In renewable-powered distribution systems and microgrids, reliable cable condition monitoring is essential for operational security and early fault detection. Sheath circulating current signals provide valuable information for identifying grounding abnormalities and incipient faults, but their diagnosis is difficult because the signals exhibit strong inter-phase coupling, and fault samples are limited in practice. This study proposes a dual-view Mixup-ResNet framework for fault diagnosis of cable sheath circulating current signals. Specifically, physical-range normalization is employed to retain magnitude-related fault information and inter-phase proportional relationships, while sample-wise z-score normalization is used to emphasize waveform morphology. These two complementary views are concatenated to form a six-channel input for a lightweight one-dimensional residual network, which is trained with Mixup, label smoothing, dropout, and cosine annealing. On an ATP-EMTP-generated eight-class dataset, the proposed method achieves an average accuracy of 91.50%, a weighted F1-score of 91.69%, and a macro F1-score of 91.69% under a unified 5 × 5 repeated stratified cross-validation protocol. Additional tests under 12% relative Gaussian noise show that the method maintains competitive Gaussian-noise tolerance within the tested condition, although broader field disturbances remain to be further validated. These findings suggest that the proposed method has potential for small-sample fault diagnosis of cable sheath circulating current signals and provides a preliminary basis for intelligent cable condition monitoring.
Inter-turn short-circuit faults are a common failure mode in permanent magnet synchronous motors, and their early diagnosis is essential for ensuring safe and reliable operation. In practical applications, however, training data are often contaminated with noisy labels, while incipient fault features are weak and exhibit limited inter-class separability, which further aggravates label misassignment and mixing. To enhance data usability and label reliability, this paper proposes a diagnostic framework integrating high-frequency signal injection with unsupervised learning. High-frequency signal injection is used to strengthen fault-related current features, followed by autoencoder-based feature extraction, UMAP dimensionality reduction, and HDBSCAN clustering to group samples, after which a majority voting strategy is applied for sample relabeling. Experimental results demonstrate that the proposed method can effectively improve noisy-label correction performance even under high noise ratios and low fault severity, thereby providing reliable data for subsequent fault diagnosis.
Haoguang Wang, Baoye Song, Wei Xin et al.· International Conference on...· 0 citations
Three-phase inverters are widely used in renewable energy conversion, industrial drives, and energy storage systems. Their IGBTs and other power switching devices often work for long periods under high-frequency and high-current conditions, which makes fault diagnosis an important issue for reliable operation. Traditional diagnosis methods usually depend on manually designed features obtained from Fourier transform, wavelet analysis, or related signal-processing tools. Although these features are interpretable, their performance is closely tied to expert experience. Purely data-driven deep learning models can learn features from raw waveforms, but they often show limited physical interpretability and may overfit when fault samples are insufficient. This paper proposes a lightweight Multi-view Fault Feature Fusion Network (MFF-Net) for fault waveform classification in three-phase inverter circuits. The model contains a lightweight one-dimensional convolutional neural network branch for temporal waveform representation and a physics-guided branch for extracting single-phase statistics, three-phase imbalance indices, zero-sequence components, and multi-band harmonic energy ratios. A gated fusion module is then used to combine the two feature groups according to sample-specific fault characteristics. A simulated dataset with 3,200 samples is built under normal, overcurrent, phase-loss, and IGBT bridge-arm open-circuit conditions. Experimental results show that MFF-Net achieves stable training, high classification accuracy, and clear feature separation, offering a feasible solution for lightweight online fault diagnosis in power electronic systems.
Huiyu Gan· Applied and Computational En...· 0 citations
Arc faults in low-voltage smart-farm distribution systems are difficult to detect reliably because normal current waveforms vary with load composition and operating conditions. This paper presents a progressive three-stage time–frequency learning framework that identifies series arc faults directly from normalized current waveforms. In Stage I, fast Fourier transform (FFT)-based and wavelet-based learnable front-ends independently learn complementary global spectral and local transient representations. In Stage II, their representation spaces are structured using supervised contrastive learning with view-level and cross-spectrum alignment. In Stage III, the aligned feature extractors are frozen, and only the feature fusion block and final classifier are trained for 10-class classification. Current signals are acquired from a practical smart-farm distribution system and evaluated using a measurement-session-level group-wise split. The framework achieves 99.92% accuracy and 99.93% macro recall while maintaining 1.06 M parameters and a model-only inference latency of 8.70 ms. The results demonstrate that the proposed framework provides robust discrimination across unseen measurement sessions within the evaluated load categories and operating conditions.
