Recently, distributed generators (DGs) have been widely integrated into distribution network, so that the distribution network is gradually transforming into an active distribution network (ADN). Due to the influence of meteorological conditions, the output of DGs has high uncertainty. At the same time, considering the increasing variety of loads in ADNs, the uncertainty of load demand of user side is also increasing. In order to fully consider the uncertainty of measurement and quantitatively evaluate the operational status, this paper proposes a steady-state analysis method for ADNs under uncertain conditions. Firstly, this paper proposes a steady-state analysis method including power flow analysis model and evaluation indicators for the operation status from the perspectives of node and network. Secondly, the uncertainty factors are elaborated from three aspects: sources, impact on evaluation index and impact on scheduling. The evaluation indicators considering uncertain conditions, the impact on system security and scheduling of network are further discussed. Finally, through the simulation analysis of the modified IEEE 33-node test system, The effectiveness of the proposed method are verified.

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.

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.

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.

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.

Causal analysis paves the way for more interpretable assessment of complex systems and phenomena, such as human-in-the-loop components of energy systems. This paper will pursue novel approaches for causal analysis of prosumers' behavior. The knowledge of this causality is core for multiple smart grid applications including but not limited to the design of demand side management programs, retail electricity market design, development of effective distributed energy resources aggregation strategies, and net load forecasting. The complex nature of human interactions with energy relies on many factors and understanding behavior causality is a core, unsolved challenge. This paper presents a probabilistic algorithm for discovering causal relationships between the end users' consumption flexibility and its influencing factors. The obtained causal knowledge is then utilized to boost the precision of demand flexibility prediction. Two causal-oriented approaches are proposed to enhance the performance and interpretability of predictive models, incorporating causal information through causal regularization and data preprocessing. Simulation results demonstrate that the algorithm can effectively identify causal probabilities among different factors and unveil key characteristics of the prosumers' behavior. Additionally, these proposed causal-oriented approaches outperform the non-causal oriented predictive models in terms of both performance and interpretability, highlighting the advantages of incorporating causal information.

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.

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.

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 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.

Transmission line planning is affected by a variety of geographic information factors, and traditional path planning methods are difficult to meet the future intelligent planning needs. In this paper, based on the GIS platform for geographic information data processing, the triangular fuzzy number analytic hierarchy pro-cess (TFN-AHP) is utilized for weight analysis to construct the cost layer. Meanwhile, guided mechanism, dimension-reducing mechanism, and avoidance mechanism are proposed to improve the ant colony opti-mization (ACO) algorithm, to shorten the planning time and improve the route planning effect. Finally, a local area in Guizhou Province is selected for case study and compared with the results of traditional ACO, and the results prove the effectiveness of the transmission line planning model proposed in this paper.

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%.

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.

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.

In order to improve the measurement accuracy of mutual inductance parameters of transmission lines in the en-vironment of double circuit power lines without power outage, this paper establishes simulation models for measuring mutual inductance parameters of transmission lines using the different frequency method in two modes: equal and unequal zero sequence self-parameters of two circuit lines, using a parallel zero sequence coupling model of double circuit power lines. In the article, simulation analysis is conducted on the line parameters with a coupling coefficient between 0.4 and 0.6 and a line length of 20km-50km.In order to further verify the correctness of the simulation model and measurement methods, a test bench was established based on the principle of line mutual inductance parameter testing in a laboratory dual circuit line without power outage environment for experimental testing. By comparing the test values under experimental conditions with the standard values, it has been proven that the model and method can meet the actual measurement requirements of engineering.

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.

Focusing solely on enhancing the resilience of power systems at a systemic level would lead to a significant underestimation of the actual impact of extreme disasters. Equally vital is the assurance of livelihood security amidst such extreme conditions, which is crucial for the development of a truly resilient power system. Hence, this paper attempts to incorporate quantifiable metrics assessing public safety impacts into resilience enhancement works, thereby guiding precise allocation of funds. Considering that the residents’ intuitive feelings are the most direct reflection of the severity of the disaster, this paper employs the modified prospect theory to formulate functions representing residents’ psychological risk perception and risk-taking willingness to tolerate risks during disruptions in power, gas, and water supplies. Meanwhile, in order to accurately calculate the energy loss duration for each residential customer, a resilience enhancement method for post-disaster collaborative dispatch of electricity-gas-water systems (EGWSs) is proposed. With the objective of minimizing the public safety and economic impact of disasters, the optimal multi-source collaborative emergency restoration strategy is developed. The significant necessity and efficiency of the proposed strategy are verified with exhaustive case studies. Numerical results evince the resilience enhancement by considering the livelihood security in the post-disaster restoration stage.

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.