Search Results (78)

Power system parameters are susceptible to multiple influencing factors such as environmental conditions and load current, with line parameters being notably affected. This compromises the accuracy of power flow calculation and fault analysis, and can significantly undermine the reliability of protection schemes. To address these limitations, this study proposes an integrated correction method for power system line parameters via a framework that combines soil resistivity inversion and multi-factor sag calculation. First, based on fault-recording data from external line faults, sequence impedance parameters are calculated using a two-terminal impedance difference subtraction strategy, followed by the inversion of soil resistivity along the transmission corridor. Second, considering the spatial inhomogeneity of the transmission corridor, a sliding-window statistical method is applied to segment the line, and a piecewise series model is employed to correct the zero-sequence impedance parameter. Finally, a conductor temperature and sag model based on the heat balance equation is established. By coupling ambient temperature, wind speed, solar radiation, and mechanical load, the ground capacitance and susceptance parameters are dynamically corrected. Simulation results demonstrate that the proposed framework can systematically achieve dynamic correction of power system line parameters and significantly reduce calculation errors. The developed method provides an effective technical pathway for enhancing the accuracy of power system simulation and improving the reliability of protection schemes. Full article

Hybrid transmission corridors combining overhead lines and underground cables introduce impedance discontinuities that significantly modify electromagnetic transient behavior. These discontinuities generate traveling-wave reflections, waveform distortions, and high-frequency components at relay measurement locations during the first microseconds following disturbance inception. This paper presents a waveform-level electromagnetic transient (EMT) analysis of overhead–cable transition effects using detailed EMTP-RV simulations including frequency-dependent line and cable models, tower representations, grounding systems, and instrument transformers within a differential protection measurement framework. The results show that overhead–cable transitions produce transient waveform modifications characterized by reflections, attenuation, dispersion, and temporary current imbalance mechanisms associated with traveling-wave propagation and cable capacitive effects. The analysis also demonstrates the transient evolution of instantaneous waveform-derived (EMT-derived) differential and restraining current quantities, defined as combinations of terminal current signals obtained directly from EMT waveforms. These quantities do not represent final phasor-domain operating values of practical numerical relays, but provide insight into the transient electromagnetic environment preceding conventional filtering and phasor estimation. The study contributes to a clearer physical interpretation of transient phenomena in hybrid transmission systems and supports EMT-based evaluation of signals relevant to differential protection applications. 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

Given the expanding scale of offshore wind power development, strict spatial constraints on offshore platforms and multi-source power coupling present operational challenges during the collaborative transmission of near-shore and far-offshore wind power through a shared corridor. To address these issues, this paper proposes a collaborative transmission scheme based on the Self-Adaption Statcom and Line-Commutation Converter (SLCC). The technical and economic characteristics of three typical topologies—Modular Multilevel Converter (MMC) onshore grid connection, MMC direct transmission, and SLCC direct transmission—are compared and analyzed. The results demonstrate the advantages of the SLCC scheme in reducing the offshore platform footprint and lowering engineering costs. Furthermore, a hierarchical collaborative control strategy is designed to mitigate the power coupling between near-shore AC wind generation and far-offshore DC wind generation at the converter bus. The bottom layer utilizes a valve-side parallel Static Var Generator (SVG) to achieve reactive power self-balance and quasi-resonant suppression of specific harmonics. In the top layer, an LCC active power-following control strategy based on instantaneous power feedback is implemented. This achieves the logical decoupling of near-shore and far-offshore wind power transmission. The effectiveness of the proposed scheme in managing wind power fluctuations, riding through AC faults, and maintaining stable operation under weak grid conditions is verified using the PSCAD/EMTDC software. Full article

