Search Results (2,063)

This systematic narrative review examines recent advances in the application of swarm robotics and swarm intelligence to smart grid systems in the context of renewable energy sources. Smart grids represent a transformative paradigm in power systems, integrating advanced communication, monitoring, and control technologies to enhance the reliability, performance, and sustainability of power distribution. Swarm robotics, drawing inspiration from the collective behaviour of social insects, enables the coordination of numerous autonomous agents to carry out complex tasks in a decentralised, scalable, and fault-tolerant manner. Over the past decade, swarm intelligence approaches—including Particle Swarm Optimisation, Ant Colony Optimisation, and consensus-based distributed control—have emerged as promising methods for addressing smart grid challenges such as decentralised energy management, fault detection, infrastructure monitoring, and maintenance. This review was conducted through a two-stage systematic search of three databases (Scopus, IEEE Xplore, and ACM Digital Library; Web of Science was not accessible during the search period and was therefore excluded), which identified 54 primary studies meeting the full-text inclusion criteria, supplemented by 11 additional records located through hand-search, for a final corpus of 65 references. For each application domain (monitoring and inspection, energy distribution optimisation, fault detection and resilience, and cybersecurity and communication), we summarise the methodologies employed, the reported performance outcomes, and the evidence level of the available studies. We also analyse the scalability limits of current swarm approaches, communication constraints relevant to grid deployment, and the integration of swarm systems with existing SCADA and EMS infrastructure. The review identifies a significant gap between laboratory demonstrations and utility-scale deployment, and outlines priority directions for future research. Full article

To address spatial confusion and semantic collapse in maintenance knowledge graph construction for long-distance belt conveyor systems, this study proposes a linear topology-aware alignment framework driven by large language models. The framework constructs a Physical–Space–Fault–Rule ontology and introduces a normalized linear topology mapping mechanism to assign unique spatial anchors to highly homogeneous components. For noisy and colloquial maintenance logs, a Condition–Constraint–Action–Space (CCAS) extraction mechanism is developed to reconstruct implicit fault causal chains and spatial information from unstructured text. To prevent erroneous merging of components with similar semantic descriptions but different physical locations, the LTA-Alignment algorithm integrates Gaussian topological penalties with LLM-based grey-zone arbitration. A structured gold-standard annotation protocol is further established using maintenance logs, BOM records, spatial anchors, and closed-loop repair evidence. Experiments over three predefined runs show that CCAS achieves a global F1-score of 94.10 ± 0.56%, exceeding T5 by 6.20 percentage points. LTA-Alignment achieves a Merge F1-score of 95.75 ± 0.53% and an entity cluster purity of 96.70 ± 0.53%, exceeding SCSA by 3.36 and 3.90 percentage points, respectively. In downstream fault traceability, the dynamic spatiotemporal knowledge graph achieves an Accuracy@1 of 92.78 ± 0.36%, 10.16 percentage points higher than R-GCN, together with an estimated 68.50 ± 0.73% reduction in rule-based simulated troubleshooting time relative to Keyword Retrieval. The results demonstrate that combining normalized spatial topology with LLM reasoning improves entity disambiguation, fault-causal reconstruction, and traceability performance under the evaluated non-branching belt conveyor maintenance setting. Full article

The Moliqing Fault Depression, located in the southwestern part of the Yitong Basin, is a Cenozoic strike–slip pull-apart basin controlled by the northern segment of the Tan-Lu Fault Zone. Based on measured formation temperature data from 41 wells and rock thermophysical property data from 41 wells in the study area, terrestrial heat flow values were calculated using a one-dimensional steady-state heat conduction model. On this basis, temperature distribution maps at different depths, a terrestrial heat flow contour map, a formation temperature distribution map of the Shuangyang Formation, and well-to-well geothermal profiles were systematically constructed. The principal factors controlling the geothermal field were further investigated in terms of basement relief, basement lithology, and the spatial distribution of volcanic vents. The results indicate that the geothermal field in the study area exhibits pronounced lateral heterogeneity. Subsurface temperatures range from 39 to 66 °C at a depth of 1000 m, from 65 to 133 °C at 2000 m, and from 91 to 182 °C at 3000 m. The geothermal gradient ranges from 3.0 to 4.9 °C/100 m, with an average of approximately 3.6 °C/100 m. Terrestrial heat flow values range from 58 to 122 mW/m², averaging approximately 84 mW/m². Measurements of rock thermophysical properties show that thermal conductivity ranges from 1.9 to 2.7 W/(m·K), with an average of 2.3 W/(m·K). Radiogenic heat production ranges from 0.985 to 1.3 μW/m³, averaging 1.1 μW/m³. Well-to-well geothermal profiles indicate that basement relief exerts a strong control on the distribution of isotherms. The relatively high radiogenic heat production of the basement granites provides an important internal heat source for the geothermal field. Geothermal gradients increase markedly near volcanic vents, reflecting the perturbing effects of deep-seated thermal activity. These findings provide a scientific basis for hydrocarbon exploration and geothermal resource assessment in the Moliqing Fault Depression. Full article

