Search Results (556)

In order to analyze and solve the problem of excessive commutation spark of DC motor in ship electric propulsion system, which leads to a decrease in output power and low torque, this paper first establishes a mathematical model of the ship DC motor, builds its simulation model based on the mathematical model, and conducts simulation verification. Secondly, the Cassie arc model is introduced to model the commutation spark, and the Cassie arc model is connected in series in the armature winding of the DC motor to achieve virtual injection of excessive spark fault of the DC motor. Finally, the Fourier transform and wavelet analysis are used to process the data of the armature winding current and excitation current of the DC motor. The simulation results show that when an arc fault occurs in the DC motor, the ripple coefficient of the armature current and excitation current will increase, and the high-frequency component will increase. DB8 is an adopted wavelet function that decomposes the armature current and excitation current six times, and calculates the energy changes before and after the fault of each decomposed signal layer. It is found that without considering the approximate components, the D4 layer wavelet energy of the armature current and excitation current has the largest proportion in the detail components. The D1, D2, and D3 layers’ wavelet decomposition signals of the armature current and excitation current have significant energy changes; that is, the energy increase in the middle and high frequency parts exceeds 20%, and the D3 layer wavelet decomposition signal has the largest energy change, exceeding 40%. This can be used as a fault characteristic quantity to determine whether the DC motor has a large spark fault. This study can provide reference and guidance for online detection technology of excessive sparks in ship DC motors. Full article

Pipelines play a vital role in material transportation within industrial settings. This review synthesizes detection technologies for early-stage small gas leaks from pipelines in the industrial sector, with a focus on acoustic-based methods, optical gas imaging (OGI), and multimodal fusion approaches. It encompasses detection principles, inherent challenges, mitigation strategies, and the state of the art (SOTA). Small leaks refer to low flow leakage originating from defects with apertures at millimeter or submillimeter scales, posing significant detection difficulties. Acoustic detection leverages the acoustic wave signals generated by gas leaks for non-contact monitoring, offering advantages such as rapid response and broad coverage. However, its susceptibility to environmental noise interference often triggers false alarms. This limitation can be mitigated through time-frequency analysis, multi-sensor fusion, and deep-learning algorithms—effectively enhancing leak signals, suppressing background noise, and thereby improving the system’s detection robustness and accuracy. OGI utilizes infrared imaging technology to visualize leakage gas and is applicable to the detection of various polar gases. Its primary limitations include low image resolution, low contrast, and interference from complex backgrounds. Mitigation techniques involve background subtraction, optical flow estimation, fully convolutional neural networks (FCNNs), and vision transformers (ViTs), which enhance image contrast and extract multi-scale features to boost detection precision. Multimodal fusion technology integrates data from diverse sensors, such as acoustic and optical devices. Key challenges lie in achieving spatiotemporal synchronization across multiple sensors and effectively fusing heterogeneous data streams. Current methodologies primarily utilize decision-level fusion and feature-level fusion techniques. Decision-level fusion offers high flexibility and ease of implementation but lacks inter-feature interaction; it is less effective than feature-level fusion when correlations exist between heterogeneous features. Feature-level fusion amalgamates data from different modalities during the feature extraction phase, generating a unified cross-modal representation that effectively resolves inter-modal heterogeneity. In conclusion, we posit that multimodal fusion holds significant potential for further enhancing detection accuracy beyond the capabilities of existing single-modality technologies and is poised to become a major focus of future research in this domain. Full article

To enhance the accuracy and precision of fault diagnosis and location for the collector lines in wind farms under complex operating conditions, an intelligent combined method based on CNN-LSTM and ICEEMDAN-PE-improved wavelet threshold denoising is proposed. A wind power plant model is established using the PSCADV46/EMTDC software. In response to the issue of indistinct fault current signal characteristics under complex fault conditions, a hybrid fault diagnosis model is constructed using CNN-LSTM. The convolutional neural network is utilized to extract the local time-frequency features of the current signals, while the long short-term memory network is employed to capture the dynamic time series patterns of faults. Combined with the improved phase-mode transformation, various types of faults are intelligently classified, effectively resolving the problem of fault feature extraction and achieving a fault diagnosis accuracy rate of 96.5%. To resolve the problem of small fault current amplitudes, low fault traveling wave amplitudes, and difficulty in accurate location due to noise interference in actual wind farms with high-resistance grounding faults, a combined denoising algorithm based on ICEEMDAN-PE-improved wavelet threshold is proposed. This algorithm, through the collaborative optimization of modal decomposition and entropy threshold, significantly improves the signal-to-noise ratio and reduces the root mean square error under simulated conditions with injected Gaussian white noise, stabilizing the fault location error within 0.5%. Extensive simulation results demonstrate that the fault diagnosis and location method proposed in this paper can effectively meet engineering requirements and provide reliable technical support for the intelligent operation and maintenance system of a wind farm. Full article

