A novel approach is proposed in this paper to reconstruct the far-field radiation pattern from the phaseless electric field of an antenna scanned on a single near-field sphere. It adopts the dipole equivalence approach to project the near-field electric field into a spherically distributed array of electric dipoles. We introduce a term representing the residue from the linearly correlated portion in the least-square problem associated with the dipole equivalence, namely the linear correlation residue (LCR). It is demonstrated that by iterative search to minimize the LCR using the Covariance Matrix Adaptation Evolution Strategy (CMA-ES), near-field phase distributions can be found efficiently from the magnitude-only near-field, and the far-field radiation pattern can be computed. Two representative case studies are given here to validate the proposed method. Results demonstrate good agreements between computations and simulations.

High Resolution Range Profiles (HRRP) have become a key area of focus in the domain of Radar Automatic Target Recognition (RATR). Despite the success of deep learning based HRRP recognition, these methods needs a large amount of training samples to generate good performance, which could be a severe challenge under non-cooperative circumstances. Currently, deep learning based models treat HRRP as sequences, which may lead to ignorance of the internal relationship of range cells. This letter introduces HRRPGraphNet, whose pivotal innovation is the transformation of HRRP data into a novel graph structure, utilizing a range cell amplitude-based node vector and a range-relative adjacency matrix. This graph-based approach facilitates both local feature extraction via one-dimensional convolution layers, global feature extraction through a graph convolution layer and a attention module. Experiments on the aircraft electromagnetic simulation dataset confirmed HRRPGraphNet’s superior accuracy and robustness, particularly in limited training sample environments, underscoring the potential of graph-driven innovations in HRRP-based RATR. Codes are available at: https://github.com/MountainChenCad/HRRPGraphNet.

This brief investigates the multi-point fault repair problem in active distribution networks, establishing resilience assessment metrics and constructing a fault repair mathematical model with road network coupling. An improved genetic algorithm is proposed, utilizing a comparison crossover operator and population information entropy. Compared to traditional algorithms, the comparison crossover operator preserves high-quality genes, while population information entropy maintains diversity, preventing the algorithm from converging on local optima. Simulation analysis demonstrates that the proposed fault repair method can restore normal power supply to distribution network lines within a short period and offers significant advantages in fault repair tasks.

In this paper, we tackle the challenging problem of multi-target cross localization within a novel distributed high-frequency surface wave radar (HFSWR). This system consists two transmitter-receiver pairs, each of which can correspondingly generate a set of Range-Doppler measurements. By associating the Range-Doppler measurements from the same target, the individual target location can be derived using the associated measurements. However, when multiple targets are involved, it is unknown which measurements originate from the same target, leading to ambiguities in the measurement-to-measurement association. False associations can result in the ghosts, ultimately degrading the overall system performance. To deal with the ghosts, we propose a three-stage deghosting method that incorporates the geometric characteristics of the system, the velocity features of the targets and the motion features of the targets across multiple frames. ̵‌The simulation results demonstrate the effectiveness of our proposed method compared with the other investigated deghosting algorithms. Our proposed approach exhibits robust target detection capability while significantly reducing both the OSPA error and the cardinality error.

This paper introduces a novel resource allocation algorithm, Priority-Aware Federated Resource Allocation (PAFRA), tailored for Power Line Communication (PLC) systems. Utilizing a federated learning framework, PAFRA optimizes the distribution of limited spectrum resources among multiple nodes within a residential environment. The algorithm employs a priority-based time slot allocation to manage subchannel conflicts and uses a Double Deep Q-Network (DDQN) for local training at each node, incorporating state, action, and reward configurations to refine transmission power and subchannel selections. Extensive simulations demonstrate that PAFRA significantly enhances system throughput across various Signal-to-Noise Ratio (SNR) levels, outperforming existing adaptive resource allocation strategies and random allocation methods. The findings highlight PAFRA’s ability to achieve superior performance in dynamic PLC environments, illustrating its potential to optimize network efficiency while adhering to strict regulatory emission standards.

