Search Results (160)

Historical masonry minarets are highly vulnerable to seismic actions due to their slender geometry, limited tensile capacity, and material heterogeneity. However, their response to near-fault ground motions characterized by velocity pulses remains insufficiently explored. This study investigates the seismic response of the historical Tavanlı Mosque Minaret (1894, Trabzon, Türkiye) subjected to pulse-like (PL) and non-pulse-like (NPL) near-fault ground motions. A three-dimensional finite element model (FEM) was developed in ANSYS Workbench and systematically calibrated using empirical formulations to represent the current dynamic condition of the structure. Seismic performance was evaluated through linear dynamic analyses in terms of displacement demands, principal stress distribution, and drift-ratio-based performance levels. The results indicate that model calibration significantly modifies the dynamic characteristics, increasing the fundamental frequency from 0.734 Hz to 1.126 Hz and reducing displacement demands by approximately 35–76% across the considered records. Despite this improvement, PL ground motions consistently generate more critical deformation demands than NPL motions, frequently exceeding Collapse Prevention (CP) limits even when Peak Ground Acceleration (PGA) values are relatively low. A key finding is that seismic demand cannot be reliably predicted by peak intensity measures or pulse-period ratios ($T_p/T_1$) alone; rather, velocity-related parameters and pulse coherence govern the structural response. These results demonstrate that integrating empirical model calibration with pulse-sensitive seismic analysis is essential for reliable seismic assessment and conservation planning of slender historical masonry structures located in near-fault regions. The study offers a systematic framework that integrates model calibration and pulse-sensitive seismic analysis for evaluating the drift-controlled response of slender historical masonry minarets in near-fault regions. Full article

Accurate prediction of distribution network operating states is essential for implementing proactive fault warning systems. However, with the high penetration of distributed energy resources, measurement data exhibit strong nonlinearity and multi-scale temporal characteristics, posing significant challenges to existing prediction methods. Current mainstream approaches face a critical dilemma: traditional recurrent neural network (RNN) models (e.g., LSTM) suffer from vanishing gradients and memory bottlenecks in long-sequence forecasting, making it difficult to capture long-term evolutionary trends. In contrast, while standard Transformer models excel at global modeling, their smoothing effect renders them insensitive to subtle transient abrupt changes such as voltage sags, and they incur high computational complexity. To address the dual challenges of “difficulty in capturing transient abrupt changes” and “inability to simultaneously handle long-term trends,” this paper proposes a fault precursor trend prediction model that integrates Extended Long Short-Term Memory (XLSTM) with Informer, termed XLSTM-Informer. To tackle the challenge of extracting transient features, an XLSTM-based local encoder is constructed. By replacing the conventional Sigmoid activation with an improved exponential gating mechanism, the model achieves significantly enhanced sensitivity to instantaneous fluctuations in voltage and current. Additionally, a matrix memory structure is introduced to effectively mitigate information forgetting issues during long-sequence training. To overcome the challenge of modeling long-term dependencies, Informer is employed as the global decoder. Leveraging its ProbSparse sparse self-attention mechanism, the model substantially reduces computational complexity while accurately capturing long-range temporal dependencies. Experimental results on a real-world distribution network dataset demonstrate that the proposed model achieves substantially lower Mean Squared Error (MSE) and Mean Absolute Percentage Error (MAPE) compared to standalone CNN, LSTM, and other baseline models, as well as conventional LSTM–Informer hybrid approaches. Particularly under extreme operating conditions—such as sustained high summer loads and winter heating peak loads—the model successfully overcomes the trade-off limitations of traditional methods, enabling simultaneous and accurate prediction of both local precursors and global trends. This provides a reliable technical foundation for proactive warning systems in distribution networks. Full article

