Jul 2026· 2026 5th International Conference on Energy and Electrical Power Systems (ICEEPS)· pp. 67-71· 0 citations· 7 references
Abstract
After photovoltaic (PV) generation is connected to a low-voltage distribution area, the residual current becomes more complex, noisier, and more variable in amplitude, which may cause conventional residual-current protection devices to mis-operate or fail to operate. To address this issue, this paper proposes a leakage fault diagnosis method that integrates multidimensional feature extraction, principal component analysis (PCA), and a LightGBM classifier. Time-domain, frequency-domain, entropy, and wavelet-packet time-frequency energy features are extracted from the residual currents of eight feeders, forming 176 original features. PCA is then used to map highly correlated features into an orthogonal space, and 170 principal components are retained as model inputs. Finally, Bayesian optimization is adopted to tune the key hyperparameters of LightGBM. Experimental results show that the proposed PCA-LightGBM method achieves an accuracy of 96.83% and an F1-score of 96.84% on the test set, with a training time of 4.98 s and a single-sample diagnosis time of 0.024 ms, demonstrating high diagnostic accuracy, redundancy resistance, and deployment efficiency.
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
A progressive three-stage time–frequency learning framework that identifies series arc faults directly from normalized current waveforms and provides robust discrimination across unseen measurement sessions within the evaluated load categories and operating conditions is presented.
Seoyoung Jeon, Won-Kyu Choi, Sungsoo Kwon et al.· Italian National Conference...· 0 citations
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.
Ruihao Zhou, Penghui Liu, Wenxiang Li et al.· Processes· 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
Stable operation of photovoltaic (PV) cells is crucial for reliable and efficient electricity generation in power systems. However, their poor defect characterization, large size variation, and high background noise lead to low automatic identification accuracy. In this paper, a YOLO-based network with an efficient feature extraction network, termed EFEM-YOLO, is proposed for accurate defect detection in PV cells. First, a multi-scale dilated convolution feature pyramid module is proposed. By constructing an adaptive feature pyramid via parallel convolutional paths, it enhances the representation of multi-scale defects. Second, the C2f-dynamic gated activation network is introduced. By incorporating a dynamic gated nonlinear activation mechanism and a cross-stage dual-branch feature aggregation strategy, the model’s adaptability to multi-scale defects and recognition accuracy are improved. Finally, a novel SmartShapeIoU loss function is proposed. This mitigates localization bias caused by object scale variations in high-noise environments, thereby improving bounding box regression accuracy. Experimental results demonstrate that the proposed method outperforms state-of-the-art methods in detection accuracy. Additional dataset experiments validate the superior detection accuracy and generalization of the proposed method for small object detection.
Yuan-Song Xiang, Xihong Fei, Tian Fang et al.· Engineering Research Express· 0 citations
The issue of PQ disturbance has now emerged as a serious problem in contemporary electrical power systems owing to the increasing use of non-linear electronic loads. Detecting and classifying PQ disturbances is crucial for maintaining the stability of electrical power systems; nevertheless, most traditional techniques for achieving this objective involve relatively complex computations and rely on extensive features and data sets. A new method of detecting and classifying PQ disturbances using chromatic monitoring along with fuzzy logic classification technique is suggested in this paper. The proposed technique works in the time domain through the constant monitoring of voltage signal and mapping them to the compact chromatic feature space through Hue-Lightness-Saturation (HLS) conversion technique. Parameters such as Hue and Lightness obtained from statistical measures of RGB filters are clear and unique in representing different PQ events. Parameters are then classified using the rules-based fuzzy logic controller without the requirement for large training data sets. Results from simulation show that the proposed technique is highly accurate in classifying PQ disturbances. It performs well with respect to disturbances and offers less computation than previous methods.
Hadeel Al-helalat, Rula Alrawashdeh, Z. Almajali· IEEE Jordan Conference on Ap...· 0 citations
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