<p>Power Quality has become one of the important issues in modern smart grid environment. Smart grid generally utilizes computational intelligence method from the generation of electricity to electricity distribution to the customers. This is done for the safety, reliability, tenacity and efficiency of the system. The classification of power disturbances has become a major topic in maintaining power quality. These disturbances occur due to faults, natural causes, load switching, energizing transformer, starting large motor, as well as utilization of power electronic devices. The key issue is about maintaining the continuous supply of electricity to the end-users without any problem. If a problem occurs, it might increase the production cost significantly especially to large-scale industries. In this paper, S-transform is used to extract distinctive features of real data from transmission system, and Support Vector Machine was utilized to classify four types PQ disturbances namely, voltage sag, interruption, transient and normal voltage. Results obtained indicate that performance of the One Against One classifier produces high accuracy using k-fold cross validation and RBF kernel.</p>
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.