Search Results (202)

This paper introduces a methodology for evaluating the voltage stability margin in high-voltage direct-current (HVDC) systems, which analyzes the singularity of the power flow Jacobian matrix—computed via the Newton—Raphson method—and identifies saddle-node bifurcations. The continuation power flow method is employed to model progressive load increases, enabling the continuous tracking of power flow solutions and the determination of voltage collapse points. Within this framework, the system’s behavior is analyzed under contingency conditions, particularly transmission line outages, assessing its capability to maintain secure operating conditions under increasing demand scenarios. The main objective is to identify the most critical line in the system, defined as that which leads to the greatest reduction in loadability when unavailable, prior to voltage collapse. This approach allows for the early identification of structural vulnerabilities, supporting decision-making processes aimed at risk mitigation and operating cost optimization. The proposed methodology is validated using two systems: the six-terminal CIGRE-B4 HVDC system and an 11-node HVDC test feeder. Full article

Insulators are essential for the secure and uninterrupted functioning of high-voltage transmission lines. However, since insulators are exposed to the outdoor environment, they are inevitably affected by environmental conditions such as icing. Accumulation of ice on insulator surfaces adversely impacts insulation efficacy and elevates surface leakage currents, resulting in power outages. This research presents a spectrogram-based convolutional neural network (CNN) model for identifying icing conditions on the surfaces of glass, porcelain, and silicone insulators. Insulators are labeled in three classes under laboratory conditions: ice-free, slightly iced (t < 12 mm), and iced (t > 20 mm). High voltage was applied at three distinct levels ranging from 10 to 50 kV, considering the icing conditions of each insulator, and leakage current signals were recorded. The Butterworth and smoothing filters were first applied to the leakage current signals, which were then transformed into spectrogram images using the Fourier transform and used as input for the created CNN architecture. Additionally, spectrogram images were also applied to AlexNet, GoogLeNet, and ResNet-50 architectures. The suggested CNN architecture attained an accuracy of 97.78% to 100% across all operating situations for glass and silicone insulators while demonstrating a classification success rate of 82.22% to 100% for porcelain insulators. Experiments indicate that the accuracy rates of established models in the literature (AlexNet, GoogLeNet, and ResNet-50) diminished to as low as 73%, particularly in porcelain insulator data, thereby validating the developed model’s proficiency in differentiating during icing detection processes and its adaptability to varying conditions. Full article

This paper presents a comprehensive review of voltage stability challenges in South Africa’s constrained power grid, particularly in the context of rising hybrid renewable energy integration. With the growing deployment of inverter-based resources (IBRs) like solar PV, wind, and battery energy storage systems (BESS), especially under programmes through the Independent Power Procurement Office, voltage stability has emerged as a key concern, particularly in weak grid areas like the Northern Cape Province. We highlight how weak grids characterized by low short-circuit capacity, long transmission lines, and limited reactive power support are more susceptible to voltage instability, especially with high penetration of non-synchronous generation. Using a modified IEEE 14-bus system with hybrid generation, the study simulates a weak grid scenario. Findings point to significant reactive power losses and capacitive over-voltages in long and lightly loaded lines, mirroring some of the weak-grid-transmission challenges experiences in an area of the South African power grid. The study underscores the importance of dynamic load modelling (e.g., ZIP and exponential models) and inverter behaviour in stability analysis. It concludes that hybrid systems, when optimally designed and integrated with storage, can help support grid stability. However, proactive planning, advanced modelling, and compliance with evolving grid codes remain essential for securing reliable renewable integration. Full article

Voltage stability under transmission line contingencies is a critical concern in modern power systems, as the growing electricity demand and the large-scale integration of renewable energy sources increasingly challenge the security of network operation. This paper addresses the problem of estimating the voltage stability margin under $N-1$ transmission line contingencies through three solution methodologies: a nonlinear programming formulation solved via an interior-point algorithm (IPOPT) with a multi-start strategy, a recursive heuristic approach based on successive Newton–Raphson power flow solutions with progressive load scaling, and a convex second-order cone programming relaxation. The proposed methods are validated on the IEEE 9-, 14-, 30-, and 57-bus test systems, thereby covering networks of varying topological complexity and redundancy. A comparative analysis evaluates the accuracy of each approach against a nonlinear programming reference, as well as their computational efficiency under a comprehensive set of contingency scenarios. The results indicate that the heuristic method achieves higher precision, while the convex formulation offers a substantially faster solution, with both approaches demonstrating robustness in cases where the nonlinear programming method fails to converge. Full article

