Wind power is an exceptionally clean source of energy, its rational utilization can fundamentally alleviate the energy, environment, and development problems, especially under the goals of “carbon peak” and “carbon neutrality”. A combined short-term wind power prediction based on LSTM artificial neural network has been studied aiming at the nonlinearity and volatility of wind energy. Due to the large amount of historical data required to predict the wind power precisely, the ambient temperature and wind speed, direction, and power are selected as model input. The CEEMDAN has been introduced as data preprocessing to decomposes wind power data and reduce the noise. And the PSO is conducted to optimize the LSTM network parameters. The combined prediction model with high accuracy for different sampling intervals has been verified by the wind farm data of Chongli Demonstration Project in Hebei Province. The results illustrate that the algorithm can effectively overcome the abnormal data influence and wind power volatility, thereby provide a theoretical reference for precise short-term wind power prediction.

The decarbonization of the power generation and transport sector encourage the analysis of connection of , such as , to the electrical system, as well as the evaluation of their impact on smart cities. A better understanding of the negative impacts on the power systems will lead to propose mitigation measures and eventually revolutionize the way distributed generation works. This paper aims at modeling and evaluating the impact of EVs on a real distribution network. The energy system chosen operates at 60 Hz, 34,5 kV (Medium Voltage) and 0,208 kV (Low Voltage) and it is simulated using PSCAD/EMTDC. To reproduce realistic user consumption profiles, dynamic load profiles based on EV owners behaviour have been simulated. The technology is modeled to mitigate the impacts of high penetration of EVs by supporting the network from undervoltage. The results show the importance of active management in modern power systems, especially considering the increase in DER penetration expected for the coming years. This work shows the benefits of implementing V2G technology while highlighting the challenges involved in a real case.

The target and sea clutter Doppler domains frequently overlap due to the frequent passage of slow ship targets through the sea clutter zone. In this letter, a novel sea clutter suppression method is suggested as a solution to this issue, whose key is a novel singular value zeroing criterion guided by the search results of two-dimensional spectral peaks. Verified by simulations, the method proposed can improve signal-to-clutter ratio (SCR) from -8 dB to 41 dB in the frequency domain and be more effective than the conventional SVD-FRFT method in[7] and improved SVD-FRFT method in[8] .

In the era of cyber-physical-social systems, research lacks a comprehensive framework to optimize dynamic coordination strategies for electric vehicles (EVs) to enhance modern power distribution networks (MPDNs) resilience. Public studies have not comprehensively addressed the impact of intelligent transportation systems (ITS) and smart charging systems (SCS) on MPDN resilience strategies after rare events. To bridge this gap, a two-stage EVs coordination framework is proposed to consider MPDN smart restoration. The first stage involves a novel proactive EV prepositioning model to optimize EV prepositioning plans before a rare event in order to enhance MPDN survivability during the immediate aftermath of the event. The second stage involves an advanced spatial-temporal EV dispatching model to maximize the number of available EVs for discharge, thereby improving MPDN recovery after a rare event. The proposed framework includes an information system facilitating dynamic data exchange between EVs and ITS/SCS automated systems and introduces a novel geographic graph to optimize EV routes between charging points. Effectiveness of the framework is assessed on a modified IEEE 123 node test feeder incorporating real-world transportation and charging infrastructure. The results suggest that significant enhancement in MPDN resilience is possible with smart restoration strategies. Sensitivity analysis specifically demonstrates that integrating recently invented technologies of the smart charging systems and automated systems potentially provides significant benefits if dynamic coordination strategies are employed in MPDNs.

As detection technology continually advances, the survivability of targets on the battlefield is significantly challenged. Therefore, microwave absorbers with stealth capabilities have become a focal point of research in modern military science. To address the issues of narrow bandwidth and complex structures in existing absorbers, we propose a model for an ultra-wideband absorber based on a hybrid structure. In this study, we design, manufacture, and characterize a polarization-insensitive ultra-wideband absorber (PIUWA), which demonstrates impressive absorptivity of over 90% across a range of 4-24.53GHz (a fractional bandwidth of 144%). This is achieved by inducing multiple resonance peaks within the hybrid structure. Moreover, the subwavelength periodicity of the PIUWA theoretically contributes to its angular stability under full-wave polarizations. We observed that absorption performance remains stable under incident conditions within 45 degrees. Furthermore, the operational mechanism of the PIUWA is elucidated through an equivalent circuit model, with design validity confirmed via experimental measurements. This study paves the way for the design and fabrication of ultra-wideband microwave absorbers that offer high absorptivity, robust angular stability, and simpler assembly processes, thereby broadening the potential for application in other absorber types.

