resonators of AW filters to beak the bandwidth constraint of the ladder-type structure. In addition, the transmission response analysis of the proposed AWLR-based filter is provided. A prototype of the AWLR-based filter is fabricated and tested to validate the proposed FBW widening technique. The measured insertion loss, FBW, and out-of-band rejection are 1.5dB, 1.63kt2 (kt2 is the electromechanical coupling coefficient of the AWR), and 38dB, respectively.

The fusion of renewable energy into power grid planning poses significant challenges. In this investigation, we assess planning strategies across eight key indicators encompassing economic, reliability, environmental, and adaptability considerations. Leveraging the Analytical Hierarchy Process (AHP) alongside the Entropy Weight Method, we ascertain both subjective and objective weights, subsequently synthesizing comprehensive weights with parsimonious discriminative data. Scores for planning strategies are then derived utilizing these weights. To illustrate and validate the efficacy of the proposed methodology, we apply it to four schemes within the IEEE-6 node system, incorporating wind and photovoltaic power plants.

Pooling operations, essential for neural networks (NNs), reduce feature map dimensions while preserving key features and enhancing spatial invariance. Traditional pooling methods often miss the feature maps’ alternating current (AC) components. This study introduces a novel global pooling technique utilizing spectral self-attention, leveraging the discrete cosine transform (DCT) for spectral analysis and a self-attention mechanism for assessing frequency component significance. This approach allows for efficient feature synthesis through weighted averaging, significantly boosting TOP-1 accuracy with minimal parameter increase, outperforming existing models.