Search Results (616)

All grid faults can cause significant problems within the power grid, including disconnection or malfunctions of wind energy conversion systems (WECSs) connected to the power grid. This study proposes a comparative analysis of the fault ride-through capability of a WECS-based permanent magnet synchronous generator (PMSG) system. To overcome these issues, active crowbar and capacitive bridge fault current limiter-based machine learning algorithm protection methods are implemented within the WECS system, both separately and in a hybrid. The regression approach is applied for the machine-side converter (MSC) and the grid side converter (GSC) controllers, which involve numerical data. The classification method is employed for protection system controllers, which work with data in distinct classes. These approaches are trained on historical data to predict the optimal control characteristics of the wind turbine system in real time, taking into account both fault and normal operating conditions. The neural network trilayered model has the lowest root mean squared error and mean squared error values, and it has the highest R-squared values. Therefore, the neural network trilayered model can accurately model the nonlinear relationships between its variables and demonstrates the best performance. The neural network trilayered model is selected for the MSC control system in this study. On the other hand, support vector machine regression is selected for the GSC controller due to its superior results. The simulation results demonstrate that the proposed machine learning algorithm performance for WECS based on a PMSG is robustly utilized under different operating conditions during all grid faults. Full article

As a cushion for the volatility of renewable energy, energy storage systems can achieve peak shaving and valley filling, thereby improving the operational efficiency and economic performance of the power grid. In addition, energy storage systems can absorb renewable energy production, thereby enhancing the safety and reliability of the electrical power system. Nowadays, energy storage systems are facing severe problems such as explosions that are caused by overcharging and discharging. The main reason for the overcharging and discharging of energy storage systems is the inconsistency in the state of the electric core in the charging and discharging process, which not only affects the safety of the electric core, but also influences the overall charging and discharging capacity of the energy storage system. To address this inconsistency of energy storage cores, this paper proposes an energy storage consistency monitoring method under the framework of clustering-classification, which adopts the Belief Peaks Evidential Clustering and Evidential K-Nearest Neighbors classification algorithm. This paper proposes a BPEC-EKNN-based method for battery inconsistency detection and localization. The proposed approach first constructs battery performance evaluation coefficients to characterize inter-cell behavioral differences, and then integrates an enhanced k-nearest neighbor strategy to identify abnormal cells. It also identifies and locates inconsistent battery cells by analyzing the magnitude of the confidence level m (Ω), without relying on predefined thresholds. Also, time-series data as opposed to the evaluation of voltage data at a singular point is engaged to realize the detection and localization of energy storage core consistency anomalies under the consideration of time-series data. The proposed algorithm is capable of identifying inconsistencies among energy storage batteries, with the parameter m (Ω) serving as an indicator of the likelihood of inconsistency. Experimental results on battery pack datasets demonstrate that the proposed method achieves higher detection accuracy and robustness compared with representative statistical threshold-based methods and machine learning approaches, and it can more accurately identify inconsistent battery cells. By applying perturbation analysis to real-time operational data, the algorithm proposed in this paper can detect inconsistencies in battery cells reliably. Full article

The integration of solar photovoltaic (PV) systems into smart grids necessitates robust, real-time fault detection mechanisms, particularly in resource-constrained environments like the Solar–Hydrogen AIoT microgrid framework at a university. This study conducts a comparative analysis of four prominent Convolutional Neural Network (CNN) architectures VGG16, ResNet-50, DenseNet121, and EfficientNetB0 to determine the optimal model for low-latency, edge-based fault diagnosis. The models were trained and validated on a dataset of solar panel images featuring multiple fault types. Quantitatively, DenseNet121 achieved the highest classification accuracy at 86.00%, demonstrating superior generalization and feature extraction capabilities. However, when considering the stringent requirements of an AIoT system, computational efficiency became the decisive factor. EfficientNetB0 emerged as the most suitable architecture, delivering an acceptable accuracy of 80.00% while featuring the smallest model size (5.3 M parameters) and a fast inference time (approx. 26 ms/step). This efficiency-to-accuracy balance makes EfficientNetB0 ideal for deployment on edge computing nodes where memory and real-time processing are critical limitations. DenseNet121 achieved 86% accuracy, while EfficientNetB0 achieved 80% accuracy with lowest model size and fastest inference time. This research provides a validated methodology for implementing efficient deep learning solutions in sustainable, intelligent energy management systems. The novelty of this work lies in its deployment-focused comparison of CNN architectures tailored for real-time inference on resource-constrained Solar–Hydrogen AIoT systems. Full article