Seoyoung Jeon, Won-Kyu Choi, Sungsoo Kwon et al.· Italian National Conference...· 0 citations
Inverter-based resources and IEC 61850 process-bus measurements introduce new protection challenges, including nontraditional fault behavior and measurement-domain cyber-physical attacks. This paper evaluates DL-Xformer, an attention-based Transformer classifier for multi-class fault and cyberattack diagnosis, side-by-side with Dynamic State Estimation-Based Protection (DSE-EBP) on identical high-fidelity electromagnetic-transient (EMT) streaming measurements from an IBR-rich power grid. The evaluation uses an 18-class taxonomy covering normal operation, 11 physical faults, and six measurement-domain attacks, including CT/PT ratio manipulation and GPS spoofing, sampled at 4.8 kHz from synchronized upstream and downstream merging units. DSE-EBP detects all streaming anomalies in 0.417-1.660 ms, with a mean detection time of 0.756 ms, while DL-Xformer classifies the same events in 2.50-50.42 ms, with a mean classification time of 13.46 ms. The longest delay occurs in a deliberate stress case where a CT ratio attack is introduced while residual oscillations from a preceding DLG fault have not fully settled; the event-window accuracy drops to 76.1 %, but the stable final classification remains correct. Measurement-level feature attribution shows that the DL-Xformer decision is driven by physically meaningful current and voltage channels at the attacked measurement location. Together, the two methods motivate a layered protection architecture for next-generation inverter-dominated smart grids.
Emad Abukhousa, Saman A. Zonouz, A. P. S. Meliopoulos· 0 citations
Liquefied natural gas (LNG) submersible centrifugal pumps are critical components in marine energy transportation systems, and fault-induced degradation may threaten operational safety, transfer reliability, and maintenance efficiency. However, condition monitoring and fault diagnosis often rely on heterogeneous multi-source data, where redundant information, unequal channel sensitivity, and inter-signal coupling may obscure discriminative fault features. To address this challenge, this paper proposes a hierarchical multi-source condition monitoring and fault diagnosis framework for LNG submersible centrifugal pumps by integrating an Entropy-Weighted Sensor Selection Method (EWSSM) with a hybrid convolutional neural network (CNN)–Transformer model. Functional information is used for front-end abnormality screening, while selected response signals are used for fault category recognition. EWSSM evaluates channel contribution and suppresses redundant inputs to construct a compact fault-sensitive input space. The CNN–Transformer model combines local feature extraction with global dependency modeling to identify complex fault patterns. A laboratory-scale fault simulation platform was established, and vibration, acoustic, internal pressure-pulsation-related response information, and operating parameter data were collected under ten operating states. Experimental results show that the proposed framework achieves effective abnormality screening and accurate fault diagnosis, with an average classification accuracy of 98.73% over repeated experiments. Covariance-difference analysis further provides interpretable evidence for condition assessment by revealing fault-related multi-source response redistribution. The proposed framework provides an effective, intelligent monitoring and diagnosis solution for LNG submersible centrifugal pumps and supports reliability-oriented operation and maintenance of marine energy transportation equipment.
Zeming Li, Kun Liu, Chongchong Guo et al.· Journal of Marine Science an...· 0 citations
Permanent magnet synchronous motors (PMSMs) are broadly used in diverse applications due to their inherent advantages. Open-circuit faults (OCFs) are among the major fault classifications in PMSMs, posing significant concerns due to their contribution to torque ripples, vibrations, and efficiency degradation. Therefore, accurate and real-time OCF diagnosis is essential for reliable operation and predictive maintenance practices. This underscores the importance of a robust diagnostic framework that enables early fault detection and localization, supports embedded integration, and requires no additional dedicated sensors. However, existing studies rarely address these requirements together. To overcome these limitations, this article proposes a novel OCF diagnostic framework that fuses features derived from multiple strategies, including wavelet energy-based features, frequency-domain features extracted from current waveforms, and speed measurement data. The extracted feature vector is used as input to a lightweight deep neural network. The proposed approach enhances interpretability and enables seamless embedded integration compared to conventional raw-data-driven machine learning models. In addition, an extended refinement layer is incorporated to enable integrated fault detection and classification for OCF while enhancing diagnostic transparency. The effectiveness of the proposed method is demonstrated through MATLAB/Simulink simulations using the PLECS Blockset and further validated in real-time with an RTBox-based hardware-in-the-loop setup using a C2000 launchpad. Furthermore, experimental validation is conducted using a domain-adaptation strategy based on transfer learning. Performance evaluation confirms diagnostic accuracy exceeding 99% across varying operating conditions. The validation process achieves fault detection within 22% of a fundamental electrical cycle, with fault localization occurring within 40% of an average, demonstrating the robustness and adaptability of the proposed method. A sensitivity analysis of the proposed algorithm’s feature vector validates the effectiveness of high-frequency features. Furthermore, the risk distribution matrix provides insights supporting informed maintenance decisions.
Nimesh Jayasena, Battur Batkhishig, B. Nahid-Mobarakeh et al.· IEEE Open Journal of Industr...· 0 citations