Search Results (368)

Timely and accurate fault diagnosis in power transmission systems is critical to ensuring grid stability, operational safety, and minimal service disruption. This study presents a unified deep learning framework that simultaneously performs fault identification, fault type classification, and fault location estimation using a multi-task learning (MTL) approach. Using the IEEE 39–Bus network, a comprehensive data set was generated under various load conditions, fault types, resistances, and location scenarios to reflect real-world variability. The proposed model integrates a shared representation layer and task-specific output heads, enhanced with an attention mechanism to dynamically prioritize salient input features. To further optimize the model architecture, Optuna was employed for hyperparameter tuning, enabling systematic exploration of design parameters such as neuron counts, dropout rates, activation functions, and learning rates. Experimental results demonstrate that the proposed Optimized Multi-Task Learning Attention Network (MTL-AttentionNet) achieves high accuracy across all three tasks, outperforming traditional models such as Support Vector Machine (SVM) and Multi-Layer Perceptron (MLP), which require separate training for each task. The attention mechanism contributes to both interpretability and robustness, while the MTL design reduces computational redundancy. Overall, the proposed framework provides a unified and efficient solution for real-time fault diagnosis on the IEEE 39–bus transmission system, with promising implications for intelligent substation automation and smart grid resilience. Full article

In the construction of ultra-high voltage (UHV) transformation substations, mass concrete is highly susceptible to temperature-induced cracking due to thermal gradients arising from the disparity between internal hydration heat and external environmental conditions. Such cracks can severely compromise the structural integrity and load-bearing capacity of foundations, making accurate temperature prediction and effective thermal control critical challenges in engineering practice. To address these challenges and enable real-time monitoring and dynamic regulation of temperature evolution, this study proposes a novel hybrid forecasting model named CPO-VMD-SSA-Transformer-GRU for predicting temperature behavior in mass concrete. First, sine wave simulations with varying sample sizes were conducted using three models: Transformer-GRU, VMD-Transformer-GRU, and CPO-VMD-SSA-Transformer-GRU. The results demonstrate that the proposed CPO-VMD-SSA-Transformer-GRU model achieves superior predictive accuracy and exhibits faster convergence toward theoretical values. Subsequently, four performance metrics were evaluated: Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Square Error (RMSE), and Coefficient of Determination (R²). The model was then applied to predict temperature variations in mass concrete under laboratory conditions. For the univariate time series at Checkpoint 1, the evaluation metrics were MAE: 0.033736, MSE: 0.0018812, RMSE: 0.036127, and R²: 0.98832; at Checkpoint 2, the values were MAE: 0.016725, MSE: 0.00091304, RMSE: 0.019114, and R²: 0.96773. In addition, the proposed model was used to predict the temperature in the rising stage, indicating high reliability in capturing nonlinear and high-dimensional thermal dynamics in the whole construction process. Furthermore, the model was extended to multivariate time series to enhance its practical applicability in real-world concrete construction. At Checkpoint 1, the corresponding metrics were MAE: 0.56293, MSE: 0.34035, RMSE: 0.58339, and R²: 0.95414; at Checkpoint 2, they were MAE: 0.85052, MSE: 0.78779, RMSE: 0.88757, and R²: 0.91385. These results indicate significantly improved predictive performance compared to the univariate configuration, thereby further validating the accuracy, stability, and robustness of the multivariate CPO-VMD-SSA-Transformer-GRU framework. The model effectively captures complex temperature fluctuation patterns under dynamic environmental and operational conditions, enabling precise, reliable, and adaptive temperature forecasting. This comprehensive analysis establishes a robust methodological foundation for advanced temperature prediction and optimized thermal management strategies in real-world civil engineering applications. Full article

