TL;DR
A two-stage WT-PSO-RBFNN framework in which adaptive db4 level-3 wavelet thresholding performs fault detection and a PSO-optimized radial basis function neural network performs phase/ground classification using normalized wavelet-domain features is proposed.
The proposed framework employs ANN as a nonlinear feature embedding and a Radial Basis Function SVM subsequently classifies using an Error-Correcting Output Codes (ECOC) strategy, validating the effectiveness of the proposed hybridisation strategy for intelligent transmission system protection.
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 inc...
Simulation results in PSCAD/EMTDC demonstrate that the proposed method significantly outperforms traditional differential protection in sensitivity and reliability, effectively mitigating maloperation risks under highimpedance faults and weak-infeed conditions, offering a viable software upgrade path for existing infra...
A TCN-Transformer algorithm that integrates local temporal feature extraction and global attention mechanism is proposed, and an end-to-end classification prediction framework is constructed, effectively improving the accuracy and stability of transmission line fault identification in complex scenarios, and providing a...
Currently, insulation faults in the DC system of photovoltaic (PV) power stations are handled by a full shutdown strategy of inverters, and fault branch localization relies on manual inspection, resulting in low efficiency and poor accuracy, leading to prolonged unplanned outages and substantial power generation losses...
A deep learning-based intelligent fault identification method that employs a deep neural network to learn fault characteristics directly from transmission line monitoring data, thereby reducing dependence on manual feature extraction and achieving better identification accuracy, stability, and generalization performanc...
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