In low-light optical imaging, the scarcity of incident photons and the inherent limitations of imaging sensors lead to challenges such as low signal-to-noise ratio, limited dynamic range, and degraded contrast, severely compromising image quality and optical information integrity. To address these challenges, we propose a novel low-light image enhancement technique, LightenResDiff, which combines a residual diffusion model with the discrete wavelet transform. The core innovation of LightenResDiff lies in it accurately restoring the low-frequency components of an image through the residual diffusion model, effectively capturing and reconstructing its fundamental structure, contours, and global features. Additionally, the dual cross-coefficients recovery module (DCRM) is designed to process high-frequency components, enhancing fine details and local contrast. Moreover, the perturbation compensation module (PCM) mitigates noise sources specific to low-light optical environments, such as dark current noise and readout noise, significantly improving overall image fidelity. Experimental results across four widely-used benchmark datasets demonstrate that LightenResDiff outperforms existing methods both qualitatively and quantitatively, surpassing the current state-of-the-art techniques. Full article

Series arc faults are a major cause of electrical fires, posing significant risks to life and property. Their negative-resistance characteristics make fault features difficult to detect, and the existing methods often suffer from high false-alarm rates, poor adaptability, and reliance on high sampling rates and long sampling windows. To enhance the accuracy and efficiency of series AC arc fault detection, this paper proposes a rapid identification method based on singular spectrum statistical features and a differential evolution-optimized XGBoost classifier. The approach first constructs the singular spectrum of current waveforms via a Hankel matrix singular value decomposition and extracts nine statistical features. It then optimizes seven XGBoost hyperparameters using differential evolution to build an efficient classification model. The experiments on 18,240 current samples covering 16 load conditions (including eight arc fault types) show that the method achieves an average identification accuracy of 98.90% using only three nominal cycles (60 ms) of current waveform. Even with a training set ratio as low as 5%, it maintains 97.11% accuracy, outperforming Back-propagation Neural Network, Support Vector Machine, and Recurrent Neural Network methods by up to three percentage points. The method avoids the need for high sampling rates or complex time–frequency transformations, making it suitable for resource-constrained embedded platforms and offering a generalizable solution for series arc fault detection. Full article

This study delves into the response characteristics of core grounding current signals in power transformers across different operating conditions, aiming to enhance the accuracy of transformer condition assessment. Existing detection technologies often rely on single-parameter methods, which fall short in providing a comprehensive evaluation of transformer conditions. To address this limitation, this research develops a wideband circuit model based on multi-conductor transmission line theory and backed by experimental validation. The model systematically investigates the response mechanisms of core grounding current to various electrical stresses, including impulse voltages, power-frequency harmonics, and partial discharges. The findings reveal distinct response characteristics of core grounding current under different stresses. Under impulse voltage excitation, the core current exhibits high-frequency oscillatory decay with characteristics linked to voltage waveform parameters. In harmonic conditions, the current spectrum shows linear correspondence with excitation voltages, with no resonance below 1 kHz. Partial discharges induce high-frequency oscillations in the grounding current due to multi-resonant networks formed by distributed winding-core parameters. This study establishes a new theoretical framework for transformer condition assessment based on core grounding current analysis, offering critical insights for optimizing detection technologies and overcoming the limitations of traditional methods. Full article

As a form of non-destructive testing, eddy current testing is widely used for detecting surface micro-damage on metal components in sectors such as aerospace. Conventional frequency-domain analysis techniques often fail to effectively extract defect-related features from non-stationary eddy current signals. This paper proposes an ECT system based on the Discrete Wavelet Transform to address this limitation. In hardware design, the system employs a DDS to generate a 1 MHz excitation signal for the probe. High-precision acquisition of defect response signals is achieved using an IQ demodulator and a 24-bit ADC. For signal processing, the Haar wavelet is applied for single-level decomposition. This method successfully isolates the defect response signal within the high-frequency detail coefficients. Experimental results demonstrate that for a metal surface notch with a depth of 1 mm, the system significantly improves the SNR, resulting in a ΔSNR improvement of 3.64 dB, which is 0.36 dB higher than that achieved using time-domain processing. Full article

This article presents a novel method for detecting series arc faults (SAFs) in residential applications using time-domain imaging (TDI) and Long Short-Term Memory (LSTM) networks. The proposed method transforms current signals into grayscale images by filtering out the fundamental frequency, allowing key arc fault characteristics—such as high-frequency noise and waveform distortions—to become visually apparent. The use of Ensemble Empirical Mode Decomposition (EEMD) helped isolate meaningful signal components, although it was computationally intensive. To address real-time requirements, a simpler yet effective TDI method was developed for generating 2D images from current data. These images were then used as inputs to an LSTM network, which captures temporal dependencies and classifies both arc faults and appliance types. The proposed TDI-LSTM model was trained and tested on 7000 labeled datasets across five common household appliances. The experimental results show an average detection accuracy of 98.1%, with reduced accuracy for loads using thyristors (e.g., dimmers). The method is robust across different appliance types and conditions; comparisons with prior methods indicate that the proposed TDI-LSTM approach offers superior accuracy and broader applicability. Trade-offs in sampling rates and hardware implementation were discussed to balance accuracy and system cost. Overall, the TDI-LSTM approach offers a highly accurate, efficient, and scalable solution for series arc fault detection in smart home systems. Full article

