A Novel Convolutional LSTM Network Based on the Enhanced Feature Extraction for the Transmission Line Fault Diagnosis

Abstract

Recently, the traditional transmission line fault diagnosis approaches cannot handle the variables’ dynamic coupling properties, and they also ignore the local structure feature information during the feature extraction. To figure out these issues, a novel enhanced feature extraction based convolutional LSTM (ECLSTM) approach is developed to diagnose the transmission line fault in this paper. Our work has three main contributions: (1) To tackle the dynamic coupling characteristics of the process variables, the statistics analysis (SA) method is first employed to calculate different statistical features of the transmission line’s original data, where the original datasets are transformed into the subsequently used statistics datasets; (2) The statistics comprehensive feature preserving (SCFP) is then proposed to maintain both the global and local structure features of the constructed statistics datasets, where the locality structure preserving technique is incorporated into the principal component analysis (PCA) model to extract the features from the statistics datasets; (3) To effectively diagnose the transmission line’s fault, the SCFP based convolutional LSTM fault diagnosis scheme is constructed to classify the global and local statistical structure features of fault snapshot dataset, because of its ability to exploit the temporal dependencies and spatial correlations of the extracted statistical features. Detailed experiments and comparisons on the datasets of the simulated power system are performed to prove the excellent performance of the ECLSTM based fault diagnosis scheme.