The increasing deployment of shared transmission corridors for High-Voltage Alternating Current (HVAC) and High-Voltage Direct Current (HVDC) systems has intensified the need to evaluate electromagnetic compatibility in hybrid overhead line configurations. This study presents an analytical methodology to estimate the electric field magnitude and magnetic flux density generated by hybrid HVAC–HVDC transmission lines under steady-state operating conditions. The electric field is determined using the Maxwell potential matrix combined with the image method, while the magnetic field is obtained from a formulation based on the Biot–Savart law. Two representative case studies were analyzed with identical electrical operating conditions but different transverse conductor arrangements to evaluate the influence of geometry on the electromagnetic environment of the corridor. The results show that variations in the spatial configuration of the conductors produce noticeable changes in the location and magnitude of the electric and magnetic field maxima across the right-of-way. These findings demonstrate that conductor geometry plays a key role in the electromagnetic behavior of hybrid corridors and should be considered in the design and assessment of HVAC–HVDC transmission systems. Full article

Typhoon-induced wind loads pose severe threats to transmission systems. However, existing resilience assessment approaches typically rely on sparse meteorological station data and assume spatially uniform wind speed distributions along transmission corridors, which fail to capture the span-level spatial difference of wind fields. To address this limitation, this paper proposes a distributed optical fiber sensing (DOFS)-driven span-level resilience assessment and hardening optimization framework for transmission networks. First, a phase-sensitive optical time domain reflectometry (Φ-OTDR)-based distributed optical fiber sensing system is employed, utilizing optical fibers embedded in existing OPGW cables as sensing media. By capturing vibration responses of the fiber induced by wind–structure interaction, real-time spatiotemporal wind speed sequences at the individual span level are reconstructed through signal processing and inversion algorithms, providing high-spatial-resolution environmental input data for resilience evaluation. Second, a span-level failure probability quantification method is established using a load–strength interference model. On this basis, a resilience evaluation framework—“span-level asset damage cost—line-level critical corridor identification—system-level load shedding assessment”—is constructed, enabling cross-scale resilience quantification from component damage to system-level performance degradation. Third, a span-level gradient hardening optimization model is developed. By adopting a scenario pre-calculation and iterative updating strategy, coordinated solving of reinforcement decisions and failure scenarios is achieved, thereby maximizing resilience enhancement benefits. The proposed framework is validated using DOFS-measured wind speed data collected from a 500 kV transmission line along the Fujian coast during three real typhoon events—Typhoon Shantuo, Typhoon Trami, and Typhoon Koinu—supporting the reliability of the acquired span-level wind speed information. Case studies conducted on a modified IEEE RTS-24 system demonstrate that the proposed span-level hardening strategy can substantially reduce reinforcement cost compared with the conventional line-level hardening strategy. In the reported benchmark case, it achieves zero load-shedding penalty with a markedly lower hardening cost, and under the same budget constraint, it further yields lower expected load shedding and lower expected asset damage. Full article

The wildfires in the vicinity of the power transmission corridors are famous for their sudden occurrence, rapid growth, and susceptibility to interference from fire-like interferences at night, which can easily lead to line discharge and trip accidents, thus affecting the safe operation of the power system. In order to address the issue of the high false alarm rate and poor generalization performance of wildfire image recognition in complex power transmission corridor environments, a wildfire image recognition method based on an improved AlexNet is proposed in this paper. The proposed method improves the description of flame and smoke properties at different scales by designing a reparameterized multi-scale feature extraction structure, and effectively alleviates the influence of strong light reflection and fire-like interference at night by using lightweight multi-scale attention and hybrid pooling attention mechanisms. A wildfire image dataset is constructed based on 1246 on-site images of the power transmission corridor captured by a visual monitoring device and 600 wildfire images downloaded from the internet, and tested in real-world imbalanced distribution scenarios. The experimental results show that the proposed method can recognize wildfire images with an accuracy of 96.9% and an F1 value of 94.9% on the test dataset, which is much higher than that of the original AlexNet, and has a strong ability to adapt to cross-dataset tests. The research work can provide technical support for online monitoring and operation and maintenance of wildfires in power transmission corridors. Full article