Grid-forming power converters play a foundational role in modern microgrids and inverter-dominated distribution systems by establishing voltage and frequency references during islanded or low-inertia operation. However, when subjected to nonlinear or impulsive impact-type loads, these converters often suffer from severe harmonic distortion and transient current overshoot, leading to waveform degradation and protection-triggered failures. While virtual impedance control has been widely adopted to mitigate these issues, conventional implementations rely on fixed or rule-based tuning heuristics that lack adaptivity and robustness under dynamic, uncertain conditions. This paper proposes a novel reinforcement learning-based framework for real-time virtual impedance scheduling in grid-forming converters, enabling simultaneous optimization of harmonic suppression and impact load resilience. The core of the methodology is a Soft Actor-Critic (SAC) agent that continuously adjusts the converter’s virtual impedance tensor—comprising dynamically tunable resistive, inductive, and capacitive elements—based on real-time observations of voltage harmonics, current derivatives, and historical impedance states. A physics-informed simulation environment is constructed, including nonlinear load models with dominant low-order harmonics and stochastic impact events emulating asynchronous motor startups. The system dynamics are modeled through a high-order nonlinear framework with embedded constraints on impedance smoothness, stability margins, and THD compliance. Extensive training and evaluation demonstrate that the learned impedance policy effectively reduces output voltage total harmonic distortion from over 8% to below 3.5%, while simultaneously limiting current overshoot during impact events by more than 60% compared to baseline methods. The learned controller adapts continuously without requiring explicit load classification or mode switching, and achieves strong generalization across unseen operating conditions. Pareto analysis further reveals the multi-objective trade-offs learned by the agent between waveform quality and transient mitigation. Full article

The electric network frequency (ENF) serves as a vital criterion in geographical localization because its frequency fluctuations remain consistent within the same power grid. However, the performance of existing ENF-based audio geo-localization methods is limited when dealing with real-world scenarios, such as short audio durations and noisy environments. Moreover, the size of available ENF data is still small. To address these issues, we propose a novel audio inter-grid geo-localization method utilizing real-time online multimedia data. First, we construct the China-Online-Data dataset using online data, which integrates enhancement and harmonic selection to reduce noise and improve ENF estimation accuracy. Subsequently, we propose an ENF-based Dual-Channel Geo-Localization Network (DC-GLNet), which leverages both time and time-frequency domain information to improve feature extraction and classification performance. Experimental results demonstrate that the proposed method outperforms existing methods, particularly in short audio scenarios, achieving superior accuracy for inter-grid geo-localization. Full article

Human beings are affected by different types of skin diseases worldwide. Automatic identification of skin disease from Dermoscopy images has proved effective for diagnosis and treatment to reduce fatality rate. The objective of this work is to demonstrate efficiency of three deep learning pre-trained models, namely MobileNet, EfficientNetB0, and DenseNet121 with ensembling techniques for classification of skin lesion images. This study considers HAM1000 dataset which consists of n = 10,015 images of seven different classes, with a huge class imbalance. The study has two-fold contributions for the classification methodology of skin lesions. First, modification of three pre-trained deep learning models for grouping of skin lesion into seven types. Second, Weighted Grid Search algorithm is proposed to address the class imbalance problem for improving the accuracy of the base classifiers. The results showed that the weighted ensembling method achieved a 3.67% average improvement in Accuracy, Precision, and Recall, 3.33% average improvement for F1-Score, and 7% average improvement for Matthews Correlation Coefficient (MCC) when compared to base classifiers. Evaluation of the model’s efficiency and performance shows that it obtained the highest ROC-AUC score of 92.5% for the modified MobileNet model for skin lesion categorization in comparison to EfficientNetB0 and DenseNet121, respectively. The implications of the results show that deep learning methods and classification techniques are effective for diagnosis and treatment of skin lesion diseases to reduce fatality rate or detect early warnings. Full article

