Abstract
In this paper, we propose a methodology to classify Power Quality (PQ) in distribution systems based on voltage sags. The methodology uses the KDD process (Knowledge Discovery in Databases) in order to establish a quality level to be printed in labels. The methodology was applied to feeders on a substation located in Curitiba, Parana, Brazil, considering attributes such as sag length (remnant voltage), duration and frequency (number of occurrences on a given period of time). On the Data Mining Stage (the main stage on KDD Process), three different techniques were used, in a comparative way, for pattern recognition, in order to achieve the quality classification for the feeders: Artificial Neural Networks (ANN); Support Vector Machines (SVM) and Genetic Algorithms (GA). By printing a label with quality level information, utilities companies (power concessionaires) can get better organized for mitigation procedures by establishing clear targets. Moreover, the same way costumers already receive information regarding PQ based on interruptions, they will also be able to receive information based on voltage sags.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: 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.