Single-phase ground faults account for more than 80% of total faults in distribution networks. However, the introduction of distributed generation complicates power grid topology, leading to strong nonlinearity and non-stationarity in the zero-sequence current. This limits the accuracy of traditional faulted line selection methods. To address this problem, a CNN–ResNet-based method for faulted line selection for single-phase ground faults in distribution networks is proposed. Firstly, a 10 kV arc ground fault simulation test platform is built to analyze the nonlinear distortion characteristics of fault current. The WOA–VMD algorithm, optimized by permutation entropy, is used to denoise the zero-sequence current signal. The Gram Angular Difference Field (GADF) is then adopted to convert the one-dimensional signal into a two-dimensional image that retains its temporal characteristics. A hybrid deep learning model is constructed by fusing the one-dimensional time-domain features extracted by CNN and the two-dimensional time-frequency image features extracted by ResNet34. Matlab/Simulink simulations and physical experimental verification demonstrate that the proposed method achieves a training accuracy of over 97%, with zero misjudgments recorded in 15 arc grounding fault tests, representing a significant improvement in accuracy compared with existing diagnostic algorithms. It can adapt to complex scenarios such as high-resistance grounding and changes in neutral point grounding mode, effectively improving the accuracy and robustness of faulted line selection and providing technical support for the safe operation of distribution networks. Full article

Stator short-circuit faults (SSCFs) account for a significant portion of induction motor failures, yet their early detection remains a challenge in industrial environments where labeled fault data is scarce and installing additional sensors is often impractical. This paper proposes a novel, data-driven fault detection and diagnosis framework grounded in the Residual Information Value (RIV) principle to overcome reliability limitations of traditional spectral and residual energy methods. By redefining fault detection as a statistical test of independence between control inputs (voltages) and current residuals, the proposed method identifies incipient faults as model drifts without relying on prior knowledge of fault distributions. A key contribution of this work is the seamless integration of the diagnostic scheme into standard Variable Speed Drives (VSDs): the healthy nominal model (a Multilayer Perceptron) is trained exclusively using data from the drive’s existing self-commissioning routine, eliminating the need for manual data collection or complex physical parameter identification. Experimental validation on an industrial test bench demonstrates that the framework achieves superior diagnostic performance compared to traditional baselines, providing higher statistical separability and a reduced false alarm rate. The system can detect 1% incipient faults in approximately 61 ms while accurately identifying the faulty phase. The results confirm that the proposed RIV-based strategy offers a robust, non-intrusive, and industry-ready solution for predictive maintenance that effectively balances high-speed detection with enhanced statistical reliability. Full article

Accurate identification of user–transformer relationships is fundamental to refined management, load forecasting, and fault diagnosis in low-voltage distribution networks. Traditional approaches often rely on costly manual inspection or complex physical modeling, which limits their scalability. This paper proposes a frequency-domain representation learning and weakly supervised optimization method for automatic transformer-area identification from large-scale user electricity data with incomplete labels. Specifically, the proposed method first applies the Fast Fourier Transform (FFT) to convert users’ voltage and current time series into robust frequency-domain feature vectors, effectively revealing intrinsic periodic structures while reducing noise interference. Then, under limited supervision, a deep metric learning framework is employed to optimize the embedding space such that users belonging to the same transformer area are clustered more compactly, while those from different areas are separated farther apart. Finally, a high-density clustering algorithm is applied in the optimized embedding space to complete the transformer-area partition for all users. Experimental results demonstrate that the proposed approach can effectively leverage limited label information and significantly improve transformer-area identification accuracy, providing an efficient and low-cost solution for digitalized operation and maintenance of low-voltage distribution networks. Full article