Weak-bus identification is a key task for online security assessment, preventive control, maintenance verification, and resilience-oriented dispatch of interconnected power grids. In large-scale grids, conventional full-graph graph neural networks preserve the complete network topology but may become inefficient when many operating scenarios must be screened repeatedly. Direct graph partitioning improves computational tractability, but it may cut tie-line channels and weaken the boundary evidence that determines cross-area risk propagation. To address this trade-off, this paper proposes a boundary-compensated partition-based parallel graph neural network for weak-bus identification. The method first constructs a scenario-aware weighted power-grid graph and divides it into electrically coherent subgraphs under coupling-strength and partition-size constraints. Local graph encoders are then executed in parallel to learn intra-partition vulnerability representations. A boundary compensation module further restores cross-partition information by weighting tie-line neighbors according to electrical coupling, branch loading, and cross-area association. Standardized partition scores are finally fused into a whole-grid weak-bus ranking, and a composite learning objective jointly considers node-score regression, boundary consistency, and pairwise ranking stability. The method is evaluated on the IEEE 57-bus benchmark with mechanism-based node and branch vulnerability labels. Compared with the original full-graph GNN, the proposed method reduces the mean square error from 0.0359 to 0.0147, improves the Spearman rank coefficient from 0.248 to 0.446, and increases Hit@10 from 30% to 70%. Topological interpretation further shows that the identified weak buses are concentrated around high-risk branches such as 8-12, 12-14, 0-14, and 7-8, indicating that the proposed framework captures local aggregation, boundary transmission, and corridor-driven vulnerability propagation. The IEEE 57-bus benchmark is used as a focused validation case because it provides aligned node- and branch-level vulnerability evidence for evaluating weak-bus ranking behavior. Because the available aligned vulnerability evidence is concentrated in this medium-scale benchmark, the results should be interpreted as a focused validation of the proposed ranking mechanism rather than as a complete large-system scalability study. Full article

Accurate transmission-line parameters are essential for reliable, efficient, and sustainable smart grid operation, especially under increasing renewable-energy integration and data-driven grid management. However, line aging, temperature variations, and measurement outliers may cause significant deviations between actual and nominal grid models, thereby degrading the state estimation, power-flow analysis, and operational security assessment. To address these challenges, this paper proposes a robust transmission-line parameter estimation method based on a variable-projection framework. The proposed framework decomposes the original high-dimensional, strongly coupled, and non-convex joint estimation problem into two subproblems associated with line-parameter identification and operating-state calibration. An iteratively reweighted least-squares algorithm based on the Huber M-estimator is introduced to dynamically adjust measurement weights and suppress the influence of outliers. The preconditioned conjugate-gradient method is further employed to avoid the explicit inversion of large-scale normal matrices. Simulations on the IEEE 118-bus system demonstrate that the proposed method achieves a higher parameter-estimation accuracy and stronger robustness than conventional weighted least-squares and joint state-parameter estimation methods. In the base case, the proposed method reduces the RMSRE of line reactance to 0.0794%, compared with 0.1558% for WLS and 0.1126% for JSE. Under the representative 5% gross-error case, the proposed method maintains lower RMSREs of 0.9772%, 0.0875%, and 5.8536% for $R_l$, $X_l$, and $B_{sh}$, respectively. Further sensitivity tests under contamination ratios from 1% to 20%, outlier magnitude factors from 1.5 to 5.0, and different outlier-location patterns confirm that the proposed method maintains a more stable estimation accuracy than WLS, conventional JSE, and Huber-JSE without VPM under diverse bad-data conditions. In downstream operational evaluations, it reduces the branch active-power flow RMSE from 1.6842 MW to 0.7215 MW, voltage-magnitude RMSE from 0.00482 p.u. to 0.00216 p.u., and active-power-loss error from 2.4368% to 0.9327% compared with WLS. These quantitative results indicate that the proposed approach can improve the grid model accuracy under imperfect measurements, thereby supporting reliable and sustainable smart-grid operation. Full article

Unmanned aerial vehicles (UAVs) are expected to serve as core nodes for next-generation communication networks, while the broadcast nature of line-of-sight (LoS) links makes the security of transmissions a server problem, which is more prominent for a mobile eavesdropper scenario. In this paper, the security enhancement of UAV swarm communication is considered. Specifically, a UAV swarm with aerial base stations attempts to transmit confidential information to terrestrial nodes, and a mobile eavesdropper lurking nearby tries to approach better receiving points to intercept communications. For the purpose of enhancing the security of transmissions utilizing spatial freedom, a virtual antenna array is formed by the UAV swarm, and a multi-agent proximal policy optimization (MAPPO)-based approach is developed to jointly optimize the collaborative beamforming and cooperative trajectories of the UAV swarm under a maximum power constraint. The simulation results demonstrate the capability of the proposed method to direct the UAV swarm to transmit mission information directionally and validate the superiority of security performance compared to benchmarks. Full article