This work is mainly to describe the phenomenon at low frequency in my previous publication. In this paper, the impact of the magnetostriction mechanism is taken into consideration for the main idea. The axisymmetric FEM model of the spiral-coil EMAT is established to implement the simulation. With the help of the simulation, it is demonstrated that the directivity of ultrasonic wave can be manipulated by frequency. And it is found that the direction of Lorentz force that dominates in the rail varies with time, but the magnetostrictive force compels the ultrasonic wave mainly generated by the Lorentz force to the axis. This describes well that the power of two combined mechanisms is greater than that of only the Lorentz-force mechanism at low frequency.

We propose and experimentally demonstrate an on-chip all-optical multicasting (AOM) for 40 Gbit/s mode-division-multiplexed quadrature phase-shift keying (MDM-QPSK) signals based on a parallel dispersion-engineered multimode nonlinear silicon waveguide. Five dual-mode multicast wavelengths are successfully obtained on the generate idlers, and the power penalties of all the multicast channels are less than 1.1 dB at the bit error rate (BER) of 3.8×10-3. The dual-mode AOM scheme has the potential to promote the ability of optical cross-connect in practical hybrid multiplexed networks including MDM channels.

In this letter, we introduce a design of virtual guarded SiPMs fabricated in a standard 0.35 μm standard complementary metal oxide semiconductor (CMOS) process. We compare the performance of these virtual guarded cells (VGC) to that of conventional cells with real guard rings, referred to as physical guarded cells (PGC). Specifically, we evaluate the photon detection efficiency (PDE) of both types of SiPMs. Our results demonstrate that the VGC SiPM outperforms the PGC SiPM, exhibiting a true PDE of (22.5 ± 0.5) %, which is significantly higher than the PDE of (10.9 ± 0.3) % obtained for the PGC SiPM. The superior PDE of the VGC SiPM is attributed to a larger active or photosensitive area due to the virtual guard rings and a thinner n-layer in the photosensitive region.

An improved coot optimization algorithm is proposed for wireless sensor networks (WSNs) coverage optimization. To monitor the interest field and obtain the valid data, a wireless sensor network coverage model is established. The population is initialized with cubic map and opposition-based learning strategy. The leader population is reversely learned dimension by dimension, so as to improve the diversity of the population and the global optimization ability of the algorithm. The simplex method is introduced to optimize the local exploration of the population. The experimental results show that the enhanced coot optimization algorithm for coverage optimization in wireless sensor networks can reduce energy consumption and improve network coverage.

The HVDC transmission based on modular multilevel converter shows the weak feed characteristic of limited fault current amplitude when connected to the traditional AC power grid, which leads to the risk of sensitivity reduction or even failure of the traditional pilot protection. In this paper, a MMC-HVDC AC side transmission line protection scheme based on positive sequence fault component current ratio is proposed. The positive sequence fault component current of the MMC-HVDC side is calculated by using the positive sequence fault component current of the power grid side, and the fault is identified through the significant difference between the ratio of the positive sequence fault component current of the MMC-HVDC side and the actual positive sequence fault component current of the MMC-HVDC side in the case of internal and external faults. This method has the advantages of simple principle, low requirement for synchronous data, not affected by capacitive current and strong resistance to high resistance. The simulation results show that the algorithm can correctly partition internal and external faults, and is not affected by fault location, fault type and operation mode of converter station, and has high sensitivity and reliability.

Fast and accurate fault direction criteria is crucial for efficient protection in hybrid multi-terminal HVDC transmission systems. The current variation method is commonly used to identify fault direction but is affected by fault resistance and distributed capacitance. This paper proposes a new direction criterion based on the backward fault traveling-wave, which has stronger resistance to interference from transition resistance and operates at a faster speed than other methods. Additionally, this paper provides the time-domain expression of the traveling wave after multiple reflections between the fault point and the converter station port, enabling the calculation of the threshold setting for the criterion. The new direction criterion is tested in PSCAD/EMTDC, and simulation results demonstrate that it can accurately identify fault direction and has a strong ability to withstand transition resistance.

In this paper, Short-term predicting of load and spinning reserve is first performed using a combination of ANFIS and meta-heuristic algorithms including Differential Evolution (DE), Genetic Algorithm (GA) and Particle Swarm Optimization (PSO). The ANFIS-PSO combination is selected as the best ANFIS combination in load and spinning reserve prediction with a lower error criterion than other methods. As a DL method, LSTM network can provide good accuracy for load and spinning reserve forecasting. In the optimal ANFIS-PSO method, the average error value is low, but the error variance is high, on the contrary, in the LSTM method, the average error value is high, and the error variance is low. Therefore, we use the combination of ANFIS-PSO and LSTM to reduce the average error and error variance to an acceptable level. The weighted average method is as follows: the accuracy of each Method is obtained in the training step, then the predicted value for each data in the test step is calculated in each Method, then they are multiplied, and after that added together, finally will be divided to the total accuracy of two methods. The results obtained from the weighted average Method show the success of the proposed Method.