Under the background of “dual-carbon” strategy and global energy transition, the metallurgical industry, which accounts for 15–20% of industrial energy consumption, urgently needs to reduce the energy consumption and emission of DC power supply of electric furnaces. Aiming at the existing 400–800 V/≥3000 A industrial-frequency transformer-rectifier system with low efficiency, large volume, heat dissipation difficulties and other bottlenecks, this thesis proposes and realizes a high-frequency integrated DC power supply scheme for high-power electric furnaces: high-frequency transformer core and rectifier circuit are deeply integrated, which breaks through and reduces the volume of the system by more than 40%, and significantly reduces the iron consumption; multiple cores and three windings in parallel are used for the system. The topology of multiple cores and three windings in parallel enables several independent secondary stages to share the large current of 3000 A level uniformly, eliminating the local overheating and current imbalance; the combination of high-frequency rectification and phase-shift control strategy enhances the input power factor to more than 0.95 and cuts down the grid-side harmonics remarkably. The authors have completed the design of 100 kW prototype, magneto-electric joint simulation, thermal structure coupling analysis, control algorithm development and field comparison test, and the results show that the program compared with the traditional industrial-frequency system efficiency increased by 12–15%, the system temperature rise reduced by 20 K, electrode voltage increased by 10–15%, the input power of furnace increased by 12%, and the harmonic index meets the requirements of the traditional industrial-frequency system. The results show that the efficiency of this scheme is 12–15% higher than the traditional IF system, the temperature rise in the system is 20 K lower, the voltage at the electrode end is 10–15% higher, the input power of the furnace is increased by 12%, and the harmonic indexes meet the requirements of GB/T 14549, which verifies the value of the scheme for realizing high efficiency, miniaturization, and reliable DC power supply in metallurgy. Full article

The complex topology of shipboard DC microgrids and the strong coupling between positive and negative poles during faults pose significant challenges for faulted-pole identification, especially under high-resistance conditions. To address these issues, this paper proposes a novel faulted-pole identification method based on S-Transformation and convolutional neural networks (CNNs). Single-ended voltage and current measurements from the generator side are used to generate time–frequency spectrograms via S-Transformation, which are then processed by a CNN trained to classify the faulted pole. This approach avoids reliance on complex threshold settings. Simulation results on a representative shipboard DC microgrid demonstrate that the proposed method achieves high accuracy, fast response, and strong robustness, even under high-resistance fault scenarios. The method significantly enhances the selectivity and reliability of fault protection, offering a promising solution for advanced marine DC power systems. Compared to conventional fault-diagnosis techniques, the proposed model achieves notable improvements in classification accuracy and computational efficiency for line-fault detection. Full article

The uncertainties arising from high renewable energy penetration on both the generation and demand sides pose significant challenges to distribution network security. Smart microgrids are considered an effective way to solve this problem. Existing studies exhibit limitations in prediction accuracy, Alternating Current (AC) power flow modeling, and integration with optimization frameworks. This paper proposes a closed-loop technical framework combining high-confidence interval prediction, second-order cone convex relaxation, and robust optimization to facilitate renewable energy integration in distribution networks via smart microgrid technology. First, a hybrid prediction model integrating Variational Mode Decomposition (VMD), Long Short-Term Memory (LSTM), and Quantile Regression (QR) is designed to extract multi-frequency characteristics of time-series data, generating adaptive prediction intervals that accommodate individualized decision-making preferences. Second, a second-order cone relaxation method transforms the AC power flow optimization problem into a mixed-integer second-order cone programming (MISOCP) model. Finally, a robust optimization method considering source–load uncertainties is developed. Case studies demonstrate that the proposed approach reduces prediction errors by 21.15%, decreases node voltage fluctuations by 16.71%, and reduces voltage deviation at maximum offset nodes by 17.36%. This framework significantly mitigates voltage violation risks in distribution networks with large-scale grid-connected photovoltaic systems. Full article

