Adaptive Traveling Wave Protection Strategy for Distribution Network Based on SVMD and Temporal Homological Class Flux Mapping

A Fault Diagnosis and Localization Method for Distribution Networks Based on APC-SVM and PC-AZOA

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.

A computational intelligence approach for reliable fault protection of underground transmission networks

An Adaptive Protection Method for Low-Voltage Distribution Networks Integrating Mechanism-Guided and Cost-Sensitive Learning

In low-voltage distribution networks, load switching, induction motor start-up, photovoltaic output variations, and short-circuit faults may produce highly overlapping electrical characteristics, which can lead to maloperation or failure to operate in conventional protection. To address this problem, this paper proposes an adaptive protection method integrating physically guided and cost-sensitive learning. First, an incremental topology-constraint deviation and a voltage-current trajectory curvature are constructed based on the fault-superimposed network constraint and the variation characteristics of system equivalent impedance, enabling the discrimination of short-circuit faults from non-fault transient disturbances. Then, a cost-sensitive physically guided extreme gradient boosting (XGBoost) model is developed, in which a fault-current-increment-based weight is introduced into the objective function to enhance the learning capability for weak-fault samples. Furthermore, a temporal-consistency-based protection operation logic is designed using sliding-window confirmation and majority voting to suppress isolated abnormal predictions. Simulation and RTDS-based real-time validation results on a 0.4-kV low-voltage distribution network with distributed photovoltaic generation show that the proposed method improves weak-fault detection sensitivity and reduces maloperation under complex source–load disturbances. The method relies only on local measurements and has potential for deployment in low-voltage intelligent protection terminals.

A Novel Fault Location Method for MMC-HVDC Grid Based on Gram Angle Difference Field

When a short-circuit fault occurs along the transmission line of a modular multilevel converter high-voltage direct-current (MMC-HVDC) grid, the sub-module capacitors discharge, causing the fault current to rapidly rise, posing a threat to the safe operation of the system. Therefore, this paper proposes a novel fault location method based on the Gram Angle Difference Field (GADF). The column corresponding to the maximum differential value in a sliding window is used to identify the fault moment and locate faults in MMC-HVDC transmission lines. In order to effectively distinguish normal fluctuations from fault mutations and avoid false alarms, a dynamic threshold is set based on the statistical characteristics of normal data. This method utilizes the unique feature extraction capability of the GADF matrix, the adaptive mechanism of the dynamic threshold, and the stability of line-mode voltage to achieve fast and accurate fault location. Finally, this method is validated using a simulation model. The results show that the proposed method can accurately locate faults in different conditions.

A Novel Fault Location Method for Distribution Networks Based on Velocity Difference Between Zero-Mode and Aerial-Mode Traveling Waves

To address the large location errors of traditional traveling-wave methods in distribution networks, this paper proposes a fault location method based on traveling-wave velocity differences between zero-mode and aerial-mode components. First, phase-mode transformation is performed on the acquired zero- sequence voltage signals to extract aerial-mode and zero-mode components. Second, use wavelet transform modulus maxima to identify the initial fault wavefront. Third, the traveling-wave velocity difference is utilized for fault location. Moreover, distance estimation results from both terminals are fused to pinpoint the final fault position. Simulation results demonstrate that the proposed method is immune to transition resistance, fault initial phase angle, and branch line disturbances, and achieves superior fault location accuracy.

Adaptive Spectral Partitioning and Gabor-GRU for Robust Fault Diagnosis of the Multifunction Vehicle Bus

Reliable fault diagnosis of the Multifunction Vehicle Bus (MVB) is essential for ensuring railway operational safety. Spectral analysis shows that MVB signal magnitude is unevenly distributed and mainly concentrated below 10 MHz, suggesting that conventional uniform spectral partitioning may not align well with this characteristic. To better exploit the non-uniform spectral characteristics of MVB signals, we propose a fault diagnosis framework centered on adaptive spectral partitioning and complemented by temporal dependency modeling. Specifically, a Cumulative Magnitude Partitioning Gabor (CMP-G) method is developed to adaptively partition the spectrum into multiple bands based on the cumulative spectral magnitude distribution, on the basis of which a Multi-band Attention Gabor-GRU (MBAGGRU) model is constructed to perform band weighting and capture temporal dependencies. On a test set from an experimentally acquired dataset containing nine simulated MVB physical-layer states, the proposed method achieves 99.80% classification accuracy. To further evaluate robustness, the method is tested on three independently acquired test sets from separate acquisition sessions and under five interference types, including random single-tone frequency-domain interference, bounded additive time-domain noise, additive white Gaussian noise (AWGN), burst-transient interference, and fractional ( $1/f^{\beta } $ ) noise. Across these test scenarios, the proposed method achieves competitive diagnostic performance compared with the STFT-based and wavelet-based baselines. In addition, controlled comparisons with multiple spectral partitioning schemes and alternative backbone classifiers further validate the design. The competitive performance maintained across all tested interference conditions indicates that the proposed framework is effective for MVB physical-layer fault diagnosis and shows potential for Prognostics and Health Management (PHM) in railway systems.