Characterizing soil moisture (SM) around drip irrigation pipes is crucial for precise and optimized farming. Machine learning (ML) approaches are particularly suitable for this task as they can reduce uncertainties caused by soil conditions and the drip pipe positions, using features extracted from relevant datasets. This letter addresses local moisture detection in the vicinity of dripping pipes using a portable microwave imaging system. The employed ML approach is fed with two dimensional images generated by two different microwave imaging techniques based on spatio-temporal measurements at various frequency bands. The study investigates the performance of K-Nearest Neighbor (KNN) and Convolutional Neural Networks (CNN) algorithms for moisture classification based on these images in three scenarios: before clutter removal, after clutter removal, and after applying imaging techniques such as back projection and the Born approximation. We also explore the potentials of CNN and KNN for moisture estimation around the plant roots and in the presence of pebbles. The results demonstrate the more accurate moisture estimation using CNN when it is applied after clutter reduction considering back projection algorithm (BPA) as the imaging technique. The results indicate that using the Back Projection technique for image formation, combined with CNN for classification, improves leak detection accuracy by approximately 20% compared to other methods.

This study developed a damped alternating current (DAC) testing methodology, composed of an improved DAC generator and a pulse-based distributed PD-detection technique. The improved DAC generator lacks high voltage (HV) solid-state switches and is thus a cost-efficient solution for field-testing voltage generation via a direct current–alternating current (DC-AC) conversion technique. A capacitor bank is used for power supply to achieve the requirement for high instantaneous power. In addition, the use of a distributed PD detection technique enables high sensitivity and makes the method free from restrictions of the cable length. To reduce the construction cost of the distributed PD-detection system, a pulse synchronization technique is used. Attempts was made to reduce the weight based on the simulations, and a prototype with the total weight of 850kg was designed and developed for a 64/110kV power cable. Laboratory and field tests validated the effectiveness of the cost-efficient DAC testing methodology.

Faults in power systems occur for various reasons, such as aging or natural disasters. Detecting, locating, and promptly clearing these faults is crucial for maintaining the safety and reliability of transmission lines. Distance relays, which protect transmission lines, detect faults, estimate their location, and send the required commands. However, these relays may experience mis-detection due to manipulated impedance arising from both internal and external factors. These factors include measurement device errors, network topology changes, the presence of fault resistance, and injected currents from remote line terminals. To address this challenge, we propose an innovative adaptive protection scheme that considers fault resistance, changes in network topology, and injected current from the opposite end of the line. By estimating the equivalent circuit impedances of the network connected to the terminal of the transmission line, this protection scheme utilizes impedance estimation techniques at the line terminals and offline network information. Simulation studies (tested on the IEEE 39-bus standard network) show that the proposed scheme accurately estimates fault location and fault resistance with high precision. The simulation results demonstrate its effectiveness in improving the performance of conventional distance protection relays in both the first and second protection zones (Z_1 and Z_2).

The correct design of the generator circuit breaker (GCB) is essential for the safe and reliable operation of a power plant or generation system. The design is usually based on standardized calculation methods according to standards (IEC standard 60909-0, IEC/IEEE standard 62271-37-013), often supplemented by selected transient calculations. A non-systematic approach can often be observed here, which does not adequately take into account significant influencing variables or operating states of the generator. This article therefore systematically examines various parameters that influence the short-circuit current components of the generator and are relevant for the design of the generator circuit-breaker: Short-circuit angle, operating point, phase clearing, switching arc, and fault arc. The results of the current parameters most relevant to the dimensioning of the GCB were then compared for different calculation methods. Particular attention was paid to the influence of the switching arc and fault arc, which is modeled in a transient calculation, and its effect on the short-circuit currents is systematically recorded. This work aims to summarize all relevant variables that influence the generator short-circuit current and are relevant for the dimensioning of the GCB and to present the different results based on a short-circuit calculation according to the standard and transient calculation to create a basis for a proper dimensioning of the generator circuit breaker.

