Large-scale integration of high-proportion new energy sources and continuous expansion of network scale complicate the transient characteristics of distribution networks. Conventional fault diagnosis methods suffer from insufficient feature extraction and weak capture of topological correlation, which degrade diagnosis accuracy. To tackle this issue, this paper proposes a complex fault diagnosis strategy for distribution networks based on analysis of the dynamic variation law of zero-sequence current. First, multivariate variational mode decomposition (MVMD) is adopted to process zero-sequence current signals, which effectively fuses multi-dimensional zero-sequence current data and fully excavates fault features. Moreover, the zebra optimization algorithm is utilized to optimize the parameters of MVMD for further improving feature extraction performance. Subsequently, a graph convolutional neural network is employed to extract temporal features from the processed waveforms, enhancing the model’s recognition capability under high-resistance faults and typical disturbance conditions. Finally, multiple IEEE test systems are used for verification, which demonstrates the effectiveness and feasibility of the proposed method.
The measurement accuracy issue of zero-sequence current transformers (CTs) has long been a critical factor restricting the accuracy of fault line selection in distribution networks. Although existing research methods are relatively mature in theory, their on-site application is limited by the measurement precision of zero-sequence CTs. To address this problem, this paper proposes a fault line selection strategy for distribution networks based on dynamic and accurate measurement of zero-sequence current. Firstly, from the data perspective, this paper analyzes the fault characteristics of various electrical quantities in different operation stages of distribution networks. Combined with system characteristics, an accurate measurement method for zero-sequence current amplitude is subsequently put forward. Afterwards, a distribution network fault line selection algorithm optimized by an attention mechanism-based multi-scale convolutional neural network is constructed. Finally, verification results based on the IEEE standard test system demonstrate that the proposed method enhances the capabilities of feature extraction and side information aggregation, realizes efficient and accurate localization of faulty lines, and exhibits strong robustness under noisy conditions.
Ruihao Zhou, Penghui Liu, Wenxiang Li et al.· Processes· 0 citations
To address the issues of insufficient feature extraction and low localization accuracy in distribution network fault diagnosis, this study proposes a fault classification and localization method based on APC-SVM and PC-AZOA. The model performs a simultaneous decomposition of three-phase signals using multivariate variational modal decomposition and employs the energy entropy of each model component as the feature vector; During the classification stage, the method integrates electrical and physical constraints, introducing three-phase energy imbalance and variance into the support vector machine ’ s parameter optimization process for the first time to dynamically adjust the penalty factor and kernel parameters; finally, a traveling wave propagation time error model is constructed, and an adaptive zebra optimization algorithm constrained by physical information is proposed. By innovatively embedding prior physical knowledge into the search space constraints, the method effectively suppresses invalid searches and improves convergence efficiency. Experimental results show that the model achieves a classification accuracy of up to 98.4% with a positioning error below 1%, demonstrating both high precision and high efficiency.
Dahua Li, Xinrui Yang, Yu Song et al.· 2026 IEEE International Conf...· 0 citations
The proposed MSFormer incorporates a parallel multi-scale Convolutional Neural Network architecture and hierarchical Transformer modules to comprehensively process 1D vibration signals to provide a powerful and precise intelligent solution for mechanical fault diagnosis.
Shu Guo, Jin Li, Tian-Ci Zhang· Machines· 0 citations
To address the challenges of large-scale fault diagnosis in traction drive systems, such as complex fault categories, strong background noise, insufficient multi-source information fusion, and difficulty in modeling dynamic sensor correlations, this article proposes a multisource fault diagnosis method based on a temporal-spatial joint perception graph convolutional network (GCN). The main research idea is to construct a unified diagnostic framework that integrates signal enhancement, time-frequency representation, global multisensor dependency modeling, and adaptive spatial graph learning. First, singular value decomposition is introduced as a front-end denoising strategy to suppress background interference while preserving fault-related dominant frequency and impulse components. Then, continuous wavelet transform is employed to convert the denoised multi-sensor vibration signals into time-frequency representations, thereby enhancing the expression of non-stationary fault features. On this basis, an Inception V3-like multi-scale feature extraction module is designed to capture fault-sensitive information from different frequency bands. A Transformer module is further introduced to model global contextual dependencies among multi-source sensor feature tokens, while a GCN is used to learn spatial relationships between sensors. Different from conventional static graph-based diagnostic models, the proposed method adaptively updates the adjacency matrix according to the learned Temporal-Spatial feature relationships, enabling dynamic perception of sensor coupling under different fault states and operating conditions. Experimental validation on the Beijing Jiaotong University–Rail Autonomous Operations (BJTU-RAO) bogie dataset demonstrates that the proposed method achieves high diagnostic accuracy and stability in a 51-class large-scale fault diagnosis task, outperforming traditional static GCN networks and several existing fault diagnosis methods. The results indicate that the proposed framework provides an effective solution for multisource, large-scale, and complex fault diagnosis of traction drive systems.
Zhihui Men, Dao Gong, Weiguang Sun et al.· Structural Health Monitoring· 0 citations
Motor drive systems operating in embedded environments are frequently affected by noise, dynamic loading conditions, and electromagnetic interference, making timely fault diagnosis difficult. To improve diagnostic accuracy and real-time performance, this study proposes an intelligent fault diagnosis framework based on embedded multi-source data acquisition and feature fusion. High-precision sensors are employed to synchronously collect vibration and current signals, while improved wavelet packet decomposition and principal component analysis are combined to extract discriminative multi-dimensional fault features and eliminate redundant information. A lightweight convolutional neural network optimized for embedded deployment is then developed to perform low-latency fault classification and edge inference. Experimental results show that the proposed method achieves an average F1-score exceeding 97% under complex mixed-fault conditions, while maintaining an average detection latency of approximately 45 ms. The proposed framework demonstrates strong robustness and computational efficiency, providing practical support for intelligent industrial maintenance and offering reference solutions for embedded sensing, signal processing, and electromagnetic compatibility environments.
The results indicate that phase-angle information provides supplementary and class dependent discriminative value, but does not consistently improve all fault classes, whereas conventional voltage and current measurements alone represent a simpler and more stable alternative, whereas phase-angle measurements may be incorporated when synchronized phasor information is already available.
Zeynep Bala Duranay, İsmail Anıl Avcı, Mohammed Bushra Mohammed et al.· Symmetry· 0 citations
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