Machine Learning based Power Quality Event Classification using Wavelet - Entropy and Basic Statistical Features - Ucar, Ferhat; Alcin, Omer Faruk; Dandil, Besir; Ata, Fikret (Ieee, 2016)
Today's industrial environment is smarter than ever before. Most production lines include electrical devices which are able to communicate each other and controlled from a single station with automation systems. Most of those elements have an internet connection link known as industrial internet. Development of smart technology with industrial internet comes with a need of monitoring. Monitoring technologies are emergent systems that focus on fault detection, grid self - healings and online tracking of power quality issues. Present study deals with one of the essential part of an electricity grid monitoring system called power quality event classification in a manner of machine learning topic. Power quality events to be processed are generated synthetically by means of a comprehensive software tool. Classification of real-like dataset is executed using extreme learning machine which is an extremely fast learning algorithm applied to single layer neural networks. Basic statistical criteria and wavelet - entropy methods are handled to achieve distinctive features of dataset. As a performance evaluation instrument, conventional artificial neural network structure is run too. Detailed results are discussed to prove the satisfactory performance of proposed pattern recognition model.
21st International Conference on Methods and Models in Automation and Robotics (MMAR) -- AUG 29-SEP 01, 2016 -- Miedzyzdroje, POLAND
Keywords: power quality events; smart grid; wavelet transform; extreme learning machine; pattern recognition