Transmission Line Fault Type Identification Based on Polar Lights Optimizer-Selected Features and a Gramian Angular Field Attention Fusion Network

To address class imbalance in transmission line fault traveling-wave samples, the strong non-stationarity of transient traveling-wave features, and the limited identification capability of single-representation methods, this paper proposes a fault type identification method that integrates Polar Lights Optimizer (PLO)-based feature selection with a Gramian Angular Field (GAF) attention fusion network. First, the Borderline Synthetic Minority Over-sampling Technique (Borderline-SMOTE) is applied to balance six fault categories in the training set, and time domain, frequency domain, time–frequency domain, and waveform-edge features are extracted from traveling-wave signals acquired by online monitoring devices. Then, PLO is used to select key explicit features, while the preprocessed traveling-wave sequences are encoded into dual-branch images using the Gramian Angular Summation Field (GASF) and the Gramian Angular Difference Field (GADF). Finally, a Gramian Angular Field–Parallel Convolutional Neural Network–Attention (GAF-PCNN-AT) model is constructed to fuse deep image features with selected explicit features for fault identification. Validation on the independent real test set under a representative stratified 8:2 split shows that the proposed method achieves an accuracy of 95.40% and an average area under the curve (AUC) of 0.9900 in the six-class fault identification task. The results indicate that the proposed method can effectively integrate deep image features of traveling-wave signals with PLO-selected explicit features, thereby providing high identification accuracy and good overall classification performance.