Dual Tree Complex Wavelet Transform with Multiobjective Optimization Algorithm for Real‐Time Power Quality Events Classification. Issue 10 (6th September 2020)

• Record Type: Journal Article
• Authors: Rahul,
• Is Part Of: Advanced theory and simulations. Volume 3:Issue 10(2020)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2020-09-06
• Subjects: DAG‐SVM -- DTCWT -- NSGA III -- optimal feature selection -- disturbance classification -- power quality (PQ) events; Science -- Simulation methods -- Periodicals; Science -- Methodology -- Periodicals; Engineering -- Simulation methods -- Periodicals; Engineering -- Methodology -- Periodicals; 507.21
• DOI: 10.1002/adts.202000141
• Languages: English
• ISSNs: 2513-0390

Abstract

Abstract: This 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. Abstract : The dual tree complex wavelet transform (DTCWT) based feature extraction technique explored here for analysis of power quality disturbances. The DTCWT is a suitable choice for power quality analysis as the performance doesn't depend on the mother wavelet. The DTCWT is approximately shift invariant and the changes in time shift is updated better compared with the discrete wavelet transform.