Classification of power quality disturbances using visual attention mechanism and feed-forward neural network

The power quality disturbances caused by large-scale grid connection of nonlinear loads and distributed generations seriously affect the safe and stable operation of precision computers and microprocessors in the power grid, and may cause serious security accidents and economic losses in some cases. Therefore, the accurate classification of power quality disturbances is of great significance for the power supply quality improvement, the power equipment condition monitoring, and the troubleshooting of power grid. For this reason, a novel method based on visual attention mechanism and feed-forward neural network is proposed to classify single and combined power quality disturbances caused by non-balanced, nonlinear loads and distributed generations in the power grid. In the first step of the proposed method, visual attention mechanism is utilized to extract the disturbance features of power quality disturbances, through performing disturbance region selection, multi-scale spatial rarity analysis, and disturbance feature fusion on the binary image converted from the original voltage signal successively. Then, four disturbance feature indexes are selected for the characterization of power quality disturbances. Finally, a classifier using feed-forward neural network is constructed to distinguish various single and combined power quality disturbances. The classification accuracy of the proposed method is compared with that of several existing methods for the classification of power quality disturbances from two types of datasources. The power quality disturbances from the simulation operating conditions include eight kinds of single and thirty-eight kinds of combined power quality disturbances. The power quality disturbances from the IEEE Work Group P1159.3 and P1159.2 Datasets include seven kinds of single and eleven kinds of combined power quality disturbances. Comparison results demonstrate that the proposed method can classify single and combined power quality disturbances more accurate than the compared classification methods, which verifies the effectiveness of the proposed method.