Search Results (120)

DC series arc faults (SAFs) are a significant safety hazard on the DC side of photovoltaic (PV) systems, with current signals characterized by strong randomness, obvious non-stationarity, and concealed fault features, posing challenges for rapid and accurate detection. With the development of application models such as agricultural PV integration, photovoltaic greenhouses, solar-powered irrigation, and livestock energy supply, the demand for the safe operation of photovoltaic systems in agricultural production scenarios is becoming increasingly prominent. To address the difficulty in fully characterizing the multi-scale dynamic features and local amplitude disturbances of DC SAF signals, this paper proposes a SAF detection method based on multi-scale generalized amplitude-aware permutation entropy (MS-GAAPE). The method extracts MS-GAAPE from arc current signals at various scales using sliding window-based generalized coarse-graining, which preserves temporal sequence information while improving the characterization of local amplitude variations. Particle swarm optimization (PSO) is applied to optimize these multi-scale features, strengthening fault-related information and reducing interference. The optimized features are then processed by a support vector machine (SVM) for SAF detection. The dataset used contains 50,000 samples covering transient conditions such as voltage fluctuations and is divided into a training set and an independent test set in a 70% to 30% ratio. The training set is utilized for feature parameter determination, feature weight optimization, and classification model construction, while the independent test set is reserved solely for final performance evaluation. Experimental results demonstrate that the proposed method achieves excellent detection performance under various operating conditions and load levels, with an accuracy of 99.32% and a total detection time of 103.62 ms, meeting the requirements of the UL1699B standard, thus showcasing strong real-time detection capability and potential for embedded implementation. Full article

In low-voltage distribution systems, series arc faults caused by poor contact and loose connections are a leading cause of electrical fires. Due to the negative resistance characteristics of arcs, such faults are difficult to detect using conventional overcurrent or leakage protectors. Existing detection methods predominantly rely on wavelet-based feature extraction or threshold-based classifiers. Wavelet transforms require predefined basis functions and lack adaptability to non-stationary current signals from appliances such as induction cookers. Threshold-based classifiers produce excessive false alarms under varying load conditions, as normal non-stationary load waveforms share high-frequency characteristics with arc fault signatures. As a result, existing arc fault protectors exhibit high false alarm rates, limiting practical deployment. To address these limitations, this study proposes a method for diagnosing low-voltage series arc faults based on differential-sliding window higher-order cumulants (HoCs) and stacked autoencoders (SAEs). The method first employs a differential-sliding time window approach to extract HoC features from current signals across seven typical loads, establishing a feature vector database for arc fault patterns. A symmetric stacked autoencoder (SAE) is constructed, trained using layer-wise pretraining to optimize hyperparameters and select the model with the best generalization performance. Experimental results demonstrate that the proposed method achieves a detection accuracy of 96.4% with a false alarm rate of 0% across all tested loads. Full article

Wind turbine main bearings are critical drivetrain components whose operating status directly affects the stability and safety of the entire unit. However, traditional unsupervised health monitoring methods suffer from difficulty in capturing early weak faults, low anomaly detection sensitivity, and inability to fully exploit axial–radial vibration coupling characteristics. To address these issues, this paper proposes an Axial–Radial Coupling-aware Informer (ARC-Informer) for unsupervised main bearing health monitoring. First, 20 time-frequency domain features are extracted from each of the axial and radial vibration signals and concatenated into a 40-dimensional coupled health feature vector. A cross-attention-based coupling enhancement module with residual fusion explicitly models the dynamic interaction between the two directions. Second, a self-attention channel-gating mechanism adaptively reweights the feature channels, and an Informer backbone captures long-range temporal dependencies for multistep prediction of the coupled features. At last, a health index (HI) is constructed from the prediction residuals, with a 99.7% quantile threshold and a six-step consecutive exceedance criterion for anomaly alarm triggering. Experimental results on real wind turbine data show that the proposed ARC-Informer achieves MSE of 0.180–0.257 across prediction horizons 1–16, with its advantage over TPE-optimized baselines (GRU, LSTM, RNN, TCN) growing from negligible at short horizons to 8.1% MSE reduction at H = 16, validating the effectiveness of the coupling enhancement for long-range forecasting. A cross-turbine case study on 10 healthy segments from five wind turbines confirms zero false alarms, and a simulated fault experiment successfully triggers an early warning, demonstrating practical unsupervised health monitoring capability. Full article

