With the development of smart distribution network and the proposal of dual carbon target, the importance of demand side management in improving the flexible operation of power system is becoming more and more prominent. In order to solve the problems of excessive load peak valley difference, insufficient utilization of demand side resources, and unreasonable pricing of aggregators, this paper propose an economic optimization scheme for aggregators based on electric vehicle three-stage dispatching. First, the loss aversion analysis is conducted on the willingness of electric vehicle users to participate in dispatching. The contract signing methods between aggregators and electric vehicles are divided into three categories: complete dispatching, rolling reward and punishment mechanism dispatching, and free dispatching. Next, the response model of electric vehicle users based on the improved cloud model is obtained. Then, the aggregators conduct three-phase optimal dispatching for electric vehicles according to the bid winning peak shaving capacity. Phase 1 according to the time of regional differences, the dispatching of reward power set rewards and punishment mechanism, phase 2 to determine the full freedom dispatching of electric vehicles, three kinds of dispatching and dynamic load capacity of electricity, phase 3 according to the phase 1 and 2, the amount of information and user loss aversion, the user response model of final rolling rewards and punishment mechanism, get the aggregators final pricing schemes, Finally, a numerical example is given to verify the feasibility of the proposed method.

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 this paper, a 2-bit digital reflection-type phase shifter working at 120 GHz is presented. It uses a compact coupled-lines coupler with low insertion loss and high isolation over a wide bandwidth. The loads are made by a microstrip-line loaded by three PIN diodes whose states are tuned ON/OFF to obtain 90°, 180° and 270° phase shift relative to the reference (0°). Measurement results show RMS phase and amplitude error equal to 10.3° and 1.2 dB, respectively. The maximum insertion loss is equal to 8.6 dB, leading to a figure of merit of 31°/dB. As shown by simulation, by flipping PIN diodes and use negative voltage for biasing, the maximum insertion loss could be reduced to 3.6 dB (figure of merit of 75°/dB) along with a great improvement in RMS phase and amplitude errors, thus showing the potential of the proposed architecture.

The distributed and privacy-preserving characteristics of fine-grained smart grid data hinder data sharing, making federated learning an attractive approach for collaborative training among data owners with similar load patterns. However, malicious models can interfere with training in the federated learning aggregation process, making it difficult to ensure the accuracy and safety of the central model in load forecasting. Therefore, we propose a secure aggregation federated learning method for distributed load forecasting based on similarity and distance (Fed-SAD), which effectively eliminates the interference of malicious models by securely aggregating models, thereby ensuring accurate and safe distributed scenario prediction. Experimental results demonstrate that Fed-SAD maintains high accuracy and robustness in both the presence and absence of malicious models, while maintaining data and model security.

Low power IoT communication signals (e.g., Bluetooth and ZigBee) may seriously suffer from the presence of high-power co-channel interference like wireless local area network (WLAN) signal. They may effectively avoid WLAN interference by exploiting dynamic characteristics of WLAN traffic. Representing the arrival/departure of WLAN users using an M/M/m/m queueing structure, we consider the characterization of large-scale dynamics of WLAN channel occupancy. We also consider the characterization of small-scale dynamics of WLAN channel occupancy by generating WLAN signal using a two-state semi-Markovian process. Simulation results show that the proposed model generates WLAN signal having similar statistical characteristics to those of real WLAN signal.

The accurate prediction of radio wave propagation is extremely important for wireless network planning and optimization. However, inexact matching between the traditional empirical model and actual propagation environments, as well as the insufficiency of the sample data required for training a deep learning model, lead to unsatisfactory prediction results. Our paper proposes a field strength prediction model based on a deep neural network that is aimed at a tiny dataset composed of the geographic information and corresponding satellite images of a target area. This model connects two pretrained networks to minimize the parameters to be learned. Simultaneously, we construct a convolutional neural network (CNN) model for comparison based on a previous advanced study in this field. Experimental results show that the proposed model can obtain the same accuracy as that of previously developed CNN models while requiring less data.