Accurate fault diagnosis of power transformers is critical for maintaining grid reliability, yet conventional dissolved gas analysis (DGA) methods face challenges in feature representation and high-dimensional data processing. This paper presents an intelligent diagnostic framework that synergistically integrates systematic feature engineering, tensor decomposition-based feature selection, and a sparrow search algorithm (SSA)-optimized multi-kernel support vector machine (MKSVM) for transformer fault classification. The proposed approach first expands the original five-dimensional gas concentration measurements to a twelve-dimensional feature space by incorporating domain-driven IEC 60599 ratio indicators and statistical aggregation descriptors, effectively capturing nonlinear interactions among gas components. Subsequently, a novel Tucker decomposition framework is developed to construct a three-way tensor encoding sample–feature–class relationships, where feature importance is quantified through both discriminative power and structural significance in low-rank representations, successfully reducing dimensionality from twelve to seven critical features while retaining 95% of discriminative information. The multi-kernel SVM architecture combines radial basis function, polynomial, and sigmoid kernels with optimized weights and hyperparameters configured through SSA’s hierarchical producer–scrounger search mechanism. Experimental validation on DGA samples across seven fault categories demonstrates that the proposed method achieves 98.33% classification accuracy, significantly outperforming existing methods, including kernel PCA-based approaches, deep learning models, and ensemble techniques. The framework establishes a reliable and accurate solution for transformer condition monitoring in power systems. Full article

Indoor landmarks play a crucial role in the process of indoor positioning and route planning for pedestrians or unmanned devices. Indoor structural landmarks, a type of indoor landmarks, can provide rich steering and semantic descriptions for indoor navigation services. However, most traditional indoor landmark extraction methods rely on indoor points of interest and indoor vector map data. These methods face the problem of difficult acquisition of indoor data and overlook the exploration of indoor structural landmarks. Therefore, this paper innovatively proposes a method for extracting indoor structural landmarks based on the commonly available indoor fire protection plan images. First, the HSV model is employed to eliminate noise from the original image, and vector data of indoor components is obtained using the constructed Canny operator. Subsequently, the visibility is calculated based on the grids of indoor space segmentation. Finally, the identification and extraction of indoor structural landmarks are achieved through grid visibility classification, directional clustering analysis, and spatial proximity verification. This approach opens up new ideas for indoor landmark extraction methods. The experimental results show that the method proposed in this paper can effectively extract indoor structural landmarks, the extraction accuracy of indoor structural landmarks reaches over 90%, verifying the feasibility of using indoor fire protection plan data for landmark extraction and expanding the data sources for indoor landmark extraction. Full article

Data-driven fault location methods based on deep learning offer strong feature learning and nonlinear mapping capabilities; however, in low-voltage distribution grids (LVDG) the scarcity of high-rate sampling devices and the variability introduced by distributed renewable generation lead to data insufficiency and data imbalance, which reduce the accuracy of deep-learning-based fault location. To address this, this paper proposes an adaptive working condition-based fault location method that integrates S-transform-enhanced feature extraction with progressive transfer learning. The method clusters working conditions using k-means on a 21-dimensional indicator set covering load, photovoltaic, and voltage. For each condition, a CNN is trained on the corresponding data, and the S-transform extracts distinctive time-frequency signatures from limited measurements to separate fault points at similar distances from the feeder head. Then, progressive transfer learning with Euclidean distance-based domain adaptation migrates effective parameters from data-rich conditions to data-scarce ones through fine-tuning and medium-tuning, thereby addressing the degradation of fault-location accuracy in scenarios with limited data. Experimental validation on a 400 V LVDG demonstrates superior performance, achieving 99.80% fault location accuracy and 99.72% fault type classification. The S-transform enhancement improves fault location by 6.63%, while transfer learning maintains 96% accuracy in edge conditions using only 200 samples. Full article