DC distribution networks employing a low-current grounding method offer high power supply reliability. However, when a single-pole grounding fault occurs on a feeder, the fault current characteristics are not distinct, making accurate fault identification and feeder protection challenging. Prolonged operation with the fault may cause insulation damage, potentially leading to pole–pole short-circuit faults and escalating the incident. Therefore, equipping the system with reliable and rapid feeder protection is crucial. This paper analyzes the fault current in a two-level VSC-based DC distribution system under single-pole grounding faults, clarifies the relationship between the transient current of the feeder and the line capacitive current, and further investigates the variation patterns of fault transient current and Teager-based signal-feature characteristics. Based on this, a single-pole grounding fault protection method utilizing the Teager instantaneous-current feature is proposed. The Teager energy operator is employed to extract the instantaneous current feature of each feeder, and a protection criterion is constructed using the dimensionless feature ratio between the faulty pole and the non-fault pole to identify the faulty feeder. A simulation model of the DC distribution network is built using MATLAB/Simulink. Simulation results show that the proposed protection method can adapt to various scenarios, including different line faults, fault locations, fault resistances, and noise, while demonstrating satisfactory performance. Full article

Public charging operators typically identify abnormal charging outcomes only after a session terminates, limiting proactive maintenance and reducing service reliability. This study develops a plug-in-time machine-learning pipeline that uses only information available when a charging session begins. The analysis uses 441,077 public charging sessions from 92 charging posts in Jiaxing, China, with 44 calendar, weather, tariff, location, user-history, and charging-post-history features. Eight classifiers are evaluated primarily with rolling-origin chronological validation. HistGradientBoosting achieved a mean PR-AUC of 0.598 and ROC-AUC of 0.819 on the four 2021 test quarters. A stricter fold-local repeated-cross-validation audit, in which validation outcomes never update user or post histories, ranked XGBoost first with a mean PR-AUC of 0.583. Charging-post recent abnormal history was the dominant predictor, but a single-feature post-history baseline achieved a pooled PR-AUC of 0.511 compared with 0.604 for the full HistGradientBoosting pipeline. Flagging the highest-risk 1% of sessions achieved 98.4% precision and 5.3% recall; quarter-specific precision ranged from 95.7% to 99.5%. Lagged prior-quarter isotonic recalibration reduced expected calibration error to 0.016–0.021 in quarters F2–F4. These results support capacity-constrained monitoring and maintenance triage rather than exhaustive fault detection. Full article

Bearing fault diagnosis increasingly relies on multiple sensors, since compound or multi-location faults can produce signatures that are only partially captured by a single sensor. The decision-level fusion (DLF) of independently trained models offers a practical way to combine such complementary information, yet systematic comparisons across DLF techniques remain scarce, particularly for measurements from different sensor locations. This paper benchmarks six DLF strategies, i.e, Max, Average, Majority Voting, Weighted Sum, Dempster–Shafer, and Stacking, on a dual-branch one-dimensional convolutional neural network (1D-CNN) with each branch trained end-to-end on vibration signals from a distinct bearing location. On a two-sensor test bench covering seven health conditions, all methods exceed 99.7% accuracy on clean signals, while Dempster–Shafer fusion proves markedly more robust under noise, retaining up to 84% accuracy at a 5 dB signal-to-noise ratio (SNR). A conflict-coefficient analysis further provides an interpretable account of when fusion succeeds, linking performance to the confidence complementarity between branches. Full article

