Abstract
Recognition of the presence of any power disturbance and classifying any existing disturbance into a particular type is the first step in combating the power quality problem. In spite of the extensive number of power disturbances classification methods, a research on the selection of useful features from the existing feature set and the parameter optimization for specific classifiers was omitted. The kernel parameters setting for support vector machine (SVM) classifier in training process along with feature selection will significantly impact the classification accuracy. Two novel wrapper based hybrid soft computing techniques are proposed in this paper for feature selection and parameters optimization to classify nine types of power disturbances without degrading the SVM classification accuracy. The feature items were selected from discrete wavelet transform across several decomposition levels of the disturbance signals and from the duration of disturbance occurrence. This analysis selects the more useful feature set and optimized parameters for two types of kernels namely the polynomial kernel and radial basis function kernel for SVM. Compared with the traditional grid algorithm the proposed genetic algorithm and simulated annealing based approach significantly improves the classification accuracy rate by eliminating relatively useless feature items and proper parameter selection for the classifier.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.