Semantic communication has attracted significant attention as a key technology for emerging 6G communications. Though it has lots of potentials specially for high volume media communications, still there is no proper quality metric for modelling the semantic noise in semantic communications. This paper proposes an autoencoder based image quality metric to quantify the semantic noise. An autoencoder is initially trained with the reference image to generate the encoder decoder model and calculate its latent vector space. Once it is trained, a semantically generated/received image is inserted to the same autoencoder to create the corresponding latent vector space. Finally, both vector spaces are used to define the Euclidean space between two spaces to calculate the Mean Square Error between two vector spaces, which is used to measure the effectiveness of the semantically generated image. Results indicate that the proposed model has a correlation coefficient of 88% with the subjective quality assessment. Furthermore, the proposed model is tested as a metric to evaluate the image quality in conventional image coding. Results indicate that the proposed model can also be used to replace conventional image quality metrics such as PSNR,SSIM,MSSIM,UQI, VIFP, and SSC whereas these conventional metrics completely failed in semantic noise modelling.

In this study, an eye blinking re-identification system was proposed. A fast local binary pattern was used for feature extraction because its grayscale invariance and rotational invariance allow for the effective acquisition of feature information even in the presence of noise. Finally, a recurrent neural network and long short-term memory were used for model training. The results indicated that, compared with the model trained using static data, the models based on dynamic features were less affected by environmental noise in terms of accuracy. In addition, the model trained using the recurrent neural network was highly effective in identifying unenrolled users and achieved high overall accuracy.

An explosion accident in an enclosed cable trench caused by the discharge of 10 kV three-phase cable joints is discussed. Combined with the disassembly analysis, the fault recording data analysis, and the validation of experiment, the evolution of the cable joint fault is deduced and discussed. The results show that the trigger of the cable joint fault is the creepage discharge at the XLPE-SiR insulation interface. This results in the partial breakdown and the grounding failure of three-phase cable joints. Under the long-term floating potential and current thermal effect, the insulations are gradually ablated and decomposed into a large amount of combustible gas. Finally, the accumulated combustible gas is ignited by the arc caused by a three-phase short circuit at the moment of the reclosing operation. The analytical method and conclusions proposed in this paper can provide suggestions and guidance for the analysis of similar fault accidents in the future.

High level of fault currents has become one of the most challenging issues, along with increased electrical energy consumption and power grid expansion. However, some of the previous work was limited to installing limiters on designs and installing them on small plans. In this study, the optimal placement of a resistive-type superconducting fault current limiter (RSFCL) to achieve good power system transient stability is proposed. The proposed method has several advantages. First, procedure fault analysis is applied to consider the magnitude of the short-circuit currents and the line located close to the generator for the placement of the device. Compared with other methods, it can directly find the exact line to be installed RSFCL. Second, the stability assessment is validated using the time domain simulation by installing limiters with varying capacities in any location. Third, parameters were analysed to evaluate the proposed method’s effectiveness, including angular speed, rotor angular velocity, and generator rotor angle in a multi-machine system. The result reveals that the placement of the limiter on the line with the most significant short-circuits value on the line closest to the generator increases the power system stability more than the placement in other locations.

Driven by the demand for carbon emission reduction and environmental protection, battery exchange stations (BES) with battery energy storage stations (BESS) and renewable energy have become one of the key technologies to achieve the goal of emission peaking and carbon neutrality. Therefore, this paper proposes a strategy to optimize the operation of BES with Photovoltaics (PV) and BESS supplied by transformer spare capacity. Firstly, it introduces the operation mechanism of BES and uses the spare capacity of building special transformers and the roof PV to supply power to BES to avoid the investment of transformers. Secondly, this paper establishes the load model of BES and proposes the charging rules of battery exchange. Thirdly, a segmented pricing mechanism for the rental price of special transformers is formulated to guide BES operators to preferentially rent spare capacity during low load rate periods. Aiming at the maximum daily profit of BES, an optimization model is established to optimize the number of batteries to be charged and the charging status of BESS in each period, on this basis, the demand response model is proposed. Simulation results show that the proposed strategy can improve the daily profit of BES. And the configuration of BESS can improve the battery exchange capacity. Moreover, the exponential segmented pricing mechanism can effectively guide BES to rent the spare capacity under low and medium load rates of special transformers.