Traditional fault diagnosis methods for gas-insulated switchgear (GIS) equipment primarily rely on offline detection and periodic maintenance, which suffer from limitations such as poor real-time performance and localization difficulties, thereby compromising the safe and stable operation of ultra-high-voltage power grids. To enhance the accurate identification and localization capabilities of defects within GIS equipment, this study first establishes a multi-physics coupled simulation model integrating temperature field, flow field, and concentration field to analyze gas diffusion characteristics under varying conditions of fault source locations, decomposition product types, and initial concentrations. Subsequently, a GIS busbar gas chamber experimental platform is constructed to validate the simulation model. Finally, a response time matrix, a peak concentration matrix, and a fault coverage index are developed, and a weighted comprehensive evaluation method is employed to optimize sensor placement schemes. The findings reveal that fault source location significantly influences concentration response speed and spatial distribution patterns; SO₂, HF, H₂S, and SOF₂ exhibit distinct diffusion characteristics due to their differing physical properties; and initial concentration primarily affects the non-uniformity during the early diffusion stage. The simulation results demonstrate good agreement with experimental data, with a maximum root-mean-square error of 3.936 × 10⁻⁴. Monitoring point M4 achieves the highest comprehensive score, making it the preferred location for single-sensor deployment. These results provide a theoretical foundation and technical guidance for GIS online monitoring and fault diagnosis. Full article

In this paper, the solar irradiance is investigated in a part of central Serbia, where a low-cost IoT sensor network was deployed at three locations: a mountain slope, an open field near a village, and an obstructed position in the city center. All three locations are in the same NASA POWER grid cell. After multi-stage quality control, 26,145 valid daytime records were compared to the satellite reference. The satellite assigns identical values to all three positions but the measured mean daytime irradiances are 267.7, 351.5 and 19.6 W/m², respectively. The rural station is in the best agreement with the reference (R² = 0.567); the mountain station suffers from a persistent shading bias (MBE = −138.9 W/m²); the signal at the urban station is attenuated by surrounding buildings and vegetation by a factor of ~12. Also, 25 regression models (gradient boosting, recurrent, convolutional, fully connected and graph-based) were trained on the 25-year monthly NASA POWER record for the same cell. XGBoost obtained R² = 0.998, the hybrid TCN-GNN R² = 0.968 and a plain ReLU network R² = 0.912, and a seasonal model with calendar features was used to reconstruct the satellite reference for two months of 2026 not yet available in the archive. The deployed hardware, network and energy behavior are documented quantitatively: per-site link delivery ratios of 96.8%, 83.5% and 58.2%, the photovoltaic harvesting record of the nodes, and the absence of any energy-aware transmission scheduling. Two of the trained models were compiled for the ESP32 nodes and measured on the deployment hardware: a depth-limited gradient-boosted corrector runs in 46.3 µs using 11.1 kB of flash, and an INT8 fully connected network in 138.1 µs using 3.0 kB, while the graph hybrid cannot be converted for microcontroller execution at all. This defines an edge–cloud partition in which local inference performs bias correction and fault detection while the cloud tier retains the heavy models and periodic retraining. To the best of the authors’ knowledge, this is the first multi-site ground-based IoT irradiance record for central Serbia with such different terrain types. Full article

Internal temperature monitoring is an important approach for assessing the operating condition of power transformers. Temperature-rise characteristics not only indicate faults such as inter-turn short circuits but also indirectly reflect the diffusion and transport behaviors of characteristic dissolved gases within the transformer oil. To enable early warning and localization of inter-turn short circuits in transformer windings, this paper proposes a method based on monitoring the oil temperatures at the inlets and outlets of the transformer cooling system. First, a simulation model of a 110 kV transformer was developed, and coupled thermal–fluid simulations were performed to analyze the oil-temperature distributions in the transformer body and cooling system under normal operating conditions and representative inter-turn short-circuit conditions. Next, a simulation-based fault-localization strategy is proposed to investigate whether the spatial temperature distribution of cooling-system oil can be used to infer the vertical position; faulty phase; and, for certain fault locations, the winding side of an inter-turn fault. Finally, the temperature sensor locations were selected, and the performance of the localization strategy was evaluated. The simulation results demonstrate the feasibility of using the temperature distribution of cooling-system oil for the detection and localization of inter-turn short-circuit faults. Full article