Electric vehicles (EVs) provide fast charging/discharging flexibility; however, single-layer assessments may overestimate the flexibility that can be physically delivered under downstream distribution-network constraints. This paper proposes a process-oriented hierarchical adjustable-potential assessment framework for transmission–distribution–microgrid coordination. At the microgrid/station layer, a chance-constrained vehicle feasible set is constructed to capture user uncertainty, and probabilistic Minkowski-sum aggregation is used to obtain a station-level theoretical envelope. At the distribution layer, voltage and line-thermal constraints are modeled using LinDistFlow and intersected with the theoretical envelope to derive an effective potential satisfying network security limits. At the transmission layer, the effective feasible region is further packaged into a time-varying generalized-battery parameter set for consistent upward reporting without introducing dispatch optimization. In addition, a bottleneck truncation effect (BTE) metric is defined to quantify how distribution constraints reduce upstream-usable flexibility. Case studies show that hierarchical network constraints compress both peak EV flexibility and the all-day feasible-region area. Specifically, the microgrid-layer theoretical envelope reaches 432 kW on the charging side, 124 kW on the discharging side, and 3799 kWh in feasible-region area. After distribution-layer security clipping, the effective envelope becomes 299 kW, 124 kW, and 2063 kWh, corresponding to reductions of 30.79%, 0.00%, and 45.70%, respectively, relative to the microgrid layer. After transmission-layer packaging, the deliverable envelope is further reduced to 285 kW, 118 kW, and 1946 kWh, i.e., reductions of 34.03%, 4.84%, and 48.78%, respectively, relative to the microgrid baseline. These results demonstrate that the proposed workflow provides verifiable and time-varying deliverable capability boundaries for cross-layer EV flexibility assessment. Full article

A Controllable Line-Commutated Converter (CLCC) is a novel piece of equipment for enhancing the commutation failure resistance of High-Voltage Direct Current (HVDC) transmission systems. Traditional lumped parameter models ignore the high-frequency coupling effects of internal distributed stray capacitances, resulting in insufficient transient simulation accuracy and restricting refined engineering design. Taking the CLCC in the HVDC transformation project as the research object, this paper analyzes the distribution characteristics of stray parameters in a press-pack Insulated Gate Bipolar Transistor (IGBT) under stacked structures. By integrating distributed stray parameter networks with the nonlinear characteristics of the devices, an improved IGBT equivalent circuit model is established, with key parameters identified based on field-measured data. Furthermore, an LCC-CLCC simulation model is built and used to replace the improved IGBT model to conduct short-circuit fault simulation verification. The results demonstrate that the high-fidelity model accurately reproduces transient waveforms under Alternating Current (AC) voltage disturbance and faithfully reflects the actual operating characteristics of a surge arrester and IGBT, thereby effectively compensating for the idealized errors inherent in traditional models. This modeling methodology provides a robust theoretical and simulation foundation for parameter optimization, valve control system design, and the secure operation of a CLCC. Full article

The demands of competitiveness in global markets require the integration of Industry 4.0 (I4.0) digital technologies for any manufacturing company, regardless of size. Industrial operations require complete supply chain visibility to ensure data protection and authenticity throughout the process. This document presents a distributed architecture based on RAMI 4.0, designed for product traceability in industrial environments. It integrates automation tools, IIoT communication, cloud storage, artificial intelligence, and secure data transmission using encrypted communication protocols. The system consists of a hybrid architecture; only the first, lower-level layer corresponds to a simulated manufacturing plant with deterministic and stochastic dynamics within the production line. In the second part, the middle and upper layers are implemented, where plant data is transmitted to a cloud instance, stored in a PostgreSQL database, and subsequently analyzed using automated scripts. Reporting capabilities are incorporated with ChatGPT-3.5 Turbo, and visualization is provided through Odoo. Experimental tests demonstrated an average end-to-end communication latency of less than 200 ms, a packet loss rate of 2.67%, and 100% reliability in verifying requested reports when using the cognitive computing service. Furthermore, the results of the systematic vulnerability identification process for the architecture show a significant reduction in overall risk for most assets, with a predominant shift from high or moderate to low or moderate. The proposed architecture is validated in a simulated industrial environment under controlled conditions, demonstrating its viability as a prototype rather than as a fully implemented industrial solution. Full article

