Deep Meta-Learning With 1D-CNNs for Surface Deterioration Recognition of Overhead Conductors of Electricity Grid

The surface deterioration process of aluminum-stranded conductors in high-voltage alternating current (ac) electricity grids is naturally gradual, dynamic, and slow, which will greatly influence the remaining life of conductors. An effective method for deterioration recognition of conductors is to design an evaluation model with energy-dispersive X-ray spectroscopy (EDS). The small number of labeled samples is commonly available for the deterioration recognition task. The conventionally successful deep learning (DL)-based applications are dependent on large-scale data to train an effective model, which restricts the application of DL in the aging recognition scenario. A deep meta-learning (DML) model with 1-D-convolutional neural networks (1D-CNNs) is proposed for the recognition of deteriorated conductors based on EDS, which learns the representation of each support class by the embedding function. Then, it performs classification tasks by comparing the Euclidean distance between the query sample and this representation. A comprehensive systematic comparison with nonneural network methods, conventional DL, and deep transfer learning shows that the proposed model substantially improves the classification accuracy of aging conductors.