Dissolved gas analysis (DGA) is an essential technique for the fault diagnosis and condition monitoring of oil-immersed power transformers. Among various characteristic gases, acetylene (C₂H₂) is a key indicator of high-energy discharge and arc faults. In this work, a high-sensitivity dissolved acetylene detection system is developed based on clamp-type quartz-enhanced photoacoustic spectroscopy (QEPAS). A specially designed clamp-type quartz tuning fork (Clamp-type QTF) is employed as the acoustic transducer to improve acoustic coupling efficiency and optical alignment tolerance. Compared with conventional standard quartz tuning forks, the clamp-type structure exhibits enlarged acoustic interaction volume, lower damping loss, and higher signal collection capability. A near-infrared distributed feedback (DFB) laser operating at 1531.6 nm is used as the excitation source. The dissolved gas is extracted from transformer oil using a headspace degassing module and introduced into the QEPAS cell for real-time measurement. Experimental results showed that the developed system achieves a 1σ-based SNR-estimated detection limit of 17 ppb at a 50 s integration time, derived from the continuous measurement of 0.75 ppm C₂H₂, with excellent linearity in the concentration range from 100 ppm to 500 ppm. The measured concentration of dissolved acetylene in transformer oil is in good agreement with gas chromatography (GC), validating the effectiveness and practical applicability of the proposed system. Full article

In 3 kV DC systems, the pantograph–catenary interface and auxiliary converter unit (ACU) are among the critical high-voltage subsystems, where electrical transients and thermal overload conditions frequently lead to service disruptions. This paper presents a case study on the application of machine-learning-based predictive maintenance to a 3 kV DC electric train, with a specific focus on the pantograph and ACU. A 2-year period of operational data collected from a passenger rail fleet was analysed using a hybrid data sampling strategy to capture both operational conditions and events associated with failures. Logistic Regression (LR), and Random Forest (RF) were trained and evaluated using standard performance metrics. The RF model achieved superior predictive performance, with an accuracy of approximately 93%, a precision of 0.91, a recall of 0.88, and an F1-score of 0.89, outperforming the baseline across all metrics. The analyses demonstrated that anomalies in electrical arcing, line voltage, and ACU current and temperature frequently preceded recorded fault events, confirming that failures arise from subsystems interactions and that it is critical for such parameters to be monitored. The results demonstrate the technical feasibility and practical value of integrating machine learning into EMU maintenance practice, enabling earlier detection of degradation, more targeted interventions, and a transition towards condition-based maintenance. Full article

Series arc faults on the DC side of photovoltaic (PV) systems are a critical hazard that can trigger system fires. Conventional contact-based detection methods suffer from cumbersome installation and high retrofit cost, whereas existing non-contact approaches mostly rely on megahertz-level high-frequency sampling and therefore require expensive radio-frequency instrumentation or high-performance computing platforms. As a result, it remains difficult to simultaneously achieve strong interference immunity and real-time performance on low-cost embedded devices with limited resources. To address this engineering paradox between high-frequency sampling and constrained computational capability, this paper proposes a fully embedded, non-contact arc fault detection system based on a 12–80 kHz low-frequency sub-band selection strategy. By exploiting the physical characteristic of broadband energy elevation induced by arc faults, the proposed strategy avoids dependence on high-bandwidth hardware. Guided by this strategy, a Moebius-topology coaxial shielded loop antenna is employed as the near-field sensor, while an ultra-simplified passive analog front end is constructed directly by using the on-chip programmable gain amplifier and analog-to-digital converter of the microcontroller unit, enabling efficient signal acquisition and fast Fourier transform processing within the target sub-band. To cope with complex background noise in the low-frequency range, an environment-adaptive baseline mechanism based on exponential moving average and exponential absolute deviation is developed for dynamic decoupling. In addition, a lightweight INT8-quantized multilayer perceptron is introduced as a nonlinear auxiliary module, thereby forming a robust hybrid decision architecture with complementary rule-based and artificial intelligence components. Experimental results show that, under the tested household, laboratory, and PV-site conditions, the proposed system achieved an overall detection rate of 97%, while the remaining 3% mainly corresponded to failed ignition or non-sustained arc attempts rather than persistent false triggering during normal monitoring. Full article

Tree contact discharge is a key contributing factor to wildfires caused by medium-voltage insulated conductors. Prolonged abrasion of the insulation layer by branches gradually creates weak points in the insulation. When subjected to lightning strikes, these areas are prone to forming lightning-induced pinholes, which can subsequently trigger partial discharge and even ignition. This study systematically investigates the discharge-induced ignition mechanism for 10 kV overhead insulated conductors in tree contact scenarios by establishing an experimental platform integrated with high-speed imaging, ultraviolet detection, and simulation methods. Three types of typical defects were set up in the experiments: complete insulation abrasion, lightning puncture holes accompanied by localized abrasion, and lightning puncture holes without abrasion. The development process and characteristics of different discharge forms were observed and analyzed. The results indicate that the tree contact discharge ignition mechanism can be categorized into two types: thermal accumulation and direct arcing. The former occurs when insulation abrasion or composite defects exist, where sustained partial discharge or a high-resistance current leads to gradual heat accumulation, resulting in an ignition delay lasting tens of seconds. The latter occurs when only small defects such as lightning puncture holes exist in the insulation layer. A concentrated arc forms due to gap breakdown under high voltage, leading to a millisecond-level ignition process. The study found that different discharge forms produce significantly distinct ablation and carbonization patterns on both the insulation layer and the branch surface, reflecting differences in energy transfer pathways. Simulation analysis further indicated that the thickness of the insulation layer affects the electric field distribution in the tree contact gap, with the initial discharge field strength decreasing as the thickness increases. This study provides experimental evidence and classification guidance for tree contact fault monitoring, insulation condition assessment, and wildfire prevention and control in medium-voltage distribution networks. Full article

