A novel amalgamation of pre-processing technique and CNN model for accurate classification of power quality disturbances

This work presents an innovative framework that combines the recurrence plots (RP) method with ResNet-50 (a convolutional neural network (CNN)) to autonomously extract relevant features for classifying multiple power quality disturbances for a power signal using the support vector machine. The ResNet-50 is employed to extract the most discriminated features from the two-dimensional images obtained from 1-D signals using the RP method. The work investigates synthetic power quality disturbances, including nine single disturbances, eight double disturbances, and seven triple disturbances. The validation of the proposed framework is conducted on the Standard IEEE 5-bus system under diverse fault scenarios, demonstrating the method's efficiency and reliability. The statistical stability of the proposed framework is assessed using two non-parametric tests, the Sign test and the Wilcoxon test, to ensure the reliability of the results. Furthermore, the proposed work is compared with other advanced CNN models and pre-processing techniques, highlighting its superior performance and effectiveness.