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.

The objective of this paper is to determine the transient characteristics and sites of the pre-breakdown (pre-BD) in the spark conditioning under power frequency voltage (PFV) in vacuum. A multi-angle optical path system was built to record the arc of pre-BD at two viewing angles by one camera, realizing the determination of the pre-BD sites in spark conditioning. The recording rate of high-speed camera was 580,000 frames/s. The experimental results indicate that, in the spark conditioning under PFV, the pre-BDs continuously occurred at a random moment. Within several half-waves, the number of the pre-BDs could reach dozens of times, which was the key for the treatment of the electrode surface in conditioning. Under the application of PFV, the single pre-BD occurred at a random moment, in which would last for several microseconds and the peak value of the current would reach several amperes. Moreover, the pre-BD sites randomly distributed in the surface of the electrode and almost covered the whole surface of the electrode. The number of the pre-BDs in edge region of the electrode tent to be more. The pre-BDs took place independently in both time and space.

A common 400V dc bus for industrial motor drives advantageously allows the use of high-performance 600V power semiconductor technology in the inverter drive converter stages and to lower the rated power of the supplying rectifier system. Ideally, this supplying rectifier system features unity power factor operation, bidirectional power flow and nominal power operation in the three-phase and the single-phase grid. This paper introduces a novel bidirectional universal single-/three-phase-input unity power factor differential ac-dc converter suitable for the above mentioned requirements: The basic operating principle and conduction states of the proposed topology are derived and discussed in detail. Then, the main power component voltage and current stresses are determined and simulation results in PLECS are provided. The concept is verified by means of experimental measurements conducted in both three-phase and single-phase operation with a 6kW prototype system employing a switching frequency of 100 kHz and 1200V SiC power semiconductors.

In this letter, an analytical method for the beampattern synthesis of symmetric nonuniform array is proposed. This method consists of two steps. In the first step, it acquires a real symmetric excitation by the convex optimization method to attain a pencil beam. In the second step, it superposes the pencil beams pointing in different directions to synthesize the prescribed beampattern. Numerical results are provided to verify the effectiveness of the proposed method.

Reactive power management (RPM) in electric power systems is usually based on a rule-based control derived from the transmission system operator’s experience. This approach faces challenges as the number of decisions and the complexity of the system operation is increasing. With the increasing generation from renewables and the evolution of electricity markets, the available resources must be optimally utilized. In this paper, a comparison is made between the optimization-based approach (OBA) and the experience-based expert approach (EBA) for RPM. The OBA is based on security-constrained optimization with minimum redispatch cost as the objective function for different contingencies. In contrast, the EBA’s actions are based on the system operator’s experience. Comparison is made in terms of the generator redispatch cost, active and reactive power redispatch volume, nodal voltages, and the number of actions to ensure secure operation. The analysis using a reduced model of the target system shows that OBA is more beneficial than EBA, with up to 22% and 42% reduction in redispatch cost and volume, respectively. Moreover, the control decisions from both approaches are seen to be similar. This study aims to show the usefulness of the OBA and motivate TSOs to move towards optimization-based reactive power management

Operating experience has shown that vibration can cause pressure fluctuations and initiate bubbles at the oil gap, which seriously threatens the insulation properties of oil-immersed power equipment. The main aim of this study was to determine the characteristics and mechanism of bubble initiation at the transformer oil gap under vibration in this paper. The results show the existence of the vibration-dependent bubbling phenomenon at the oil gap. Interestingly, bubbles initiate during the process of gap expansion, and the evolution of bubble morphology was further analyzed. The threshold characteristic of the bubble initiation process was found, and the relevant influencing factors were experimentally obtained. In the range considered in this study, higher gas content, higher temperature, and lower moisture content are conducive to bubble initiation at the oil gap under vibration, while vibrational frequency does not affect the threshold characteristic of bubble initiation process. The vibration-dependent bubbling phenomenon can be attributed to the vibration-induced cavitation process caused by the decrease in pressure at the oil gap. The influence mechanism of the above factors on the threshold characteristics of bubble initiation was further discussed. This study contributes to providing a reference for risk assessment of abnormal vibration of oil-immersed power equipment.

Using Jensen's inequality and integration by parts, we derive some tight upper bounds on the Gaussian Q-function. The tightness of the bounds obtained by Jensen's inequality can be improved by increasing the number of exponential terms, and one of them is invertible. We obtain a piece-wise upper bound and show its application in the analysis of the symbol error probability of various modulation schemes in different channel models.