基于灰度图像分解的局部放电特征提取与优化

Effective features extraction of partial discharge (PD) is the foundation of defect identification of electrical apparatus. Using PD gray image as the analysis object, a PD image features extraction strategy was proposed based on two-dimensional principal component analysis (2DPCA). Various 1-dimensional (1D) vectors were obtained by implementing 2DPCA on PD gray images in the proposed method. 9 characteristic parameters were extracted from each 1D vector, which constituted the PD image decomposition features. In addition, a PD features selection algorithm was developed based on particle swarm optimization (PSO) algorithm, which attempts to optimize the extracted PD image decomposition features and improve the PD recognition accuracy. The recognition results of PD samples considering the multi-factor influences in laboratory illustrate that the proposed 2DPCA image decomposition features can achieve the high PD recognition accuracy of 93%. Besides, the PSO optimized 2DPCA features can further improve the PD recognition accuracy to 96% and simultaneously reduce the feature dimension from 72 to 28, which fully demonstrates effectiveness of the proposed algorithm. Moreover, the average recognition accuracies of PD samples added with different random noises are all higher than 85%, which indicates that 2DPCA image features possess good tolerance ability of random noises.