Ali Hasan

Ali Hasan

Assistant Teaching Professor
Engineering Leadership and Society

Ali Hasan

Assistant Teaching Professor
Engineering Leadership and Society

Biography

Ali Hasan, PhD, joins the engineering, leadership and society faculty as an assistant teaching professor. Hasan earned his PhD from the University of Johannesburg and brings experience teaching courses such as Renewable Energy Systems, Control Systems, Electrical Power Systems, Electrical Circuit Fundamentals, and Applied Programming. His teaching philosophy emphasizes blending theoretical knowledge with practical applications, preparing students for real-world engineering challenges. Hasan's approach aims to equip students with both the technical skills and problem-solving abilities needed in the field.

His research specializes in applying Artificial Intelligence to energy and renewable energy, power systems, and communication systems. Dr. Hasan authored/coauthored more than 80 papers published in prestigious international journals and conference.

Areas of Study

Select Publications

  • Mpho Sam Nkambule, Ali N Hasan, Thokozani Shongwe, “Advanced Control Strategies for Photovoltaic Power Quality and Maximum Power Point Tracking Optimization”, IEEE ACCESS, May 2024.
  • Katleho Masita, Ali N Hasan, Thokozani Shongwe,” Defects Detection on 110 MW AC Wind Farm’s Turbine Generator Blades Using Drone-Based Laser and RGB Images with Res-CNN3 Detector”, Applied Sciences MDPI, Appl. Sci. 2023, 13(24), 13046; https://doi.org/10.3390/app132413046, December 2023.
  • Katleho L Masita, Ali N Hasan, Thokozani Shongwe, “75MW AC PV module field anomaly detection using Drone-based IR Orthogonal images with Res-CNN3 detector", IEEE ACCESS journal, Vol 10, ISSN 2169-3536, July 2022.
  • Ndamulelo Tshivhase, Ali N. Hasan, Thokozani Shongwe, “An Average Voltage Approach to Control Energy Storage Device and Tap Changing Transformers Under High Distributed Generation”, IEEE Access, Vol 9, PP(99):1-1 , July 2021.
  • Sibonelo Motepe, Ali N. Hasan, Riaan Stopforth, “Improving Load Forecasting Process for a Power Distribution Network Using Hybrid AI and Deep Learning Algorithms”, IEEE Access, Vol 7, Issue 1, pp. 82584-82598, June 2019.