Substations’ auxiliary systems support the station’s operational loads and are crucial for grid security, often requiring backup power to ensure uninterrupted operation. A new alternative for this backup power supply is a microgrid composed of photovoltaic (PV) generation and storage. This paper proposes an electric–hydrogen microgrid as backup power supply for substation auxiliary systems. This microgrid ensures power supply during emergencies, provides clean and stable energy for daily operations, and enhances environmental friendliness and profitability. Firstly, using a 220 kV substation as an example, the construction principles of the proposed backup power microgrid are introduced. Secondly, operation strategies under different scenarios are proposed, considering time-sharing tariffs and different weather conditions. Following this, the capacity configuration optimization model of the electric–hydrogen microgrid is proposed, incorporating critical thresholds for energy reserves to ensure system robustness under fault conditions. Finally, the Particle Swarm Optimization (PSO) algorithm is used to solve the problem, and a sensitivity analysis is performed on hydrogen market pricing to evaluate its impact on the system’s economic feasibility. The results indicate that the proposed electric–hydrogen microgrid is more economical and provides better fault power supply time than battery-only power supply. With the development of hydrogen energy storage technology, the economy of the proposed microgrid is expected to improve further in the future. Full article

This study presents a comprehensive techno-economic assessment of offshore wind projects in the Colombian Caribbean, emphasizing the impact of site-specific parameters on development costs and performance. Wind resource conditions were evaluated in four coastal regions (La Guajira, Magdalena, Atlántico, and Bolívar) using hourly meteorological data from 2015 to 2024, adjusted to 100 m above ground level through logarithmic and power law wind profile models. The analysis included wind speed, bathymetry, distance to shore, distance to substation, foundation type, wind power density (WPD), and capacity factor (C_f). Based on these parameters, annual energy generation was estimated, and both capital expenditures (CAPEX) and operational expenditures (OPEX) were calculated, considering the technical and cost differences between fixed and floating foundations. Results show that La Guajira combines excellent wind conditions (WPD of 796 W/m² and C_f of 61.5%) with favorable construction feasibility (bathymetry of −32 m), resulting in the lowest CAPEX among the studied regions. In contrast, Magdalena and Atlántico, with bathymetries exceeding 200 m, require floating foundations that more than double the investment costs. Bolívar presents an intermediate profile, offering solid wind potential and fixed foundation feasibility at a moderate cost. The findings confirm that offshore wind project viability depends not only on wind resource quality but also on physical site constraints, which directly influence the cost structure and energy yield. This integrated approach supports more accurate project prioritization and contributes to strategic planning for the sustainable deployment of offshore wind energy in Colombia. Full article

The grounding grid of a substation is a crucial component for ensuring normal operation. However, since it is buried underground for long periods, it is highly susceptible to electrochemical corrosion. This corrosion leads to a reduction in its grounding performance, and severe corrosion may endanger the reliable operation of high-voltage equipment and secondary relay-protection equipment, as well as the safety of personnel. In this paper, the electromagnetic field analysis method is used to conduct simulation modeling of the grounding grid. A different-frequency current is injected into the grounding grid to study the variation law of the surface magnetic field distribution when corrosion occurs to different degrees at different positions in the grounding grid. Through the analysis of the evolutionary characteristics of the magnetic field distribution, the corrosion-induced breakages in the grounding grid are located and a comprehensive state evaluation is carried out. The results show that when a fault occurs in a conductor at the same position, the variation amplitude of the surface magnetic field gradually increases with increased corrosion. Based on this finding, an online monitoring algorithm for the location of corrosion-induced breakages and state evaluation of the grounding grid is proposed. A comprehensive evaluation model is constructed by combining the grounding resistance value and corrosion characteristic value to accurately locate the fault. Full article

Offshore wind energy is rapidly expanding as a critical resource for global carbon neutrality, with 10.8 GW of new capacity added in 2023, raising the worldwide total to 75.2 GW. However, large-scale integration of offshore wind farms introduces power quality challenges due to the characteristics of inverter-based resources, particularly harmonic distortion, which can threaten system stability. This study quantitatively investigates the influence of short circuit ratio (SCR) on voltage and current harmonic distortion during offshore wind farm integration. A 500 MW offshore wind farm was modeled, and MATLAB/Simulink simulations were performed for 345 kV and 154 kV systems to evaluate the impact of varying SCR on total harmonic distortion (THD) and individual harmonic orders. Furthermore, the harmonic assessment based on the IEC 61400-21-2 summation method was compared with the simulation results, demonstrating the limitations of the simple summation approach and underscoring the importance of simulation-based evaluation. The results reveal that, under certain SCR conditions, parallel resonance caused by system impedance and line parameters produces unexpectedly high distortion in the 345 kV system, contrary to expectations based solely on voltage level. This resonance phenomenon and SCR dependency were also validated using short circuit capacity data from actual offshore wind farm candidate sites. Overall, the study emphasizes the necessity of comprehensive power quality assessments that account for SCR conditions, voltage levels, and harmonic emission characteristics, providing practical guidance for site selection, substation design, and harmonic mitigation in offshore wind integration. Full article