To address the practical engineering challenges of limited fault samples for high-voltage circuit breaker spring operating mechanisms and the inability of single features to fully reflect equipment status, this paper proposes a small-sample fault diagnosis method based on multi-source feature fusion and Stacking ensemble learning. First, a multi-source sensing system containing MEMS (Micro-Electro-Mechanical System) pressure and travel, coil, and motor current was constructed to achieve comprehensive monitoring of the mechanical and electrical states of a 220 kV circuit breaker; in particular, the introduction of non-invasive MEMS sensors effectively solves the difficulty of capturing static spring fatigue characteristics inherent in traditional methods. Second, a high-dimensional feature space was constructed using Savitzky–Golay filtering and physical feature extraction techniques. To address the characteristics of small-sample data distribution, a two-layer Stacking ensemble learning model based on 5-fold cross-validation was designed. This model utilizes the SVM (Support Vector Machine), RF (Random Forest), and KNN (K-Nearest Neighbors) as base classifiers and Logistic Regression as the meta-learner, achieving an adaptive fusion of the advantages of heterogeneous algorithms. True-type experimental results show that the average diagnostic accuracy of this method under normal conditions and four typical fault conditions reaches 96.1%, which is superior to single base models (the RF was 94.2%). Feature importance analysis further confirms that closing and opening pressures are the most critical features for distinguishing mechanical faults. This study provides effective theoretical basis and technical support for condition-based maintenance of high-voltage circuit breakers under small-sample conditions. Full article

The independent application of conventional electrical or thermal models is, generally, not adequate to model the interdependence between temperature distribution, heat transfer mechanisms, and the electrical performance of Photovoltaic (PV) generators. In this context, coupled thermal–electrical modeling approaches have recently gained increasing importance to accurately simulate the PV performance. This work presents a comprehensive and systematic analysis of electrical, thermal, and coupled thermal–electrical models developed for PV modules. Electrical models are classified into analytical/physical, semi-empirical, and empirical classes, highlighting their assumptions, parameter requirements, computational complexity, and applicability at cell, module, and system levels. Thermal modeling approaches are reviewed by distinguishing lumped parameter and thermal network models from spatially distributed numerical methods. Particular emphasis is placed on the ability of these models to represent non-uniform temperature distributions and transient operating conditions. Furthermore, this review critically examines state-of-the-art coupled thermo-electrical models, focusing on different coupling strategies, feedback mechanisms, and levels of spatial resolution. The advantages and limitations of each modeling approach are discussed in relation to accuracy, computational cost, and suitability for performance prediction, fault analysis, and reliability assessment. Finally, current research gaps and future directions are identified, providing a structured framework to guide the selection of the most appropriate model and the development of more accurate and physically consistent PV modeling strategies under complex and realistic operating conditions. Full article

A demountable connection is necessary to enable quick and easy replacement of high-temperature superconducting (HTS) tape samples during cryogenic (77 K) testing, particularly when investigating their application in superconducting fault current limiters (SFCLs). Testing HTS tapes for application in SFCLs involves inducing their transition from the superconducting state to the resistive state, which can result in sample damage. The contact resistance of the HTS tape to the current lead depends on the area and on the uniform pressure. Stress distribution in screwed connections with two, four and six screws was analysed using a solid model to compare them and achieve the uniform contact essential for minimising contact resistance in cryogenic conditions. The analysis indicated a solution that provides the most uniform pressure distribution across the HTS tape surface. This solution was utilised in subsequent calculations of thermal shrinkage, and for the determination of the optimal disc spring stack configuration. It is imperative that the compensating disc springs maintain the requisite pressure of the copper block on the tape across the entire operational temperature range (room to cryogenic). Furthermore, the disc springs must provide adequate stroke to compensate for the thermal shrinkage of a copper block and an aluminium clamp. Full article

In modern power distribution networks, robust and intelligent fault management techniques are increasingly important as system complexity grows with the integration of distributed energy resources. This article reviews the use of deep learning methods for short-circuit fault detection, classification, and localization in power distribution systems, including symmetrical, asymmetrical, and high-impedance faults. The approaches examined include convolutional neural networks, recurrent neural networks, deep reinforcement learning, graph neural networks, and hybrid architectures. A comprehensive taxonomy of these models is presented, followed by an analysis of their application across the stages of fault diagnostics. Major contributions to the field are highlighted, and research gaps are identified in relation to data scarcity, model interpretability, real-time responsiveness, and deployment scalability. The paper provides an in-depth technical and performance comparison of deep learning approaches based on current research trends, and it also outlines the limitations of previous review studies. The objective of this work is to support researchers in selecting and implementing appropriate deep learning techniques for fault analytics in complex smart electricity grids with significant penetration of distributed energy resources. The review is intended to serve as an initial foundation for continued research and development in intelligent fault analytics for reliable and sustainable power distribution systems. Full article

