In September 2023, Professor Mladen Kezunovic delivered an invited lecture on “Automated Fault and Disturbance Analysis” as part of the IEEE Distinguished Lecturer Program hosted by the IEEE Victoria Chapter in Melbourne. The lecture focused on leveraging automation, AI, and machine learning to enhance fault detection and disturbance analysis in power systems, improving grid reliability and minimizing operational disruptions. This transformative approach supports smarter, more resilient energy infrastructure.

“Automated Fault and Disturbance Analysis,” Invited Lecture, IEEE Distinguished Lecturer
Program, IEEE Victoria Chapter, Melbourne, Australia, Sept. 2023

“Automated Fault and Disturbance Analysis,” Invited Lecture, IEEE Distinguished Lecturer
Program, IEEE New Zealand North Chapter, Auckland, New Zealand, Sept. 2023

In September 2023, as part of the IEEE Distinguished Lecturer Program, Professor Mladen Kezunovic delivered an insightful lecture on “Automated Fault and Disturbance Analysis” hosted by the IEEE New Zealand North Chapter in Auckland. The lecture highlighted the critical role of automation in detecting and analyzing faults and disturbances in power systems. By leveraging advanced technologies like AI and machine learning, automated systems enable faster, more accurate fault detection, improving grid reliability and minimizing downtime. This approach not only enhances operational efficiency but also supports the transition to smarter and more resilient energy systems worldwide.

“Big Data Analytics for Predicting Risk of Outages, and Managing and Mitigating Impacts using Distributed Energy Resources,” Keynote, IEEE IAS Global Conference on Renewable Energy and Hydrogen Technologies-GlobConHT-2023, Male, Maldives, March 2023

At the IEEE IAS Global Conference on Renewable Energy and Hydrogen Technologies (GlobConHT-2023) in the Maldives, a keynote presentation showcased how Big Data Analytics is revolutionizing the energy sector by predicting outage risks and optimizing the management of Distributed Energy Resources (DERs). By processing vast amounts of data and deploying machine learning models, this approach identifies vulnerabilities in energy grids, enabling proactive risk management and ensuring dynamic coordination of DERs. This results in enhanced grid reliability, optimized use of renewable resources, and cost savings for both utility providers and consumers. The solution also supports sustainability by reducing reliance on fossil fuels and improving energy access in underserved areas, demonstrating the transformative potential of data-driven strategies for a sustainable and resilient energy future