The safety of offshore structures is a key topic in developing offshore oil and gas and offshore wind energy. Due to the harsh offshore environment and costly offshore field tests, offshore field trials to validate the theoretical models for offshore structures are limited, and testing results can rarely be found in the public domain. The Bayesian updating technique combines existing engineering knowledge with the observed performance data about in-service offshore structures to update model uncertainties. Hence, the Bayesian technique overcomes the shortcomings of limited offshore field trials. This paper compiles performance data on offshore jackets in hurricanes in the Gulf of Mexico (GoM) in the past two decades and calibrates the model uncertainties of the API method using the Bayesian technique. With the updated model uncertainty, this paper evaluates the reliability of generic offshore jackets in the GoM and the case study’s offshore wind substation in China. With a typical reserve strength ratio (RSR) of about 2.0 to 2.2, the reliability analysis reveals that the updated annual failure probability of a generic offshore jacket in the GoM is largely less than $1.0\times {10}^{-3}$, indicating that the extreme weather overload is not a major concern. However, the RSR of the case study platform in China is greater than 4.5, and the annual failure probability for the case study offshore wind substation is about 2–3 orders of magnitude lower than typical oil and gas jackets. Hence, from the extreme metocean condition perspective, the substation under investigation has sufficient structural capacity, and the design practice for offshore wind substations in northern China may be improved. Full article

Against the backdrop of the global energy crisis and the “dual carbon” goals (carbon peaking and carbon neutrality), the passive energy-saving design of substation buildings in cold regions faces severe challenges. This study systematically conducts a decomposed analysis of the shape coefficient, thermal performance of the building envelope (including external walls, internal walls, roofs, and external windows), and window-to-wall ratio of substation buildings in cold regions, quantifies the degree of influence of each factor, and proposes corresponding energy-saving design strategies. This study took a 110 kV substation in Yuhua District, Shijiazhuang City, Hebei Province, as the research object. A building energy consumption model was established based on DeST (2023) software, and the influence of the building shape coefficient, U-values of the envelope structure (external walls, internal walls, roofs, external windows), and window-to-wall ratio on the building’s cooling and heating loads was analyzed using the numerical simulation and control variable methods. Leveraging a rigorously validated, high-resolution simulation framework, we quantitatively dissect the marginal energy penalties and payoffs of every passive design variable governing fully indoor substations in cold-climate zones. The resultant multidimensional response surfaces are distilled into a deterministic, climate-specific passive energy-saving protocol that secures heating-energy savings of up to 43% without compromising electrical safety or operational accessibility. (1) Reducing the shape coefficient can significantly lower the heat load, and it is recommended to control it at 0.35–0.40; (2) The thermal performance of the envelope structure has a differential effect: the energy-saving effect is optimal when the U-value of external walls is 0.20–0.30 W/(m²·K) and the U-value of roofs is ≤0.25 W/(m²·K). A U-value of 2.4 W/(m²·K) is recommended for external windows, while the internal wall exerts a weak influence; (3) The window-to-wall ratio should be controlled by orientation: east-facing/north-facing ≤ 0.20, south-facing ≤ 0.35, and west-facing ≤ 0.30. Based on the above results, a comprehensive energy-saving strategy of “compact form–high-efficiency envelope–limited window-to-wall ratio” is proposed, which provides theoretical support and technical pathways for the energy-saving design of substation buildings in cold areas. Compared with existing substation buildings, the recommended parameters yield a significant reduction in total life-cycle carbon emissions and hold important practical significance for realizing the “dual carbon” goals (carbon peaking and carbon neutrality) of the power system. Full article