Transmission lines traversing forested areas pose significant fire risks, necessitating timely and efficient inspection mechanisms. Traditional manual patrols and cloud-based UAV inspections suffer from high latency, bandwidth dependence, and delayed response times. To address these challenges, this study proposes an integrated, real-time UAV-edge computing system for the early identification of fire risks and structural hazards along transmission corridors. The system integrates a DJI M300 RTK UAV with a Manifold 2-G edge computing unit (based on NVIDIA Jetson TX2), deploying a lightweight, TensorRT-optimized YOLOv8 model. By leveraging FP16 precision quantization and operator fusion, the system achieves a real-time inference speed of 32 FPS on the embedded platform. Furthermore, a custom Payload SDK integration ensures automated image acquisition and closed-loop data transmission via a dual-mode (4G/5G + Wi-Fi) communication link. Field experiments demonstrate that the system significantly reduces data transmission latency while maintaining high detection accuracy (mAP > 94%), providing a robust and replicable solution for intelligent power grid maintenance in resource-constrained environments. Full article

Reliable fault detection along transmission corridors is essential for preventing small defects from developing into long outages and costly emergency operations. This study aims to improve the field reliability of an open vocabulary vision language backbone without retraining the large model in an end-to-end manner. The work focuses on four operational fault classes in multi-region corridor imagery collected during routine inspections and uses a Florence-2 vision language model as the base recognizer. On top of this backbone, three domain-specific components are introduced. A subclass-aware fusion scheme keeps probability mass within the active parent concept so that insulator icing and conductor icing produce stable, action-oriented decisions. A Power-Line Focus Then Crop normalization uses an attention-guided corridor window together with isotropic resizing so that thin conductors and small fittings remain visible in the processed image. A corridor geo prior reduces scores as the distance from the mapped centerline increases and in this way suppresses detections that lie outside the corridor. All methods are evaluated under a shared preprocessing and scoring pipeline in training-free and parameter-efficient tuning modes. Experiments on unseen regions show higher accuracy for skinny and low-contrast faults, fewer false alarms outside the right-of-way, and improved score calibration in the confidence range used for triage, while keeping throughput and memory usage suitable for unmanned aerial vehicles and substation edge devices. 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

Voltage stability assessment under transmission contingencies is traditionally performed using severity-based indices evaluated on isolated outage scenarios. While effective for identifying extreme events, such approaches provide limited insight into which transmission corridors structurally govern voltage-stress behavior across the full contingency space. This paper introduces a persistence-based diagnostic framework for voltage stability assessment under exhaustive $N-1$ line contingencies, using the Fast Voltage Stability Index (FVSI) as a base indicator. Rather than ranking lines by instantaneous severity, the proposed methodology identifies dominant transmission lines—defined as those attaining the maximum FVSI in each convergent contingency—and aggregates these outcomes statistically to quantify dominance persistence, conditional severity, and dispersion. A dominance concentration metric ($k_{90}$) is introduced to measure how many transmission corridors are sufficient to explain the majority of dominant voltage-stress events. The framework is applied to IEEE 14, 30, and 118-bus benchmark systems under exhaustive $N-1$ enumeration. Results reveal a clear phenomenon of dominance collapse: as system size increases, dominant voltage-stress outcomes concentrate onto an extremely small set of transmission corridors. While IEEE 14 exhibits partial dominance dispersion ($k_{90}=2$), both IEEE 30 and IEEE 118 demonstrate near-total dominance collapse ($k_{90}=1$), where a single corridor governs more than 90% of dominant FVSI events. The proposed approach is fully deterministic, scalable, and independent of control or optimization assumptions, making it well-suited for planning-stage screening, monitoring prioritization, and pre-filtering of large-scale contingency studies. By shifting voltage stability analysis from severity-only screening to persistence-based structural diagnosis, this work provides new insight into vulnerability concentration in modern transmission networks. Full article