Wind energy systems require fault diagnosis that identifies faults despite data inconsistencies. This study addresses challenges in supervisory control and data acquisition (SCADA) systems for monitoring wind turbine conditions from imbalanced data representation and error vulnerability. It examines the efficacy of Adaptive Elite-Particle Swarm Optimization (AEPSO)-tuned Extreme Gradient Boosting (XGBoost) on an imbalanced SCADA dataset for wind turbine fault classification. The methodology integrates the resampled SCADA dataset with t-distributed Stochastic Neighbour Embedding represented deep learning features. Employing AEPSO-XGBoost classifier trained on merged SCADA and deep learning data from a physics-informed deep convolutional neural network forms the basis of the fault classification model. The AEPSO-XGBoost regressor is validated across three distinct rear bearing temperature datasets, facilitating parameter optimization and model robustness. Also, this study explores supervised and unsupervised anomaly detection models using PDCNN and AEPSO-XGBoost with rear-bearing temperature data. Findings exhibit substantial fault classification and prediction enhancements by merging resampled SCADA data with deep learning features. Moreover, results show that applying AEPSO-XGBoost can significantly improve anomaly detection metrics. Through AEPSO-XGBoost’s efficacy in enhancing fault prediction within imbalanced SCADA datasets, the study proposes an integrated framework for fault classification and anomaly detection as an innovative predictive maintenance system for wind energy systems.

The single transmitter and multiple receivers (STMR) mobile radar system is a special distributed radar system, which has stronger detection and survival capabilities. However, the high-speed movement of the radar platforms easily produce the across-range cell migration (RCM) of radar echo signal, which cause envelope shift effects within the coherent processing interval (CPI). In this paper, we proposed a motion compensation method for the STMR mobile radar system in the sea environment, to solve the problem of RCM. Firstly, the target points and clutter points with the same Doppler velocity are obtained by the radon Fourier transform (RFT) method. Secondly, using the range / Doppler difference between clutter and target under multi-radar views, the target and clutter points are separated by the spatial range mapping. The simulation results show that the method can effectively improve the coherent accumulation effect of the target in the sea environment with low signal-to-clutter ratio (SCR).

The detection of defects on the copper chain in the production process of tobacco cutters is crucial for ensuring product quality. Traditional defect detection methods often rely on spatial domain image analysis, which not only has a large computational load but also performs poorly in handling high-frequency noise and complex backgrounds. To address this issue, this paper proposes a novel neural network model based on frequency domain analysis, called Frequency Domain Attention Network (FDANet). This network first utilizes Discrete Cosine Transform (DCT) to transform the image from the spatial domain to the frequency domain, effectively reducing computational complexity and improving processing speed. Subsequently, through the innovative Frequency Domain Attention Module (FDAM), the network automatically identifies and enhances key discriminative features in the frequency domain, thereby strengthening the model’s ability to identify defects. Finally, the frequency domain attention map, after feature extraction and integration, is inputted into the coupling detection head to achieve high-precision defect detection. Experimental results demonstrate that FDANet performs excellently in the task of detecting copper chain defects in tobacco cutters, showing significant improvement compared to traditional methods and verifying the effectiveness and practicality of the proposed approach.

Satellite panel is an important part of satellite, and its modeling accuracy and reliability are very important for satellite design and manufacturing. However, how to improve the accuracy and reliability of satellite panel modeling has become a key issue to be solved urgently in the field of satellite technology. In this research, based on the sandwich sandwich theory, a new finite element model for satellite panels is constructed, which can numerically express the multi-layer structure, material properties and their interactions of satellite panels, and more accurately predict and simulate the behavior of satellite panels under various conditions. In the process of model building, Lanczos method is used to analyze the modal of the satellite solar panel model, and the frequency coincidence analysis between the analysis results and the real mode is carried out. The results show that the frequency error of the first five modes is less than 5%, which indicates that the established finite element model of satellite solar panel has high accuracy. It can well capture the modal characteristics of satellite solar panel in the vibration process. Using Modal confidence and MAC (Modal Assurance Criteria) to assess, conform to the degree of its MAC value is above 0.9, conform to the Modal correlation standard. To sum up, the satellite panels finite element modeling method is appropriate, has the high accuracy and reliability, can better meet the needs of design and manufacture of satellite, promote the sustainable development of satellite technology and progress

Reconfigurable intelligent surfaces (RISs) have attracted a great deal of interest due to the potential contributions to the next-generation wireless networks. This letter proposes an enhancement to the physical layer security (PLS) of a multi-hop RIS-assisted underwater optical wireless communication (UOWC) system. Owing to the complexity of the underwater environment, a security-based adaptive RIS (SA-RIS) clustering strategy, which aims to reflect optical signals among clusters to improve the performance of the overall system, is evaluated. By combining the underwater channel model, the closed-form expressions of probability density function (PDF) and cumulative distribution function (CDF) are derived. Moreover, by increasing the numbers of RIS clusters, the performance metrics such as secrecy outage probability (SOP) and average secrecy capacity (ASC) are evaluated under different scenarios. The obtained results demonstrated that, in contrast to the case without preventing the eavesdropper, the proposed strategy in evasion scenarios could improve the SOP significantly. It can be concluded that the system secrecy performances are further improved by assigning different RIS clusters with proper channel quality.