Background/Objectives: We developed a telehealth-enabled fiber-bundle endomicroscopy platform and evaluated its preclinical feasibility for targeted fluorescence imaging in cervical cancer models. Methods: The platform integrates a portable fiber-bundle endomicroscopy (FBE) system, fluorescein isothiocyanate (FITC)-labeled candidate peptides, and a secure web-based telehealth platform for remote consultation. The FBE probe achieved a field of view of 1,700 µm and a lateral resolution of 4 µm, enabling cellular-level fluorescence imaging in a compact, portable format. Four FITC-labeled peptides (SHS1*, SHS2*, FPP*, and CRL*) were evaluated in A549, SiHa, and CaSki cell lines. Ex vivo testing was performed on commercial cervical tissue-array samples. The telehealth platform was assessed for secure medical-image/video transmission and end-to-end latency in a simulated remote-consultation setting. Results: Among the tested probes, FPP*-FITC and CRL*-FITC showed higher fluorescence-positive fractions in the p16-overexpressing cervical cancer cell lines than in the A549 comparator line, with the strongest signals observed in CaSki cells. In ex vivo testing, CRL*-FITC generated higher fluorescence intensity in malignant cervical tissue-array samples than in non-malignant comparator tissues, with a reported 4.6- to 7.4-fold difference in mean signal intensity (p < 0.001). The telehealth platform supported the secure transmission of medical images and video and demonstrated an end-to-end latency of <500 ms in a simulated remote consultation setting. Conclusions: These results support the technical and preclinical feasibility of integrating targeted fluorescence imaging, portable fiber-bundle endomicroscopy, and telehealth into a single platform. This study should therefore be interpreted as a preclinical feasibility study evaluating optical, molecular, and telehealth integration, rather than as a clinically validated cervical cancer screening test. Full article

Precise segmentation of transmission equipment is crucial for ensuring secure power grid operation, yet practical deployment faces substantial challenges including the preservation of elongated morphological characteristics of transmission lines and accurate boundary localization for complex transmission tower structures. This paper proposes a novel segmentation method that synergistically integrates cross-directional convolutions with multi-layer attention mechanisms within the YOLO11 framework. The designed C3x cross-directional convolution module incorporates orthogonal convolutional operations during feature extraction, enabling independent enhancement of feature responses along horizontal and vertical dimensions. This architecture effectively captures continuous morphological characteristics of elongated targets while mitigating fragmentation artifacts. Additionally, the proposed Multi-Layer Cascaded Attention (MLCA) module employs a progressive fusion strategy combining spatial and channel attention, significantly augmenting the network’s capacity to extract multi-scale semantic information while maintaining computational efficiency. This design particularly enhances boundary detail preservation for structurally complex targets. Experimental evaluations on the TTPLA dataset (comprising 1232 images across 4 categories) demonstrate remarkable performance improvements: bounding box detection achieves 72.56% mAP@0.5 and mask segmentation reaches 68.37% mAP@0.5, representing gains of 2.97% and 4.52% respectively over the baseline YOLO11 model. The Mask F1 score improves from 67.85% to 71.76%, comprehensively validating the proposed method’s effectiveness in enhancing segmentation capabilities for both elongated and morphologically complex targets. These results substantiate the practical applicability of the proposed approach for intelligent transmission infrastructure monitoring systems. Full article

Escalating global climate change has intensified the frequency and scale of wildfires in mountainous regions hosting transmission line infrastructure. These conflagrations act as extreme meteorological events, capable of generating localized heatwaves that compromise the air insulation of power lines and trigger protective relay operations, thereby posing systemic threats to regional grid stability. To enhance wildfire early-warning efficacy for grid security, this study formulates wildfire early warning for power transmission corridors as a regression-based risk prediction problem and proposes a hierarchical “global screening–local refinement” risk assessment framework. The primary contribution of this study lies in the integration of a machine-learning-based global wildfire risk screening model with tower-level spatial refinement using geographically weighted regression (GWR), enabling coordinated global–local wildfire risk characterization along power transmission corridors The framework employs a predictive model built on a Gradient Boosting Decision Tree algorithm, integrating geospatial and statistical analyses. A global risk model, utilizing historical data from the Himawari-8 satellite alongside meteorological, topographic, and anthropogenic variables, produces a composite risk index. This index is spatially interpolated via Kriging to generate stratified wildfire risk maps for broad-area assessment. For precise corridor-level analysis, these Globally Projected Risk Indices, along with localized terrain features, inter-tower clearance distances, and proximity to historical ignition points, are incorporated into a Geographically Weighted Regression model. This yields a spatially calibrated wildfire risk index along critical routes. The results show that the GBDT-based model achieved the best predictive performance among the evaluated regression models, with an R² of 0.626 and a mean squared error of 0.178. This approach offers a scientifically robust and operationally viable reference for wildfire prevention strategies in power line maintenance. Full article