Optical and acoustic stereo imaging has great potential for the precise and consistent localization of intervention underwater robots; however, it is still being explored due to its sensing limitations and various technical challenges. This study presents a novel localization method by combining an inertial navigation system and an optical and acoustic stereo imaging system. As a strategy for localization correction relative to underwater structures, the robot’s pose is estimated based on a single acoustic image using a sonar simulator for mid-range localization, and a robust visual tracking using a 3-D wireframe model is employed for high-precision localization near the target structures. The performance of the proposed technique was demonstrated through experimental validation using real data obtained from a test tank.

This letter studied the phase error in the frequency-interleaving digital-to-analog converter (FI-DAC) and proposed a comprehensive phase estimation method. Firstly, the model of FI-DAC system was established, and the phase error were considered that consists of three parts: time delay error, initial phase error and nonlinear phase error. By analyzing the phase function characteristics of the system, the least squares (LS) was used to linearly fit the phase functions of the non-overlapping bands to estimate both the time delay and initial phase of each sub-band channel. Then, the nonlinear phase error of the system was estimated by the difference between the system phase function and the estimated linear phase function. Finally, the effectiveness of the proposed estimation method was verified by the built FI-DAC test bench.

Integration of Distributed Energy Resources (DERs) in power systems exacerbates the existing information problems between power utilities and regulators. DER policies oblivious to the trilemma of information asymmetry between power utilities, DER aggregators, and regulators result in distorted price signals to DER investors, and socially inefficient DER roll-out. Therefore, in this paper, we propose a game-theoretic approach for modeling information asymmetry in distribution network information and consumer data between the DER aggregators and the power utilities. The proposed framework is based on Single Leader Single Follower (SLSF) games, reformulated as Mathematical Programs with Equilibrium Constraints (MPECs), and solved using the Scholtes’s relaxation technique. Our results, based on the 7-bus Manhattan power network, show that unless the DER aggregators have complete information about the distribution network characteristics, the welfare along with the realized installed capacity of DERs in the system decreases. Moreover, progressively decreasing DER investment costs alleviate the effects of information asymmetry, suggesting that early adopters face disproportionately high welfare losses attributed to incomplete information between the DER stakeholders. Hence, policy interventions to alleviate the rampant information problems are imperative to ensure an optimal DER roll-out.

Energy routers based on the electronic power transformer are suitable for the AC-DC hybrid grid with multiple voltage levels, but their structures are complex. This paper proposes a novel energy router based on the multi-winding line frequency transformer. By a combination of a multi-winding line frequency transformer and power electronic devices, the proposed energy router can take advantage of the high reliability of the multi-winding line frequency transformer and the high controllability of power electronic devices. The proposed energy router is suitable for the AC-DC hybrid grid with multiple voltage levels and has the characteristic of a simple structure. The simulations and experimental results demonstrate the effectiveness of the proposed energy router.

In this letter, a novel sext-band bandpass filter (BPF) using two split-type multiple-mode resonators is proposed. The filter is composed of two sets of resonators which are respectively designed to realize Filter 1 with two passbands and Filter 2 with quad passbands. Then the two BPFs are combined by parallel coupling feed lines for sext-band responses, and each of the six centre frequencies in the proposed sext-band BPF is able to be controlled due to the design freedom. To validate the design and analysis, a prototype filter has been fabricated with six passbands centered at 1.88/2.59/3.48/5.26/5.82/6.75 GHz. The measured result of the fabricated filter agrees well with the simulation, which shows that the proposed structure is a good candidate for sext-band BPF designs and validates the proposed design flow well.

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.

In this letter, the angular jamming effect of electronically controlled corner reflectors after a maneuver aircraft is analyzed. Motion characteristics of the corner reflectors towed by a high-speed aircraft are calculated through the Lagrange theory, and the angle measurement error of the jamming method for the monopulse angle measurement algorithm is analyzed based on the motion states and the electromagnetic states (scattering or penetration) of the corner reflectors. Results show that the measured angle by a monopulse radar seeker can be effectively interfered by this method under complex maneuver motion while the automatic tracking is employed.