In electric vehicle (EV) charging systems, DC series arc faults, due to their high concealment and severe hazard, have become one of the important causes of electric vehicle fire accidents. An improved hybrid arc fault model of a charging system was established in Simulink for preliminary study. The results show that the high-frequency noise generated by arc faults affects the output voltage quality of the charger, and this noise is conducted to the battery voltage. Arc faults in a real electric vehicle charging experimental platform were further investigated, where it was found that, during arc fault events, the charging system provides no alarm indication, and the current signals exhibit significant large-amplitude random disturbances and nonlinear fluctuations. Moreover, under normal conditions during vehicle charging startup and the pre-charge stage, the current waveforms also present high-pulse spike characteristics similar to arc faults. Finally, a carefully designed deep neural network-based arc fault detection algorithm, Arc_TCNsformer, is proposed. The current signal samples are directly input into the network model without manual feature selection or extraction, enabling end-to-end fault recognition. By integrating a temporal convolutional network for multi-scale local feature extraction with a sparse Transformer for contextual information aggregation, the proposed method achieves strong robustness under complex charging noise environments. Experimental results demonstrate that the algorithm not only provides high detection accuracy but also maintains reliable real-time performance when deployed on embedded edge computing platforms. Full article

Series arc faults often occur in aging or faulty electrical systems due to insulation degradation, poor contact, or corrosion. These faults typically generate low current signatures, which are difficult to detect with traditional overcurrent protection methods. To address this measurement challenge, this paper proposes a systematic fault detection framework that combines discriminative feature extraction, statistical validation, and optimized classification. To comprehensively characterize arc fault signals, a diverse set of time- and frequency-domain features is extracted, and composite multiscale entropy is introduced to quantify nonlinear and transient fault dynamics more effectively. The MRMR (Maximum Relevance Minimum Redundancy) algorithm is applied to select features with high information content and low redundancy, thereby improving model generalization. A random search algorithm is used to adaptively optimize the random forest hyperparameters, establishing a high-accuracy fault diagnosis model. The experimental setup was established based on the UL1699B standard using a 115 V/400 Hz arc fault platform, and 1800 sets of data under nine different load types were collected for training and validation. Experimental results show that the proposed method outperforms five mainstream machine learning algorithms in terms of fault detection accuracy and performance. The results confirm its metrological robustness and its potential for deployment in waveform-based fault electrical monitoring systems. Full article

Arc fault is the dominant cause of fire in photovoltaic (PV) systems, making its accurate identification crucial for PV fire prevention. This study investigates the influence of voltage (200, 300, and 400 V) and current (3, 5, 7, 9, and 11 A) on the DC series arc fault characteristics in PV systems obtained through experimental analysis. The results show that voltage variation has a negligible impact on arc fault behavior, while higher current levels substantially increase noise in the arc fault signals. To effectively mitigate noise, this paper proposes a denoising method that combines an improved moss growth optimization algorithm (IMGO) with improved complete ensemble empirical mode decomposition featuring adaptive noise (ICEEMDAN). It is found that the IMGO-ICEEMDAN denoising algorithm can effectively diminish noise in current signals, broaden characteristic frequency bands, and ameliorate arc feature discernibility. Subsequently, an integrated multi-scale spatiotemporal model is developed to extract arc fault features from the denoised signals. The model employs wide deep convolutional neural networks (WDCNNs) and bidirectional long short-term memory (BiLSTM) networks for parallel feature extraction, supplemented by a cross-attention (CA) module to optimize feature integration. The proposed WDCNN-BiLSTM-CA model ultimately achieves a detection accuracy of 99.89%, demonstrating superior detection performance over conventional methods, such as CNN-GRU and 1DCNN-LSTM models. This work provides a reliable framework for arc fault detection and fire risk reduction in PV systems. Full article

