Accurate classification of power quality disturbance based on 3D visualized spiral curve and hybrid ER-MVCNN model

Abstract

This work proposes a new method for accurate power quality disturbance (PQD) classification. Firstly, 3D-visualized spiral curve (3D-VSC) is innovatively proposed to convert 1D PQD signal into 3D spiral curve. It can display time-frequency information in multi-dimensional perspectives and the disturbance characteristics more intuitively in image visual sense, which can realize PQD features enhancement. Secondly, efficient channel attention-ResNet multi-view convolution neural network (ER-MVCNN) hybrid model is creatively proposed by introducing ECA-ResNet as feature extraction module. It can solve limitations of multi-view convolution neural network (MVCNN) in single-view feature extraction and defects of multi-view feature fusion in the weighting problem, which can effectively achieve classification accuracy to 99.68 %. Comparison experiments with MVCNN, AlexNet-MVCNN, VGGNet-MVCNN, ResNet18-MVCNN, and SENet-ResNet-MVCNN show that ER-MVCNN has higher accuracy by 12.4 %, 6.79 %, 6.14 %, 3.86 %, and 0.88 % respectively. Finally, through establishing PQD experiment platform and IEEE PES datasets to verify high accuracy and robustness of proposed method.