This paper presents a weakly supervised learning framework for real-world event identification in transmission networks using phasor measurement unit (PMU) data. The growing integration of renewable energy sources has introduced greater variability in grid conditions, intensifying the need for accurate event detection. Although high-resolution PMU measurements enable event identification to be formulated as a classification problem, traditional supervised learning approaches are hindered by the scarcity of labeled data, and acquiring large-scale, high-quality labeled PMU datasets remains prohibitively expensive. To overcome this challenge, we propose an automated PMU data-labeling method that combines domain knowledge with machine learning techniques through the use of labeling functions. A novel t-cherry junction tree-based estimation algorithm is introduced to enhance label accuracy, and a greedy strategy is employed to reduce computational complexity. These components are integrated into a weakly supervised framework capable of training robust event classifiers using limited labeled data and abundant unlabeled data. Extensive experiments on real-world PMU datasets demonstrate that our approach achieves competitive accuracy with significantly fewer labeled samples compared to conventional data-driven methods, highlighting its adaptability and resilience under real-world conditions. Full article

Early detection of performance degradation and prevention of critical failures in photovoltaic (PV) arrays are essential for ensuring system reliability and efficiency. This study presents an intelligent fault detection and classification framework based on a Multi-Layer Neural Network (MLNN). The model was developed and validated using a simulated 250 kW grid-connected PV system tested under five operating scenarios: normal operation, open-circuit fault, partial short-circuit, partial shading, and string-to-string fault. Unlike conventional diagnostic approaches, the proposed model directly processes raw electrical measurements (current, voltage, power, irradiance, and temperature) under varying environmental conditions, thus emulating real-world operational variability. The MLNN achieved 98% test accuracy and outperformed benchmark classifiers Support Vector Machine (SVM) and Random Forest (RF) across multiple metrics. Performance was evaluated using the confusion matrix, precision, recall (sensitivity) and F1-score. The framework is designed for scalability and can be integrated into predictive maintenance platforms to enable early fault detection and improve long-term PV system availability and efficiency. Full article

Mapping of rice-cropping regimes is crucial for effective irrigation planning and yield monitoring, particularly in data-scarce regions. We analyzed 48 months of 3 m PlanetScope NDVI data, aggregated to a 25 m hexagonal grid, and used Dynamic Time Warping Clustering to segment phenological patterns. Internal validation consistently identified two main clusters, indicating two dominant seasonality modes. Cluster 1 exhibited a higher mean NDVI, fewer low-canopy months, more vigorous growth periods, more peaks, and greater annual cycling, which suggests irrigated double cropping. Cluster 2 exhibited prolonged low NDVI values and a greater amplitude, consistent with single-rainfed systems. The rain–NDVI analysis supported these findings: Cluster 1 responded modestly to rainfall, whereas Cluster 2 exhibited a stronger and delayed response. Independent spatial checks confirmed these classifications. Off-season greenness, measured as NDVI above 0.50 from July to November, was concentrated near main and secondary canals and decreased with distance from intake points. This workflow combines DTW clustering with rainfall lag and off-season greenness analysis, effectively distinguishing between irrigated and rain-fed regimes using satellite time series. These findings are considered indicative rather than definitive, providing an assessment of cropping systems in Timor-Leste and demonstrating that DTW-based NDVI clustering offers a scalable approach in data-scarce regions. Full article