Automated positioning of transmission tower keypoints is crucial for drone-based intelligent inspection systems. This paper proposes TPKE (Transmission Pylons Keypoint Extraction), a novel framework designed to extract multiple transmission tower keypoints from LiDAR point clouds. The method targets two core components: insulator string endpoints and ground wire hanging points. For insulator positioning, TPKE introduces adaptive density clustering, a morphological “concavity” index (η) for V-shaped insulators, and a “positioning-verification-compensation” strategy for handling missing data. For ground wire positioning, it combines local geometric feature analysis with spatial orthogonal projection. Using semantic segmentation for preprocessing, the framework reliably identifies components from complex transmission corridor point clouds. Validated on 1427 towers across 14 types, TPKE achieves an MAE of 0.0747 m for insulators and 0.0696 m for ground wires. It maintains centimeter-level accuracy even under challenging conditions like sparse point clouds. With an average processing time of 3.03 s per tower, the method demonstrates high efficiency, significantly reducing manual annotation workload while supporting autonomous navigation for transmission line maintenance. Full article

Severe surface deformation induced by super-large mining height longwall extraction poses a significant threat to the safe operation of high-voltage transmission towers. In this study, a 330 kV straight-line transmission tower located above the 122104 working face of the Caojiatan Coal Mine was selected as a case study to investigate tower stability under mining-induced surface deformation and to develop corresponding protection technologies. An integrated monitoring system combining instantaneous and long-term measurements was established to characterize surface movement throughout the mining process. The results indicate that the maximum surface subsidence reached 7300 mm, while the maximum inclination and curvature attained 50 mm/m and 0.62 mm/m², respectively, reflecting intense deformation of the overlying ground. Numerical simulations based on ANSYS 2021R1 were conducted to systematically evaluate the effects of surface inclination, compressive deformation, and tensile deformation on the structural response of the transmission tower. The critical deformation thresholds leading to structural failure were identified as 30 mm/m for inclination, −7.2 mm/m for horizontal compression, and 7.7 mm/m for horizontal tension. Based on these findings, a comprehensive protection system was proposed, integrating tower body reinforcement, combined foundation reconstruction, surface subsidence monitoring, dynamic jacking-based rectification, and foundation grouting reinforcement. The proposed scheme was successfully implemented in field practice. Monitoring results demonstrate that, after reinforcement and rectification, differential settlement of the tower foundation was controlled within 20 mm, and tower inclination remained below 1‰. This ensured uninterrupted underground mining operations and continuous power transmission within the Caojiatan Coal Mine corridor. The outcomes of this study provide a practical reference for the protection of high-voltage transmission towers under similar mining conditions. Full article

Semantic segmentation of transmission-line point clouds is fundamental to intelligent power inspection and grid asset management, as segmentation accuracy directly influences defect detection and facility assessment tasks. However, transmission-line point clouds collected across different voltage levels often show significant variations in density and geometric structure due to heterogeneous LiDAR sensors and flight configurations. Combined with the high cost of large-scale manual annotation, these factors limit the scalability of existing supervised segmentation methods. To overcome these challenges, we propose a geometry-consistency-guided unsupervised domain adaptation framework tailored for cross-voltage transmission-line point-cloud segmentation. The framework employs KPConvX as the backbone and integrates three progressive components. First, a geometric consistency constraint enhances robustness to spatial variations and enables extraction of structural features invariant across voltage levels. Second, a domain feature alignment module reduces distribution shifts through global feature transformation. Third, a minimum-entropy-based pseudo-label refinement strategy improves the reliability of pseudo-labels during self-training. Experiments on a multi-voltage transmission-line dataset demonstrate the effectiveness of the proposed method. With the KPConvX backbone, the framework achieves 66.1% mean Intersection over Union (mIoU) and 94.3% overall accuracy on the unlabeled 110 kV target domain, exceeding the source-only baseline by 15.6% mIoU and outperforming several state-of-the-art UDA methods. This work provides an efficient, annotation-friendly solution for cross-voltage point-cloud segmentation and offers a promising direction for domain adaptation in complex power-grid environments. Full article