The paper presents a hybrid intelligent expert diagnostic system (HIESD) of power transformer (PT) subsystems realized on the basis of integration of measuring and computing hardware and software complexes into a single functional architecture. HIESD performs online diagnostics of four main subsystems of PT: 1—insulating (liquid and solid insulation); 2—electromagnetic (windings, magnetic conductor); 3—voltage regulation; and 4—high-voltage inputs. Computational complexes and modules of the system are connected with the real object of power grids, 110/10 kV substation, which interact with each other and contain a relational database of retrospective offline data of the PT “life cycle” (including test and measurement results), supplemented by online monitoring data of the main subsystems, corrected by high-precision test measurements; analytical complex, in which the work of calculation modules of the operational state of PT subsystems is supplemented by predictive analytics and machine learning modules; and a knowledge base, sections of which are regularly updated and supplemented. The system architecture is tested at industrial facilities in terms of online transformer diagnostics based on dissolved gas analysis (DGA) data. Additionally, a theoretical model of diagnostics based on the electromagnetic characteristics of the transformer, which takes into account distorted and nonlinear modes of its operation, is presented. The scientific significance of the work consists of the presentation of the following new provisions: Methodology and algorithm for diagnostics of electromagnetic parameters of ST, taking into account nonlinearity and non-sinusoidality of winding currents and voltages; formation of optimal client–service architecture of training models of hybrid system based on the processes of data storage and management; and modification of the moth–flame algorithm to optimize the smoothing coefficient in the process of training a probabilistic neural network Full article

Partial Discharge (PD) measurement is one of the effective methods for assessing the internal insulation condition of power transformers in factories and substations. The pulse current signals generated by PD within transformer windings are significantly influenced by the winding structure during their propagation from the discharge source to the external measurement system. This influence may lead to misinterpretation of the insulation status, particularly in the analysis of PD measurement results. Such effects are closely related to the signal transmission path and distance and exhibit a strong correlation with the winding transfer function, manifesting as attenuation, distortion, or delay of the measured signals compared to the original PD waveforms. Therefore, it is essential to investigate the impact of the discharge path on the propagation characteristics of transformer windings and its effect on PD waveforms. This paper establishes a simplified distributed parameter model of a 180-turn single-winding multi-conductor transmission line using the finite element method and mathematical modeling, deriving the transfer functions between the winding head or winding end and various internal discharge positions. By injecting different types of PD waveforms collected in the laboratory at various discharge locations within the winding, the alterations of PD signals propagated to the winding head and winding end are simulated, and clustering analysis is performed on the propagated PD signals of different types. Full article

Accurate and real-time monitoring of low-frequency electromagnetic field (EMF) is essential in power and industrial environments, yet most conventional approaches still suffer from limited spatial coverage, manual operation, and insufficient digitization. To address these challenges, this paper proposes an intelligent EMF monitoring system that integrates six-axis magnetic field sensing, temperature compensation, vector synthesis, Sub-1 GHz wireless communication, and real-time data visualization. The system supports simultaneous measurement of both AC and DC magnetic fields across the 30 Hz–100 kHz range, with specific optimization for power-frequency conditions (50/60 Hz). Designed with modular integration and low power consumption, it is suitable for portable deployment in field scenarios. Comprehensive laboratory and substation tests demonstrate high accuracy, with maximum measurement errors of 1.17% under zero-field and 1.42% under applied-field conditions—well below the ±5% tolerance defined by international standards. Wireless performance tests further confirm stable long-distance communication, achieving ranges of up to 5 km without significant transmission errors, while overall system measurement error reached as low as 0.015%. These results verify the system’s robustness, fidelity, and compliance with international safety standards. Overall, the proposed platform provides a practical and scalable solution for intelligent EMF monitoring, offering strong potential for deployment in industrial environments and infrastructure-critical applications. Full article

