Automatic identification and classification of power quality events using a hybrid intelligent approach

Abstract

This article proposes an implementation of Enhanced-Non-dominated Sorting Genetic Algorithm (E-NSGA III) and Directed Acyclic Graph-Support Vector Machine (DAG-SVM) based combined approach for recognition and classification of power quality events. The function of E-NSGA III is to extract features and DAG-SVM for the purpose of classification of power quality events with minimum error. The non-stationary and non- linear nature of power quality disturbances makes it a suitable choice for E-NSGA III. This technique gives unique Pareto-optimal solutions based on multi-objective optimization. Considering equal priority for all the objectives, a fitness function is used to obtain the best solution set from the first Pareto front. Non-dominated sorting sets a dominated count and assigns domination to each individual. Dominated count means the number of times an individual data point has been dominated by another data point. The obtained unique feature vectors are used for training of DAG-SVM classifier to classify the power quality disturbances. Many power quality events including harmonics, swell, sag, transient, swell with harmonics and sag with harmonics are taken into consideration for investigation of power signal disturbances. Comparative investigation reveals the novelty and efficacy of the proposed hybrid technique in comparison of support vector machine, radial basis feed-forward neural network, probabilistic neural network, support vector machine and type-1 and type-II based fuzzy classifier under serve noise conditions. The simulation results show that the Directed Acyclic Graph Support Vector Machine gives the best classification accuracy of 99.20% and less computation time.