Problems such as unreasonable processability or model defects generated in the design stage will lead to continuous rework during the manufacturing process, which greatly increases the manufacturing cost of the product. Through manufacturability analysis, the process designer can find design defects that are difficult to manufacture, impossible to manufacture, or have high manufacturing costs as early as possible, so as to reduce the number of round trips between design and process, and shorten the product development cycle. However, it is difficult for the current rule-based manufacturability analysis method to obtain professional knowledge and construct a complete manufacturability analysis rule repository. Therefore, a manufacturability analysis method based on a graph neural network is proposed. First, the attribute adjacency graph and UV gridding are combined to characterize the part model data, which can effectively represent the topological information and geometric information on the part model. At the same time, parameter information on the spherical coordinate system is used to ensure rotation and translation invariance; then, based on the graph representation of the part model, a hierarchical graph neural network is constructed, which is divided into three levels, edge, node, and graph, for encoding, information transmission and updating, and expanding the receptive field for better node classification to support manufacturability analysis. Finally, graph contrastive learning is used as a regularization technique in the pre-training stage to maximize the similarity of graph representations between different views to improve prediction performance. Manufacturability analysis tests were carried out on the constructed part model dataset, and the experimental results showed that the method has good performance. Full article

Global climate change has profoundly affected agricultural ecosystems by altering the spatiotemporal patterns of temperature and precipitation, disrupting ecological equilibrium, and increasing environmental variability for crop growth, thereby posing significant challenges to food security. Based on 1 km-resolution gridded datasets of mean precipitation and temperature for Liaoning Province from 1950 to 2023, this study integrated the Miami and Thornthwaite Memorial models with climate tendency rate analysis, Mann–Kendall trend tests, and inverse distance weighting interpolation to assess spatiotemporal changes in climate potential productivity (CPP) and its relationship with grain yield dynamics. The results show that, from 1950 to 2023, annual precipitation exhibited a fluctuating downward trend (−8.5 mm/10a), while mean annual temperature increased significantly (0.3 °C/10a). Consequently, precipitation-based climatic production potential declined at a rate of 10.4 g·m⁻²·(10a)⁻¹, whereas temperature-based, evapotranspiration-based, and standard climate potential productivity (Yb) increased at rates of 23.3-, 6.6-, and 5.7 g·m⁻²·(10a)⁻¹, respectively. Spatially, CPP displayed a distinct gradient characterized by higher values in the southeast and lower values in the northwest, with a stronger correlation to precipitation than to temperature. Climate classification analysis indicated that warm-humid conditions enhanced CPP, whereas cold-dry, cold-humid, and warm-dry conditions reduced productivity. Although grain yield per unit area and climate resource utilization efficiency increased by 89.4 g·m⁻²·(10a)⁻¹ and 9.0% per decade, respectively, the yield-increasing potential declined by 84.1 g·m⁻²·(10a)⁻¹, indicating that while advances in agricultural technology have improved resource conversion efficiency, the potential for further yield gains through climate-dependent strategies alone is increasingly limited. Full article

High-voltage (HV) cable systems—particularly those insulated with cross-linked polyethylene (XLPE)—are increasingly deployed in both AC and DC applications due to their excellent electrical and mechanical performance. However, their long-term reliability is challenged by partial discharges (PD), insulation aging, space charge accumulation, and thermal and electrical stresses. This review provides a comprehensive survey of the state-of-the-art technologies and methodologies across several domains critical to the assessment and enhancement of cable reliability. It covers advanced condition monitoring (CM) techniques, including sensor-based PD detection, signal acquisition, and denoising methods. Aging mechanisms under various stressors and lifetime estimation approaches are analyzed, along with fault detection and localization strategies using time-domain, frequency-domain, and hybrid methods. Physics-based and data-driven models for PD behavior and space charge dynamics are discussed, particularly under DC conditions. The article also reviews the application of numerical tools such as FEM for thermal and field stress analysis. A dedicated focus is given to machine learning (ML) and deep learning (DL) models for fault classification and predictive maintenance. Furthermore, standards, testing protocols, and practical issues in sensor deployment and calibration are summarized. The review concludes by evaluating intelligent maintenance approaches—including condition-based and predictive strategies—framed within real-world asset management contexts. The paper aims to bridge theoretical developments with field-level implementation challenges, offering a roadmap for future research and practical deployment in resilient and smart power grids. This review highlights a clear gap in fully integrated AC/DC diagnostic and aging analyses for XLPE cables. We emphasize the need for unified physics-based and ML-driven frameworks to address HVDC space-charge effects and multi-stress degradation. These insights provide concise guidance for advancing reliable and scalable cable assessment. Full article