Dual Tree Complex Wavelet Transform with Multiobjective Optimization Algorithm for Real‐Time Power Quality Events Classification

Abstract

AbstractThis article proposes an application of Dual Tree Complex Wavelet Transform (DTCWT) with non‐dominated sorting genetic algorithm III (NSGA III) based multi‐objective optimization and directed acyclic graph support vector machine (DAG‐SVM) for recognition and classification of power quality events. The proposed method first employs DTCWT for extraction of features then NSGA III algorithm for an effective optimization of extracted features. After that, DAG‐SVM based classifier are used to predict the classes of PQ disturbances. The NSGA III algorithm generates optimalsolutions based on multi objective optimization and then fitness function is generated with the help of Pareto front to obtain unique features set from power signals. The NSGA III not only optimizes selected features with DTCWT but also reduces the computational time in comparison with the traditional NSGA II. The obtained unique feature vectors are used for training of Directed Acyclic Graph‐SVM to classify the power quality disturbances. The short event detection, lesser computational timing, superior classification accuracy, and high anti‐noise performance are the main advantages of the proposed method.