The large-scale integration of Distributed Generators (DGs) has significantly altered fault characteristics in distribution networks, posing challenges to conventional fault location methods. To address these limitations, this paper presents a novel approach that combines dynamic cluster partitioning with the arithmetic optimization algorithm (AOA). The proposed method first divides the network into autonomous clusters based on electrical coupling, facilitating preliminary fault area identification. Subsequently, the AOA optimizes fault section identification through current matching analysis. Using MATLAB simulations on an IEEE 33-node system with various DG types and fault scenarios, the method demonstrates superior accuracy and faster convergence compared to traditional approaches. Results confirm its effectiveness in improving fault location performance for modern distribution networks with high DG penetration. Full article

The widespread integration of inverter-based distributed generators (IIDGs) severely limits the adaptability of conventional three-step overcurrent protection in distribution networks (DNs). To address weak rural infrastructure and incomplete post-fault data, this paper proposes a dynamic adaptive current protection strategy for active distribution networks (ADNs) against two-phase short-circuit faults (TPSCFs), using local sequence components. First, we derive analytical expressions for positive/negative-sequence current/voltage at feeder outlet protection devices during TPSCFs, analyzing how the IIDG fault output affects these components. Based on this, an adaptive scheme is developed using only local measurements, with feeder head voltage/current sequence components as criteria. Leveraging line impedance and topology, the scheme ensures selective, accurate fault section identification under incomplete measurements, requiring only feeder head sequence data. A high-IIDG-penetration DN model is built in PSCAD/EMTDC, and TPSCFs under various conditions are simulated. Results show the scheme provides rapid, reliable full-line protection for TPSCFs in IIDG-penetrated ADNs, enhancing protection effectiveness. Full article

This paper presents a PSCAD-based analysis of short-circuit faults and protection characteristics in a real distribution-level microgrid that integrates a 1 MWh battery energy storage system (BESS) with a 500 kW power conversion system (PCS) and a 500 kW photovoltaic (PV) plant connected to a 22.9 kV feeder. While previous studies often rely on simplified inverter models, this paper addresses the critical gap by integrating actual manufacturer-defined control parameters and cable impedances. This allows for a precise analysis of sub-millisecond transient behaviors, which is essential for developing robust protection schemes in inverter-dominated microgrids. The PSCAD model is first verified under grid-connected steady-state operation by examining PV output, BESS power, and grid voltage at the point of common coupling. Based on the validated model, DC pole-to-pole faults at the PV and ESS DC links and a three-phase short-circuit fault at the low-voltage bus are simulated to characterize the fault current behavior of the grid, BESS and PV converters. The DC fault studies confirm that current peaks are dominated by DC-link capacitor discharge and are strongly limited by converter controls, while the AC three-phase fault is mainly supplied by the upstream grid. As an initial application of the model, an instantaneous current change rate (ICCR) algorithm is implemented as a dedicated DC-side protection function. For a pole-to-pole fault, the ICCR index exceeds the 100 A/ms threshold and issues a trip command within 0.342 ms, demonstrating the feasibility of sub-millisecond DC fault detection in converter-dominated systems. Beyond this example, the validated PSCAD model and associated data set provide a practical platform for future research on advanced DC/AC protection techniques and protection coordination schemes in real BESS–PV microgrids. Full article

