Research group

Ayman El-Hag received his B.S. and M.S. degree from King Fahd University of Petroleum and Minerals and his PhD from the University of Waterloo in 1993, 1998 and 2003 respectively. He joined the Saudi Transformer Co. as a Quality Control Engineer from 1993 till 1999. From January till June 2004, Dr. El Hag worked as a Postdoctoral fellow at the University of Waterloo then he joined the University of Toronto as an NSERC Postdoctoral fellow from July 2004 till July 2006. In 2006, Dr. El-Hag joined the electrical engineering department at the American University of Sharjah. He was promoted to associate and then to professor in 2011 and 2016 respectively. Currently, he is a lecturer in the electrical and computer engineering department at the University of Waterloo. Dr. El-Hag current main areas of interest are condition monitoring and diagnostics of electrical insulation and application of machine learning in power engineering. Dr. El-Hag was the middle east editor for the IEEE Insulation magazine from 2016-2018. Currently, he is an associate editor for the IEEE Transactions on Dielectric and Electrical Insulation Transaction and a member of the IEEE outdoor insulation committee.

Outdoor polymeric insulators are crucial components of the overhead power distribution and transmission network. However, leakage current (LC) develops on polymeric insulators, particularly in areas with severe contamination, which may lead to deep erosion from a severe (eroding) dry-band arcing (DBA). The erosion exposes the fiberglass core to weathering, leading to insulator failure. Silicone rubber (SR) is a commonly used insulating material for outdoor applications and has shown an outstanding erosion performance during the inclined plane test (IPT) under mild test voltages. However, more severe (critical) voltages are required to test strong materials that are highly loaded with inorganic fillers and in use today. Critical test voltages have been shown leading to material puncture or deep erosion with combustion under the eroding DBA. Current methods, such as analyzing LC waveforms using third harmonic and wavelet transform techniques, have demonstrated effectiveness in detecting DBA during IPT and correlating LC with erosion taking place gradually on the surface. However, the deep erosion rather takes place over much shorter periods of time under the eroding DBA, necessitating a reliable supporting technique to detect the phenomenon. Additionally, identifying material failure based on visual observations would also not be feasible during the IPT, as these techniques may not clearly identify deep erosion in the presence of liquid contaminant, continuous DBA activity, and surface residue. There is a critical need for an online and a non-intrusive method of identifying deep erosion failure during the IPT. Therefore, this research proposes the use of ultra-high frequency (UHF) detection technique of deep erosion failure during the IPT. The proposed method has the potential to be implemented as a reliable online and non-intrusive technique to identify deep erosion failure during the Inclined Plane tests.

The utilization of ceramic insulators in overhead lines has been a prevalent practice for more than a century. A significant portion of these insulators have either approached or exceeded their intended lifetime, posing a risk of sudden failure. Therefore, there is a growing need for reliable, fast, and cost-effective condition monitoring systems. Defective insulators can experience partial discharge (PD) and dry band arcing (DBA), which release detectable radiation in the form of heat, electromagnetic waves and ultrasonic emissions. This research introduces a non-contact method for assessing ceramic insulators using vision- and radiation-based sensors. It employs deep learning algorithms to classify and quantify defects from images and acoustic emissions.