Power Quality Fault Identification Method Based on SDP and Convolutional Neural Network

Abstract

The global power demand is increasing year by year. Power quality fault recognition plays an important role in determining the fault type when a fault occurs, so as to maintain power quality and supply stability. Therefore, this paper proposed using the symmetrized dot pattern (SDP) algorithm plus the convolutional neural network (CNN) to study power quality fault recognition. Six common power quality fault models were taken as the subjects of discussion, including normal voltage, voltage sag, voltage swell, voltage interruptions, voltage flickers, and voltage harmonics. First, the voltage variation data of five cycles were extracted from a 60 Hz power supply and introduced into the SDP. Afterwards, the data were converted into graph data, which could be used for fault recognition. Lastly, the power quality fault type was identified by CNN. In this study, 600 random fault data (100 data per fault) were imported into the algorithm, and the recognition rate reached as high as 92.9%. Additionally, SDP could reduce the mass original data. After the subtle changes in the output signals were captured, they could be observed by images. The power quality fault state could thus be accurately recognized by CNN.