Agricultural nondestructive testing technology is pivotal in safeguarding food quality assurance, safety monitoring, and supply chain transparency. While conventional optical methods such as near-infrared spectroscopy and hyperspectral imaging demonstrate proficiency in surface composition analysis, their constrained penetration depth and environmental sensitivity limit effectiveness in dynamic agricultural inspections. This review highlights the transformative potential of microwave technologies, systematically examining their operational principles, current implementations, and developmental trajectories for agricultural quality control. Microwave technology leverages dielectric response mechanisms to overcome traditional limitations, such as low-frequency penetration for grain silo moisture testing and high-frequency multi-parameter analysis, enabling simultaneous detection of moisture gradients, density variations, and foreign contaminants. Established applications span moisture quantification in cereal grains, oilseed crops, and plant tissues, while emerging implementations address storage condition monitoring, mycotoxin detection, and adulteration screening. The high-frequency branch of the microwave–millimeter wave systems enhances analytical precision through molecular resonance effects and sub-millimeter spatial resolution, achieving trace-level contaminant identification. Current challenges focus on three areas: excessive absorption of low-frequency microwaves by high-moisture agricultural products, significant path loss of microwave high-frequency signals in complex environments, and the lack of a standardized dielectric database. In the future, it is essential to develop low-cost, highly sensitive, and portable systems based on solid-state microelectronics and metamaterials, and to utilize IoT and 6G communications to enable dynamic monitoring. This review not only consolidates the state-of-the-art but also identifies future innovation pathways, providing a roadmap for scalable deployment of next-generation agricultural NDT systems. Full article

Inter-turn short-circuit (ITSC) faults in permanent magnet synchronous machines (PMSMs) present a significant reliability challenge in electric vehicle (EV) drivetrains, particularly under non-stationary operating conditions characterized by inverter-driven transients, variable loads, and magnetic saturation. Existing diagnostic approaches, including motor current signature analysis (MCSA) and wavelet-based methods, are primarily designed for steady-state conditions and rely on manual feature selection, limiting their applicability in real-time embedded systems. Furthermore, the lack of publicly available, high-fidelity datasets capturing the transient dynamics and nonlinear flux-linkage behaviors of PMSMs under fault conditions poses an additional barrier to developing data-driven diagnostic solutions. To address these challenges, this study introduces a simulation framework that generates a comprehensive dataset using finite element method (FEM) models, incorporating magnetic saturation effects and inverter-driven transients across diverse EV operating scenarios. Time-frequency features extracted via Discrete Wavelet Transform (DWT) from stator current signals are used to train a Transformer model for automated ITSC fault detection. The Transformer model, leveraging self-attention mechanisms, captures both local transient patterns and long-range dependencies within the time-frequency feature space. This architecture operates without sequential processing, in contrast to recurrent models such as LSTM or RNN models, enabling efficient inference with a relatively low parameter count, which is advantageous for embedded applications. The proposed model achieves 97% validation accuracy on simulated data, demonstrating its potential for real-time PMSM fault detection. Additionally, the provided dataset and methodology contribute to the facilitation of reproducible research in ITSC diagnostics under realistic EV operating conditions. Full article

This paper introduces an enhanced bi-directional full-bridge resonant converter designed for Vehicle-to-Grid (V2G) systems. A key innovation lies in the incorporation of an auxiliary LC resonant circuit connected via a tertiary transformer winding. This circuit dynamically modifies the magnetizing inductance based on operating frequency, enabling soft-switching across all primary switches, specifically, Zero-Voltage Switching (ZVS) at turn-on and near Zero-Current Switching (ZCS) at turn-off across the entire load spectrum. Additionally, the converter supports both Constant Current (CC) and Constant Voltage (CV) charging modes at distinct, fixed operating frequencies, thus avoiding wide frequency variations. A 3.3 kW prototype developed for onboard electric vehicle charging applications demonstrates the effectiveness of the proposed topology. Experimental results confirm high efficiency in both charging and discharging operations, achieving up to 98.13% efficiency in charge mode and 98% in discharge mode. Full article

Multi-channel LED drivers are crucial for high-power lighting applications. Maintaining a constant average forward current is essential for stable LED luminous intensity, necessitating drivers capable of consistent current delivery across wide operating ranges. Meanwhile, achieving precise current sharing among channels without incurring high costs and system complexity is a significant challenge. Leveraging the constant-current characteristics of the LCL-T network, this paper presents a multi-channel DC/DC LED driver comprising a full-bridge inverter, a transformer, and a passive resonant rectifier. The driver generates a high-frequency AC bus with series-connected diode rectifiers, a structure that guarantees excellent current sharing among all output channels using only a single control loop. Fully considering the impact of higher harmonics, this paper derives an exact solution for the output current. A step-by-step parameter design methodology ensures soft switching and enhanced switch utilization. Finally, experimental verification was conducted using a prototype with five channels and 200 W, confirming the correctness and accuracy of the theoretical analysis. The experimental results showed that within a wide input voltage range of 380 V to 420 V, the driver was able to provide a stable current of 700 mA to each channel, and the system could achieve a peak efficiency of up to 94.4%. Full article