With the development of the digital economy, the power demand for data centers (DCs) is rising rapidly, which presents a challenge to the economic and low-carbon operation of the future distribution network. To this end, this paper fully considers the multiple flexibility of DC and its impact on the distribution network, and establishes a collaborative planning model of DC and distribution network. In this model, the interval method is utilized to capture the inherent uncertainties within the system (such as the renewable energy source (RES) output, wholesale market price, power load demand, carbon emission factor and workloads), and the planning model is transformed into a multi-objective optimization problem with interval parameters problem (IMOP) to minimize economic cost and carbon emission. On this basis, an interval multi-objective optimization evolutionary algorithm based on decomposition (IMOEA/D) is proposed to solve the IMOP and obtain the Pareto optimal solution while retaining all the uncertainty information. Finally, an improved IEEE 33-node distribution network is utilized as an example for simulation and analysis to confirm the efficacy of the proposed approach.

The global energy landscape is undergoing a profound transformation, marked by an unprecedented integration of renewable sources. This paradigm shift brings forth the challenge of low inertia in power systems, posing significant uncertainties to grid stability and reliability. This paper addresses these challenges and proposes innovative solutions to ensure the resilience of future transmission networks. The paper leverages advanced modeling techniques, including dynamic simulation models and control methods to analyze real-world case studies, mainly focusing on wind power plants operating as hybrid plants with integrated energy storage systems and participating in reserves markets to provide frequency response. The analysis includes adapting the Nordic equivalent power system model, allowing a deeper understanding of the dynamics of low-inertia environments and the impact of renewable energy integration. The aim is to provide valuable insights into the complex interactions within low-inertia power systems and highlight the importance of adapting power systems to ensure resilience in evolving energy scenarios. The paper contributes to the ongoing discourse on building sustainable and reliable future transmission networks through empirical analysis and theoretical modeling. It emphasizes technical strategies, operational advancements, and policy considerations essential for navigating the challenges posed by the transition to renewable energy sources.

To address the key challenges of insufficient comprehensive extraction and fusion of meteorological conditions, temporal features, and periodic characteristics of power in short-term power prediction of distributed photovoltaic (PV) stations, a TPE-CBiGRU model prediction method based on multi-scale feature fusion is proposed. Firstly, multi-scale feature fusion of meteorological features, temporal features, and hidden periodic features in PV power is performed to construct model input features. Secondly, CNN and Bi-GRU are utilized to model the feature relationships between PV power and its influencing factors from spatial and temporal scales, respectively, and the spatial-temporal features extracted are fused through an Add network. Finally, the Bayesian hyperparameter optimization method is adopted to further optimize network parameters, achieving the prediction of single-station PV power. Validation using measured data from a certain PV station shows that the proposed method enhances the comprehensiveness of feature information extraction from both data and model layers, significantly improving the accuracy of short-term PV power prediction. Compared with other prediction models, the Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) are reduced by 26.11% and 35.64%, respectively, and the R-squared (R2) is increased by 3.07%.

The tribological behaviors of nanoparticles (NPs) have received extensive application in the fields of nano-lubrication and ultra-precision manufacturing. The frictional and dynamic behaviors of SiO2 NPs acted with the single asperity were studied on silicon surface utilizing atomic force microscope. Both the static and kinetic friction forces of NPs exhibit an initial decrease followed by an increase with the increasing manipulation load (0 ~ 300 nN). The nonmonotonic load-dependence of friction behavior matches the dynamic transformation of “sliding-rolling-sliding” motion state of the manipulated NPs, which can be predicted by a Double-Hertz model and further confirmed by the nanoindentation marked NPs. This research has a great guiding significance for the regulated dynamic behaviors of NPs in the current three-body abrasive tribology.