Efficient Framework to Manipulate Data Compression and Classification of Power Quality Disturbances for Distributed Power System

Abstract

There is some risk of power quality disturbances at many stages of production, transformation, distribution, and energy consumption. The cornerstone for dealing with power quality problems is the characterization of power quality disturbances (PQDs). However, past research has focused on a narrow topic: noise disruption, overfitting, and training time. A new strategy is suggested to address this problem that combines efficient one-dimensional dataset compression with the convolutional neural network (CNN) classification algorithm. First, three types of compression algorithms: wavelet transform, autoencoder, and CNN, are proposed to be evaluated. According to the IEEE-1159 standard, the synthetic dataset was built with fourteen different PQD types. Furthermore, the PQD classification procedure integrated compressed data with the CNN classification algorithm. Finally, the suggested method demonstrates that combining CNN compression and classification methods can efficiently recognize PQDs. Even in noisy environments, PQD signal processing achieved up to 98.25% accuracy and managed the overfitting.