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Neural Networks for Wind Turbine Fault Detection via Current Signature Analysis

Raed Ibrahim Loughborugh University, United Kingdom NEURAL NETWORKS FOR WIND TURBINE FAULT DETECTION VIA CURRENT SIGNATURE ANALYSIS Abstract ID: 645  Poster code: PO.078c | Download poster: PDF file (0.23 MB) | Download full paper: PDF (0.41 MB)

Presenter's biography

Biographies are supplied directly by presenters at WindEurope 2016 and are published here unedited

Mr. Raed has been working in Power System Industry including protection, control and monitoring system for 5 years. He is currently a PhD student at the Centre for Renewable Energy Systems Technology (CREST), Loughborough University, UK. He received the B.Sc. degree in electronics and electrical engineering from the University of Tikrit, Iraq, in 2009, and the M.Sc. degree in electrical power engineering from the University of Mosul, Iraq, in 2011. His research is focused on the development of wind turbine electrical and mechanical fault detection algorithms by using advanced signal processing and machine learning.

Abstract

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