Partial discharge (PD) detection is a crucial means for the early warning of incipient insulation defects in arresters. However, under strong electromagnetic interference and background noise, PD signals are prone to distortion, making it difficult to accurately determine the pulse onset front and thus severely degrading the accuracy of time-delay estimation. To address the difficulty of time-delay estimation under low signal-to-noise ratio (SNR) and multi-channel aliasing conditions, this paper proposes a method for arrester PD detection and high-precision time-delay estimation based on joint denoising and improved cross-correlation. First, a joint denoising strategy that integrates singular value decomposition (SVD), variational mode decomposition adaptively optimized by the sparrow search algorithm (SSA-VMD), and the Teager energy operator (TEO) is constructed. This strategy suppresses white noise and periodic narrowband interference while effectively extracting the oscillatory onset characteristics of PD pulses. Second, an enhanced time-delay estimation method based on HB-weighted generalized quadratic cross-correlation is introduced. By employing the dual mechanisms of HB frequency-domain weighting and amplitude weighting to sharpen the correlation peak, the estimation robustness under low SNR is improved. Simulation results show that the proposed method attains an accuracy of 99.9911%, significantly outperforming conventional cross-correlation, PHAT-SCOT, and NLMS methods. Finally, experiments are conducted on a needle-plate discharge platform. In multiple comparative experiments with different spatial distance differences (ranging from <30 cm to >50 cm), the maximum relative error is kept within 0.6%, verifying the reliability and accuracy of the proposed algorithm under controlled laboratory conditions. This method can provide a new approach for online monitoring and accurate fault location of arresters in power systems. Full article

This study evaluates a hierarchical workflow for fault diagnosis in transmission systems by integrating established machine-learning techniques for fault-type classification and zonal fault location with a complementary double-ended traveling-wave procedure for point-location estimation. The contribution lies at the level of process integration and comparative evaluation rather than in the proposal of a new machine-learning or traveling-wave algorithm. The methodology was evaluated using the IEEE 9-bus test system. Symmetrical and asymmetrical short-circuit scenarios were automatically simulated at multiple positions along six transmission lines using DIgSILENT PowerFactory, and the resulting oscillographic records were exported in COMTRADE format, producing a database of 2952 fault events. Phase voltages and currents, together with positive-, negative-, and zero-sequence components, were used to evaluate Decision Trees, Self-Organizing Maps (SOM), Artificial Neural Networks (ANN), and k-Nearest Neighbors (KNN) for fault-type classification and zonal fault location. Under the simulated noise-free conditions and the adopted fixed hold-out partition, all four algorithms correctly classified the 591 fault-type testing observations, yielding 100% test-set accuracy. This result characterizes the specific evaluation subset considered in the study; repeated, cross-validated, or grouped partitions were not performed, and neighboring simulated fault positions may therefore be represented across the training and testing subsets. For zonal fault location, the ANN exhibited the strongest and most consistent observed performance in the retained 590-event evaluation set, with class-specific recall values between approximately 0.96 and 0.99 across the six fault zones, whereas the Decision Tree provided a favorable compromise between zonal discrimination and computational efficiency. Some model-specific hyperparameter values from the original executions are unavailable in the retained experimental record, which limits exact replication of those original configurations; the reported results correspond to the evaluated executions documented in this study. As a complementary third component of the workflow, the double-ended traveling-wave procedure based on discrete wavelet analysis was illustrated for one AG event simulated at 25% of the transmission-line length, producing a normalized point-location estimate of approximately 25.07% from the local terminal. This single-event analysis demonstrates the operation of the traveling-wave processing sequence, while broader multi-event validation is outside the present experimental scope. Overall, the results demonstrate the coordinated application of fault-type classification, zonal fault location, and traveling-wave-based point-location refinement within a common diagnostic workflow. The findings should be interpreted within the deterministic simulation conditions, fixed evaluation subsets, and experimental records considered in this study. Full article