Low-voltage AC series arc faults are a leading cause of electrical fires, posing significant risks to life and property. While artificial intelligence-based detection methods have achieved high accuracy, they often suffer from limited interpretability and are typically tailored to specific loads, thus struggling to adapt to the diverse and dynamic load conditions in residential environments. To address these limitations, this paper proposes a novel interpretable arc fault detection model based on prototype learning with a hybrid attention mechanism. Specifically, we design a Tri-Domain Dynamic Attention (TDDA) module that integrates time-domain, frequency-domain, and temporal derivative information, and embed it into a Convolutional Neural Network (CNN) for enhanced feature extraction. Visual prototypes are constructed from sample characteristics, forming a tri-domain arc fault prototype set. A dedicated non-arc prototype set is further introduced to refine the decision boundary and improve accuracy. The proposed model is validated through comprehensive experiments and hardware implementation on a dedicated test platform. Results demonstrate that our model achieves an accuracy of 99.65%, maintains over 99% accuracy across various single-load conditions, and exhibits high detection performance under complex multi-load scenarios. Full article

Timely identification of series arc faults (SAFs) is of vital importance for preventing electrical fires. How to identify SAFs at the output side of a frequency converter (i.e., output-side SAF) is still not clear. A new approach of identifying output-side SAFs by analyzing the output current signals from frequency converters was proposed. First, output-side SAF experiments were performed with harmonic power supplies. Second, the output current signals were decomposed into eight modal components by empirical wavelet transform and an autoregressive model was established. The autoregressive model parameters and the energy ratios of the first three modal components were adopted as the fault features. Finally, an optimized support vector machine was designed and employed to identify SAFs. Comparison tests with similar methods were performed and performance tests under different noise levels and operation conditions were conducted. The test results indicated that the proposed scheme can effectively recognize output-side SAFs. Its runtime is shorter than 1.4 ms. This method provides a reference for the development of industrial three-phase SAF detection devices. Full article

To address the reclosing failures in the distribution networks (DNs) with high penetration of distributed energy resources (DERs), this paper proposes a communication-free cooperative fault recovery control method based on staged active power injection of an energy storage (ES) system. First, during the initial phase of a fault, a back-electromotive force (b-EMF) suppression arc extinction control strategy was designed for the ES converter, promoting fault arc extinction. Subsequently, the ES switches to grid-forming (GFM) control, providing active power injection to the network following the circuit breaker (CB) tripping. A time-limited variable power control of ES converter is also designed to establish voltage characteristics for fault state detection. And a fault state criterion based on voltage relative entropy is designed, helping reliable reclosing. Simulation results demonstrate that the proposed method achieves coordination solely through local measurements without the need for real-time communication between ES and CB, and can shorten the recovery time of transient faults to hundreds of milliseconds. Full article

The development of high-performance DC circuit breakers (DCCBs) and rapid fault detection schemes is a crucial and challenging part of advancing Modular Multilevel Converter (MMC) HVDC grids. This paper introduces a new current-limiting DCCB that uses the differential discharge times of shunt capacitors to generate artificial current zero-crossings, thus facilitating arc quenching. This mechanism significantly reduces the effect of fault currents on the MMC. The shunt capacitors and arresters in the proposed breaker also offer voltage support during faults, effectively stopping transient traveling waves from spreading to nearby non-fault lines. This feature creates an effective line protection boundary in multi-terminal HVDC systems. Additionally, a fast fault detection scheme with primary and backup protection is proposed. A four-terminal MMC-HVDC (±500 kV) simulation model is built in PSCAD/EMTDC to validate the scheme. The results demonstrate the excellent fault detection performance of the proposed method. The voltage and current behavior during the interruption process of the new DCCB is also analyzed and compared with that of a hybrid DCCB. Full article

Arc fault detection is a key technology for preventing electrical fires. However, existing research has primarily focused on series connections, with insufficient attention paid to parallel load conditions, which are prevalent in real-world residential electricity usage. In accordance with the UL 1699 and IEC 62606 standards, this study established an experimental platform for arc faults, incorporating seven single loads (categorized into four types) and nine multi-load combinations. A systematic analysis of the differences in time–frequency characteristics under different connection modes was conducted. Time-domain and frequency-domain analyses revealed that under parallel connection the dispersion of arc fault time-domain characteristics decreases by more than 50% and the fundamental frequency component increases significantly. For parallel multi-load scenarios, the fundamental component of resistive combinations can reach 90%, while the frequency variance of inductive combinations can be as high as 400,000. By elucidating the time–frequency domain characteristics of parallel arc faults, this study proposes an optimized feature parameter analysis scheme for electrical fire monitoring systems. Based on this, this paper proposes an arc fault detection method using the Dual-Channel Convolutional Neural Network (DCNN). The method achieves 97.09% recognition accuracy for arc faults with different connection modes. Comparative experiments with other models and ablation studies show that the model attains 98.52% detection accuracy, verifying the effectiveness of the proposed method. This approach can significantly improve the accuracy of arc fault detection in multi-load environments, thereby enabling early warning of electrical circuit faults and potential fire hazards. Full article