Voltage security assessment is increasingly challenged by stochastic demand growth and localized stress patterns that are not well represented by deterministic, single-snapshot analyses. This paper proposes a fully steady-state probabilistic stress-testing framework based on Monte Carlo simulation and Newton–Raphson AC power flow, jointly evaluating the minimum bus voltage magnitude $V_{min}$ (voltage-floor adequacy) and the scenario maximum Fast Voltage Stability Index $FVS{I}_{max}$ (worst-case line stress). Stress is injected selectively on screened weak buses by sampling a random stress footprint and intensity across three progressive levels (L1–L3), while preserving the local power factor. The approach is demonstrated on IEEE 14-, 30-, and 118-bus benchmark systems using $N=2000$ realizations per level, with 100% convergence across all cases. Across all systems, results show a consistent, monotone degradation of the voltage floor and a systematic increase in violation risk as stress intensifies. For the IEEE 14 system, the voltage-risk profile escalates rapidly, with $\mathbb{P}(V_{min}<0.90)$ rising from 0.16 (L1) to 0.54 (L3), while the worst-case FVSI tail strengthens markedly (p95 increasing from 0.1455 to 0.2081), indicating a growing likelihood of severe voltage-stress events. In contrast, the IEEE 30 and IEEE 118 systems exhibit milder shifts in central voltage levels but maintain substantial exposure relative to the 0.95 pu planning threshold, with $\mathbb{P}(V_{min}<0.95)$ reaching 0.79 and 0.74 at L3, respectively. Beyond risk magnitudes, the framework reveals a nontrivial structural phenomenon in worst-case line stress: as system size increases, stochastic stress outcomes become increasingly concentrated into a small number of dominant transmission corridors. Recurrence analysis at the highest stress level shows fragmented criticality in IEEE 14 (Top-3 lines sharing criticality), near-total dominance by a single corridor in IEEE 30 (>92% of cases), and complete dominance collapse in IEEE 118 (one corridor governing 100% of $FVS{I}_{max}$ events). These results demonstrate that probabilistic stress-testing can simultaneously quantify voltage-risk escalation and expose hidden structural bottlenecks that remain invisible under deterministic screening, providing a scalable diagnostic tool for planning-stage monitoring and reinforcement prioritization. Full article

Automated insulator inspection systems face critical challenges from small object sizes, complex backgrounds, and vulnerability to adversarial attacks, a security concern largely unaddressed in safety-critical power infrastructure. We introduce Faster-YOLOv12n, integrating a FasterNet backbone with SGC2f attention modules and Wise-ShapeIoU loss for enhanced small defect localization. Our architecture achieves 98.9% mAP@0.5 on the CPLID, improving baseline YOLOv12n by 1.3% in precision (97.8% vs. 96.5%), 4.7% in recall (95.1% vs. 90.4%), and 1.8% in mAP@0.5. Through differential data augmentation, we expand training samples from 678 to 3900 images, achieving balanced class distribution and robust generalization across fog, adverse weather, and complex transmission line backgrounds. Comparative evaluation demonstrates superior performance over RT-DETR, Faster R-CNN, YOLOv7, YOLOv8, and YOLOv9, with per-class analysis revealing 99.8% AP@0.5 for defect detection. We provide the first comprehensive adversarial robustness evaluation for insulator defect detection, systematically assessing FGSM, PGD, and C&W attacks across perturbation budgets. Through adversarial training with mixed-batch strategies, our robust model maintains 93.2% mAP@0.5 under the strongest FGSM attacks ($ϵ$ = 48/255), 94.5% under PGD attacks, and 95.1% under C&W attacks ($\tau$ = 3.0) while preserving 98.9% clean accuracy, demonstrating no trade-off between accuracy and robustness. Grad-CAM visualizations demonstrate that attacks disrupt confidence calibration while preserving spatial attention on defect regions, providing interpretable insights into model decision-making under adversarial conditions and validating learned feature representations for safety-critical smart grid monitoring applications. Full article