Date and time: 25.04.2019 14:15-15:00

Place: C121

Slides can be found here (TBA)

Speaker: Ferhat Ucar

Title: Extreme Learning approach for Power Quality Event Classification
Abstract: Monitoring Power Quality Events (PQE) is a crucial task for sustainable and resilient smart grids. This paper proposes a fast and accurate algorithm for monitoring PQEs from a pattern recognition perspective. The proposed method consists of two stages: feature extraction (FE) and decision-making. This seminar focuses on utilizing a histogram based method that can detect the majority of PQE classes while combining it with a Discrete Wavelet Transform (DWT) based technique that uses a multi-resolution analysis to boost its performance. Then, the proposed Extreme Learning Machine (ELM) is used to classify the Power Quality measurement data.
Bio: Dr. Ferhat UÇAR received his Bachelor of Technical Education (electricity) from University of Firat, Elazig, Turkiye, in 2006 (graduated with a first). He worked for the industry as a project engineer in process automation and energy area with international companies for four years. His M. Tech degree in active power filters topic from the University of Firat in 2012. He has completed his Ph.D. at the Electrical and Electronics Engineering, Firat University and currently works as a research assistant with the Energy Systems Engineering Department of Faculty of Technology, University of Firat, Elazig, Turkiye. His areas of research include power quality, intelligent systems and smart grid technologies (basics).