The integrated fusion and inversion of seismic data and marine controlled-source electromagnetic (MCSEM) data can identify the gas hydrate distributions. However, due to differences in observation systems and scales between seismic and MCSEM data, current fusion methods have failed to effectively address the critical issue of physical property variation within gas hydrate reservoirs. This research seeks to consolidate the two datasets into a cohesive observational framework. By transforming the MCSEM data into low-frequency constraints applicable to seismic impedance inversion, it is possible to realize an effective integrative interpretation that combines both seismic and MCSEM data. Using a 3 km-long seismic dataset and MCSEM data from the Shenhu Sea area in the South China Sea as a case study, we apply the Poisson blending algorithm to integrate seismic and MCSEM data, enabling precise identification and characterization of gas hydrate reservoirs and underlying gas-bearing fluids. The gas hydrate saturation results, derived from seismic inversion constrained by MCSEM data, demonstrate strong consistency with well logging and geological interpretation. This concordance validates the efficacy of the integrated fusion and inversion methodology and highlights its advantages in accurately predicting the spatial distribution of gas hydrate enrichment. The developmental positions of deep gas-bearing fluid pathways, coupled with the fault locations within the free gas zone and gas hydrate-bearing layer, play significant roles in the heterogeneous enrichment of gas hydrates. This research provides important technical and theoretical support for the precise and efficient prediction of gas hydrate reservoirs. Full article

The increasing penetration of inverter-based renewable energy resources is reshaping transmission-line fault characteristics and weakening protection criteria designed for synchronous-generator-dominated grids. This paper proposes an internal-fault identification scheme based on voltage–current coupling characteristic curves (UICs) constructed from voltage and current measurements at both line terminals. Geometric descriptors of the UIC are used to build an ellipsoidal feature space representing normal operating conditions and external faults. Internal faults are identified from the normalized distance between the online feature vector and this space. A local voltage-transient startup criterion is also introduced, and current-transformer (CT) saturation correction is incorporated to reduce distortion in the measured currents. PSCAD simulations under different fault locations, transition resistances, fault types, noise levels, and CT-saturation conditions show that the proposed scheme distinguishes internal faults from external faults and normal operation reliably. Because the criterion depends on line-side coupling features rather than the short-circuit output of a specific power source, it is suitable for protection applications in renewable energy systems with grid-forming inverters. Full article

By implementing a proactive reserve scheduling mechanism, distribution networks (DNs) can optimize emergency resource deployment to improve fault recovery resilience. To address the limitations of existing pre-disaster preparation strategies that only consider limited resources, this paper proposes a proactive scheduling strategy that integrates mobile resources and field personnel in a coordinated manner for DNs. By establishing a two-stage stochastic mixed-integer programming (SMIP) model for coordinated control of emergency resources in DNs, the first stage determines the quantity and location of mobile energy storage systems (MESSs), repair crews (RCs), and switching crews (SCs). In the second stage, the emergency resources rapidly reach the affected sites to participate in sequential restoration of the DN. Finally, the model is validated using standard IEEE test systems. The results from the experiments demonstrate that the proposed method reduces load shedding cost by 19.0% and 19.9% on 33-node and 123-node systems. Empirical simulations confirm that the introduced framework enables efficient emergency resource orchestration, thereby enhancing the pre-disaster preventive response capability and post-disaster real-time restoration capability of the DN, significantly mitigating the impact of disruptive events. Full article

Monitoring gas generated by internal faults in high-voltage switchgear is crucial for early warning and condition assessment; however, the diffusion process between gas generation and detection is currently widely overlooked, which severely compromises the accuracy of gas-based assessments. Implementing “virtual sensing” for high-voltage switchgear using digital twin technology to supplement data from physical sensors can effectively support digitalised and intelligent operation and maintenance. To this end, taking the characteristic gases of partial discharge as the subject of study, we first constructed a 1:1 scale digital twin model of internal gas diffusion within a 10 kV high-voltage switchgear. Calculations revealed significant differences in the diffusion processes of various gases: CO diffuses rapidly and mixes strongly, making it suitable for early warning; CO₂ and O₃ tend to accumulate in lower regions and stagnant zones, making them sensitive to faults in the lower sections; whilst NO reflects the channelling effect within the structure. Subsequently, a test platform comprising a 10 kV high-voltage switchgear unit and a gas detector was established for experimental validation. This revealed the trends in gas concentration changes, response sequences and peak behaviour at different fault locations, thereby verifying the validity of the proposed model. The maximum error in concentration balance was within 10 percent, providing a theoretical basis and engineering reference for the optimised placement of gas sensors in high-voltage switchgear and for fault tracing and localisation. Full article