Lina Bertling Tjernberg (Docent, Ph.D., MSc.) is a Professor in Power Grid Technology and Deputy Head of the School of Electrical Engineering and Computer Science at KTH, Royal Institute of Technology, Stockholm, Sweden. She is a Fellow of the Royal Swedish Academy of Engineering, a senior member of IEEE, and serves as an independent expert for the EU Commission. Lina has held various leadership roles in IEEE and received the 2021 Power Woman of the Year award for her contributions to sustainable energy systems. Lina established the Reliability Centered Asset Management (RCAM) framework and has co-authored over 140 scientific articles and several books, including Infrastructure Asset Management with Power System Applications. Her research focuses on applying mathematics to predict and model reliability in electric power systems, with interests in microgrids, battery storage, renewable energy, and electrified transportation. She is an active advisor and expert in several professional organizations and a frequent keynote speaker. Currently, she serves on the board of the Swedish Electromobility Center and the IEEE PES ISGT Europe Steering Group

Transforming Predictive Maintenance and Condition Monitoring with AI-Based Machine Learning in Asset Management

The integration of artificial intelligence (AI) and machine learning (ML) into asset management is revolutionizing the field of predictive maintenance and condition monitoring. This keynote will explore cutting-edge AI-based ML models that enhance the efficiency, reliability, and longevity of critical assets within electric power and energy systems. Attendees will gain insights into how these advanced technologies can predict failures, optimize maintenance schedules, and monitor the condition of assets in real time. Through detailed examples and case studies, the practical applications of AI-driven predictive maintenance and condition monitoring will be demonstrated and, highlighting the significant improvements in operational performance and cost savings