Regulated Two-Dimensional Deep Convolutional Neural Network-Based Power Quality Classifier for Microgrid

Abstract

Due to the penetration of renewable energy and load variation in the microgrid, the diagnosis of power quality disturbances (PQD) is important to the operation stability and safety of the microgrid system. Once the power imbalance is present between the generation and the load demand, the fundamental frequency would deviate from the nominal value. As a result, the performance of the power quality classifier based on the neural network would be deteriorated since the deviation of fundamental frequency is not taken into account. In this paper, the regulated two-dimensional (2D) deep convolutional neural network (CNN)-based approach for PQD classification is proposed. In the data preprocessing stage, the IEC-based synchronizer is introduced to detect the deviation of fundamental frequency. In this way, the 2D grayscale image serving as the input of the deep CNN classifier can be accurately regulated. The obtained 2D image can effectively preserve information and waveform characteristics of the PQD signal. The experiment is implemented with datasets containing 14 different categories of PQD. According to this result, it is revealed that the regulated 2D deep CNN can improve the effectiveness of PQD classification in a real-time manner. Furthermore, the proposed method outperforms the methods in previous studies according to the field verification.