Line traps are critical components of power line carrier systems, enabling remote control signaling, voice communication, and inter-substation control within electrical transmission and distribution networks. Despite their importance, limited research has addressed their seismic performance, particularly under near-fault and far-fault ground motions. This study addresses this gap by experimentally and numerically evaluating a full-scale 170 kV line trap. Ambient Vibration Tests (AVTs), using Enhanced Frequency Domain Decomposition (EFDD), and shake table testing established its modal and seismic response characteristics. A finite element (FE) model was then developed and calibrated using the experimental results. Dynamic analyses were conducted to evaluate the structural response under both near-fault and far-fault ground motions. Experimental findings revealed that the seismic response of the line trap increased with height, with the upper segment experiencing over four times the base acceleration. Numerical analyses further demonstrated that near-fault ground motions induced significantly higher displacement and acceleration responses than far-fault records. These findings collectively constitute a detailed investigation into the seismic performance of a full-scale line trap, emphasizing the pivotal role of ground motion characteristics in the structural evaluation of substation apparatus. Full article

This study investigates the bidirectional bending performance of double- and triple-spliced steel-ribbed composite slabs for substation applications. Full-scale experiments and numerical parametric analyses were conducted to evaluate ultimate load, ductility, stiffness, failure modes, and load-transfer mechanisms. Results indicate that double-spliced slabs exhibit better performance than triple-spliced slabs, showing a 24.5% higher ultimate load and 65.3% greater ductility, with well-developed orthogonal cracks and yielding of both longitudinal prestressing steel and transverse reinforcement. Triple-spliced slabs display partial bidirectional behavior due to reduced transverse integrity, with stresses in edge slabs concentrated at the corners. Compared with monolithic slabs, spliced slabs show nearly identical stiffness at cracking onset but progressively reduced stiffness, load capacity, and ductility in the mid-to-late loading stages. Joint-crossing reinforcement is critical for transverse load transfer, and increasing its diameter is more effective than increasing its strength in preventing premature joint-controlled failure. These findings provide significant theoretical guidance and technical support for the prefabricated construction of high-voltage substation floor systems. Full article

To address the issue of missed detection of abnormal images caused by scarcity of defect samples and inadequate model training that characterize the current substation image inspection methods, this paper proposes a new substation image inspection method based on visual communication and combination of normal and abnormal samples. In this new method, the quality of substation equipment images is first evaluated, and images are recaptured when they are defocused and underexposed. Images are then preprocessed to eliminate the impact of noise on the algorithm. Image feature alignment is then performed to mitigate camera displacement errors that could degrade algorithmic accuracy. Subsequently, normal-labeled images are used to train the model, and a normal sample database is thus established. Built upon visual communication infrastructure with low-level quantization, the visual feature discrepancy between the current inspection images and those in the normal sample database is calculated using the Learned Perceptual Image Patch Similarity (LPIPS) metric. Through this process, the normal images are filtered out while abnormal images are classified and reported. Finally, this new method is validated at a municipal power supply company in China. When the abnormal image reporting rate is 18.9%, the abnormal image reporting accuracy rate is 100%. This demonstrates that the proposed method can significantly decrease the workload of substation operation and maintenance personnel in reviewing substation inspection images, reduce the time required for a single inspection of substation equipment, and improve the efficiency of video-based substation inspections. Full article

To address the challenges of complex harmonic characteristics, multi-source coupling, and strong time variability in aggregated loads downstream of high-voltage substations, this paper proposes an Adaptive Multi-Task Gaussian Process Regression (AMT-GPR) method for harmonic modeling. First, field measurements from the medium-voltage side of a 500 kV substation are denoised and analyzed using Fourier transform to reveal the dynamic patterns and interdependencies of harmonic current magnitudes. Then, a multi-task GPR framework is constructed, incorporating task correlation modeling and adaptive kernel functions to capture inter-task coupling and differences in feature scales. Finally, a probabilistic harmonic model is developed based on multiple sets of measured data, and the modeling performance of AMT-GPR is compared with single-task GPR, conventional MT-GPR, and mainstream machine learning approaches including RBF, LS-SVM, and LSTM. Simulation results demonstrate that traditional harmonic modeling methods are insufficient to capture the dynamic behavior and uncertainty of aggregated loads and AMT-GPR maintains strong robustness under small-sample conditions, significantly reduces prediction errors, and yields narrower uncertainty intervals, outperforming the baseline models. These findings validate the effectiveness of the proposed method in modeling harmonics of aggregated loads in high-voltage substations and provide theoretical support for subsequent harmonic assessment and mitigation strategies. Full article