An Advanced Genetic Algorithm with Improved Support Vector Machine for Multi-Class Classification of Real Power Quality Events

This article proposes an application of non-dominated sorting genetic algorithm III (NSGA III) and directed acyclic graph support vector machine (DAG-SVM) based combined approach for recognition and classification of power quality disturbances. Power disturbances are non-stationary and non-linear by nature and require a large number of features vectors for detection, which results in high computation time and error. The proposed method first employs NSGA III algorithm for an effective features extraction from power signals. The numbers of features required for detection are less in case of proposed NSGA III algorithm. The NSGA III algorithm generates optimal solutions 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 has exact localization of power quality events and stronger robustness but also reduces the computational time in comparison with the traditional NSGA II. The obtained unique feature vectors are used for training of DAG-SVM to classify the power quality disturbances. In case of N-classes, the proposed DAG-SVM generates N(N-1)/2 classifiers, one for each pair of classes and makes the decision accurate and faster. The short event detection, lesser computational timing, superior classification accuracy, and high anti-noise performance are the main advantages of the proposed method. Furthermore, Virtex-5 FPGA based processor is used to test and validate the feasibility of the proposed method for real time analysis of power quality events.

Automated summary

This article proposes a combined approach using NSGA III and DAG-SVM for recognition and classification of power quality disturbances. NSGA III algorithm effectively extracts features for detection, reduces the number of required features, and generates optimal solutions based on multi-objective optimization. DAG-SVM is used for training, generating multiple classifiers for improved classification accuracy and reduced computation time.