The Pearl River Mouth Basin is a significant hydrocarbon basin in the northern part of the South China Sea, where deep hydrocarbon exploration has become increasingly important research in recent years. However, the current understanding of the source-to-sink and depositional systems of the Paleogene Zhuhai Formation is still limited, which restricts the exploration and discovery of large-scale sand bodies. Based on core observation, heavy mineral analysis, and well-seismic integrated analysis, this paper clarifies the development of a fan delta-tidal flat depositional and the source-to-sink systems of the Zhuhai Formation. The bedrock in the source region primarily consists of granite, Mesozoic sandstone, and tuff. The source region is divided into five parts (A1–A5), with seven main valleys (V1–V7) developed, supplying sediments to five depositional areas (S1–S5). Additionally, a fault-slope type coupled valley–fan depositional model is established for the study area, revealing the spatiotemporal distribution and main controlling factors of the fan delta system in a steeply dipping boundary fault environment. Catchment area, valley length, and cross-sectional area show a strong positive correlation with sedimentary system scale. Increased elevation difference enhances sediment transport potential energy, while reduced width-to-depth ratio strengthens hydrodynamic forces, promoting sedimentary sand body development and sedimentary system expansion. Full article

As power systems expand and grow smarter, the safe and steady operation of substation equipment has become a prerequisite for grid reliability. In cluttered substation scenes, however, existing deep learning detectors still struggle with small targets, multi-scale feature fusion, and precise localization. To overcome these limitations, we introduce PBZGNet, a defect-detection network that couples a gradual parallel-branch backbone, a zoom-fusion neck, and a global channel-recalibration module. First, BiCoreNet is embedded in the feature extractor: dual-core parallel paths, reversible residual links, and channel recalibration cooperate to mine fault-sensitive cues. Second, cross-scale ZFusion and Concat-CBFuse are dynamically merged so that no scale loses information; a hierarchical composite feature pyramid is then formed, strengthening the representation of both complex objects and tiny flaws. Third, an attention-guided decoupled detection head (ADHead) refines responses to obscured and minute defect patterns. Finally, within the Generalized Focal Loss framework, a quality rating scheme suppresses background interference while distribution regression sharpens the localization of small targets. Across all scales, PBZGNet clearly outperforms YOLOv11. Its lightweight variant, PBZGNet-n, attains 83.9% mAP@50 with only 2.91 M parameters and 7.7 GFLOPs—9.3% above YOLOv11-n. The full PBZGNet surpasses the current best substation model, YOLO-SD, by 7.3% mAP@50, setting a new state of the art (SOTA). Full article

Inter-turn short-circuit (ITSC) faults in motor drives can induce substantial circulating currents and localized thermal stress, ultimately degrading winding insulation and compromising torque stability. To enhance the operational reliability of open-winding (OW) five-phase fault-tolerant fractional-slot concentrated-winding interior permanent-magnet (FTFSCW-IPM) motor drive systems, this paper proposes a real-time fault-tolerant control strategy that provides current suppression and torque stabilization under ITSC conditions. Upon fault detection, the affected phase is actively isolated and connected to an external dissipative resistor, thereby limiting the fault-phase current and inhibiting further propagation of insulation damage. This reconfiguration allows the drive system to uniformly accommodate both open-circuit (OC) and ITSC scenarios without modification of the underlying control architecture. For OC operation, an equal-amplitude modulation scheme based on carrier-based pulse-width modulation (CPWM) is formulated to preserve the required magnetomotive-force distribution. Under ITSC conditions, a feedforward compensation mechanism is introduced to counteract the disturbance generated by the short-circuit loop. A principal contribution of this work is the derivation of a compensation term that can be estimated online using zero-sequence voltage (ZSV) together with measured phase currents, enabling accurate adaptation across varying ITSC severities. Simulation and experimental results demonstrate that the proposed method effectively suppresses fault-phase current, maintains near-sinusoidal current waveforms in the remaining healthy phases, and stabilizes torque production over a wide range of fault and load conditions. Full article