ABSTRACT. Recent advancement of renewable energy technology eables transforming centralized bulk power systems with large-scale power generation connected to transmission networks to decentralized systems with numerous small-scale distributed generation (DG) units connected to distribution networks. As an essential part of the future energy mix, renewable DGs creates new challenges in planning, operation, and control of tomorrow’s active distribution networks. A resilient power distribution network can reduce length and impact of power outages, maintain continuous services, and improve reliability. One effective way to enhance the system’s resilience is to form microgrids during power outages. This talk focuses on a novel dynamic microgrid formation technique-based service restoration method using deep reinforcement learning in renewable-rich distribution systems.

ABSTRACT. To warrant the comprehensive impregnation of substantially viscous thermoplastics into the carbon reinforcement, hybrid yarn production techniques of friction (DREF-II) spinning and electrostatic spray coating were used. 2D and 3D (angle inter-lock and orthogonal weave) woven fabrics were developed from the produced hybrid yarns, which were later consolidated to 2D and 3D composites. In the tensile tests and flexural tests, 2D composites were observed to be better than the 3D composites. However, superior notch impact properties were observed for 3D orthogonal woven composites. The superior notch impact properties of 3D orthogonal composites were attributed to the presence of the closer wrapping of binder warp ends that holds the filler and stuffer warp ends. From micro-CT scans, porosity was a common feature of DREF spun hybrid yarn composites. Also, composites made from powder coated towpregs presented better integrity and enabled in overcoming the disadvantages of the type of the weave. The composites made from electrostatic spray coating process can be used in biomedical applications due to their superior characteristics due to better impregnation of matrix under less severe consolidation (pressure, temperature and time) conditions. Also, the carbon-PP composites made from the electrostatic spray coating process are ergonomically suitable due to their lower specific weight.

ABSTRACT. Global energy consumption is increasing at a dramatic rate and most of the energy comes from fossil fuels which cause the problem of global warming due to the emission of greenhouse gases. Accordingly, the world is going through a crucial energy problem to supply efficient and cost-effective energy to face the rapid rise of energy need and increasing negative environmental impacts. Therefore, new guidelines and technological innovation are necessary to ensure energy security without causing emissions. The sustainable energy security and decarbonisation of electricity production is the national energy priority and global electricity challenge. This keynote focuses on various aspects of renewable energy integration in converting energy from conventional form to a sustainable form which in turn solves the existing net-zero problem. In this keynote, few developed projects on optimized algorithm or artificial intelligence-based energy storage systems (ESSs) integrated grid control technology and electric vehicle applications for a net-zero energy future will be demonstrated. Expected outcomes of this projects include new knowledge and comprehensive solutions using developed models and control technologies to achieve ESS integrated platform and electric vehicle integration ensuring new market strategies, decarbonized electricity and socio-economic viability towards net-zero pathways for future energy needs. These projects bring significant benefits in fulfilling the renewable energy initiatives to increase the uptake of battery energy storage and electric vehicle by 2030/2050. All the highlighted insights of this keynote will hopefully lead to increasing efforts towards the development of the battery storage integrated grid and EV applications for efficient energy conversion which in turn can reducing of global warming/net-zero.

ABSTRACT. AI and IoT are revolutionizing healthcare by enabling smarter, faster, and more accurate diagnosis and monitoring systems. This keynote highlights cutting-edge advancements such as IoT-based disease monitoring, AI-driven prediction models, and explainable deep learning techniques for conditions like Alzheimer’s, cancer, and heart disease. We explore lightweight models, hybrid methods, and real-world applications, including COVID-19 detection and malaria diagnosis, emphasizing their potential in resource-limited settings. Key trends, challenges, and ethical considerations in deploying secure and scalable systems will also be discussed, showcasing a future of precision and proactive care powered by technology.