AI-Driven Stability and Control of Converter-Dominated Power Systems – Assoc. Prof. Qianwen Xu
In connection with the PhD defense of Lisa Marie Dannappel, DTU Wind and Energy Systems is pleased to have Assoc. Prof. Qianwen Xu from KTH Royal Institute of Technology, who will give a talk titled “AI-Driven Stability and Control of Converter-Dominated Power Systems.”
Modern power systems are rapidly evolving into converter-dominated energy systems, driven by distributed energy resources, electrified transport, and emerging large-scale loads such as data centers. This transformation fundamentally reshapes grid stability and operation: stability is no longer governed by physical inertia but by fast, complex converter interactions, while system operation must cope with increasing uncertainty and scale.
This talk presents how artificial intelligence can enable reliable and scalable operation of such systems across three layers. First, transfer learning-based approaches are developed for fast, online modeling of converter-interfaced assets, enabling accurate system representation without detailed device-level knowledge. Second, AI-driven stability assessment frameworks are introduced for real-time evaluation of converter–grid interactions. Third, safe deep reinforcement learning methods are presented for large-scale grid operation, ensuring guaranteed safety while optimizing performance. Together, these advances point toward a new paradigm of autonomous, AI-enabled energy systems, where learning-based methods are tightly integrated with physical constraints to ensure stability, efficiency, and resilience.
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- Co-sponsored by DTU Wind and Energy Systems
Speakers
Qianwen Xu
AI-Driven Stability and Control of Converter-Dominated Power Systems
Modern power systems are rapidly evolving into converter-dominated energy systems, driven by distributed energy resources, electrified transport, and emerging large-scale loads such as data centers. This transformation fundamentally reshapes grid stability and operation: stability is no longer governed by physical inertia but by fast, complex converter interactions, while system operation must cope with increasing uncertainty and scale.
This talk presents how artificial intelligence can enable reliable and scalable operation of such systems across three layers. First, transfer learning-based approaches are developed for fast, online modeling of converter-interfaced assets, enabling accurate system representation without detailed device-level knowledge. Second, AI-driven stability assessment frameworks are introduced for real-time evaluation of converter–grid interactions. Third, safe deep reinforcement learning methods are presented for large-scale grid operation, ensuring guaranteed safety while optimizing performance. Together, these advances point toward a new paradigm of autonomous, AI-enabled energy systems, where learning-based methods are tightly integrated with physical constraints to ensure stability, efficiency, and resilience.
Biography:
Qianwen Xu is an Associate Professor at KTH Royal Institute of Technology, Sweden, where she leads the Intelligent Sustainable Grid Lab and co-directs the Dig-It Lab. She received her PhD from Nanyang Technological University, Singapore in 2018. Her research focuses on modeling, stability assessment, and control of power electronics-dominated energy systems, with applications in microgrids, electrified transport, and large-scale grids. She serves as an Associate Editor for IEEE Transactions on Smart Grid, IEEE Transactions on Sustainable Energy, IEEE Transactions on Transportation Electrification, and IEEE Transactions on Industrial Informatics. She is Chair of the IEEE Power and Energy Society & Power Electronics Society, Sweden. She has received the IEEE J. David Irwin Early Career Award (2025), and her work has been recognized by the Royal Swedish Academy of Engineering Sciences (IVA 100) for its societal impact.
Address:Sweden
Agenda
- Welcome by Senior Researcher Shi You
- Talk by Associate Prof. Qianwen Xu from KTH "AI-Driven Stability and Control of Converter-Dominated Power Systems"
- Closure