The extreme power output scenarios of renewable energy sources (RES) proposed new challenges to the safe and stable operation of the power system. Transmission expansion planning (TEP) with large-scale RES grid integration needs considering the risk of extreme scenarios. In this paper, an adaptive decision-making approach for the TEP problem based on planning-risk assessment-replanning iterative process is proposed. The method obtains massive temporal and spatial correlated wind-photovoltaic (PV) power output scenarios by generalizing the historical data to describe the uncertainties. A data-driven load loss risk assessment model (RAM) based on the power system’s actual operating state is built, referring to the degree of extreme scenario risks on the balance of supply and demand, and the probability of extreme scenario occurrence. The planning decision is progressively revised according to the risk assessment result. The Garver’s 6-bus system and the IEEE RTS 24-bus system are adopted as simulation cases. The results show that the optimal expansion plans achieve a balance between the economy and robustness, which verifies the effectiveness of the proposed method.

In this paper, we investigate the application of Hybrid Representation in Wide-Angle Synthetic Aperture Radar (WASAR) imaging, addressing the challenges of achieving sparse representation in the presence of complex electromagnetic scattering characteristics and highly anisotropic targets. We utilize a Convolutional Neural Network (CNN) to represent two-dimensional data within the same subaperture, while employing dictionary learning for sparse representation across different subapertures. Convolutional Neural Networks (CNNs) excel at learning spatial hierarchies and local dependencies in two-dimensional data, but require a large amount of training data. Isotropic targets within subapertures can be used for training with conventional SAR data, whereas anisotropic targets present challenges in obtaining training samples. To address this, a dictionary for different subapertures is generated from measurements using dictionary learning, eliminating the need for additional training data. By integrating these methods, we propose a novel approach, Hybrid-WASAR, which incorporates two regularization terms into WASAR imaging and employs the Alternating Direction Method of Multipliers (ADMM) to iteratively solve the imaging model. Compared to traditional WASAR imaging techniques, Hybrid-WASAR not only enhances the accuracy of the reconstructed target backscatter coefficients, but also effectively reduces sidelobes and noise, resulting in a significant improvement in overall imaging quality.

The transparency of machine learning models is central to good practice when they are applied in high stakes applications. Recent developments make this feasible for tabular data, which is prevalent in risk modelling and computer-based decision support across multiple domains including healthcare. Important motivating factors for interpretability are outlined and practical approaches are summarised, signposting the main methods available, with pointers to the supporting literature. A key finding is that any black box classifier making probabilistic predictions of class membership from data in tabular form can be represented with a globally interpretable model without loss of performance.

In this letter, a novel model for broadband power amplifier (PA) linearization is proposed, namely Attention Mechanism based Bidirectional Long Short-term Memory network (AM-BiLSTM). In order to verify the linearization performance of the AM-BiLSTM model, a 100MHz bandwidth 5G new radio (5G NR) signal is employed to test the sub-6G PA operating at 2.6-GHz. The experimental results show that the adjacent channel power ratio (ACPR) of the PA with AM-BiLSTM can be improved by 24dB which is 6-dB better than the generalized memory polynomial (GMP) and 3-dB better than the Chebyshev polynomials LSTM (CP-LSTM) in ref[1]. Therefore, the proposed AM-BiLSTM is very effective for the linearization of broadband PA.

TSP is one of the most famous problems in graph theory, as well as one of the typical NP-hard problems in combinatorial optimization. Its applications range from how to plan the most reasonable and efficient road traffic to how to better set up nodes in the Internet environment to facilitate information flow, among others. Reinforcement learning has been widely regarded as an effective tool for solving combinatorial optimization problems. This paper attempts to solve the TSP problem using different reinforcement learning algorithms and evaluated the performance of three RL algorithms (Q-learning, Sarsa, and Double Q-Learning) under different reward functions, ε-greedy decay strategies, and running times. The results show that the Double Q-Learning algorithm is the best algorithm, as it could produce results closest to the optimal solutions, and by analyzing the results, better reward strategies and epsilon-greedy decay strategies are obtained.

In order to improve the ability of the modular multi-level converter to quickly clear and isolate faults in the multi-terminal flexible DC grid, this paper analyzes the characteristics of current faults in the multi-terminal flexible direct current grid, introduces the comprehensive distance of current fault waveform, and proposes a line protection method based on the similarity of current fault waveform. This protection method firstly uses Euclid DTW (Dynamic Time Warping) distance and entropy weight method to process electrical quantity signals, calculates the comprehensive distance of current fault waveform of two converter stations, and builds the protection criterion based on the similarity of current fault waveform. Then, the fault identification is realized by using the calculated comprehensive distance, and the fault pole identification is realized by using the ratio of comprehensive distance between positive and negative poles. Finally, PSCAD was used to build a simulation model to output the fault data under different fault conditions, and MATLAB was used to process the fault data to verify the protection algorithm. The simulation results show that the proposed method has good resistance to high resistance, and its rapidity and adaptability are further improved. The research results will provide a theoretical basis for the safe and reliable operation of flexible DC power grid.