Tune Inverter Control Gains using Reinforcement Learning
The growing complexity of modern power systems and the proliferation of inverter-based resources (IBRs) demand intelligent, adaptive, and autonomous control strategies. Traditional tuning approaches for inverter controllers often rely on linearized models, fixed assumptions, or manual parameter optimization methods that are increasingly inadequate in dynamic, data-rich grid environments.
In this talk, a reinforcement learning (RL)-based framework for inverter controller tuning, presenting a paradigm shift toward data-driven, self-optimizing control, will be introduced. At the core of this approach is Control RL, an open-source reinforcement learning library purpose-built for control and power system applications.
This webinar will demonstrate how Control RL enables rapid prototyping, training, and deployment of RL agents capable of achieving robust and optimal inverter control under diverse grid conditions. Beyond the theoretical foundation, attendees will see how this framework was used to develop and validate the results of our recent research on autonomous inverter tuning. By bridging the gap between RL theory and practical control engineering, this talk aims to empower researchers and practitioners to harness reinforcement learning for next-generation power system control.
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- Co-sponsored by University of California Riverside
- Starts 11 October 2025 01:00 AM UTC
- Ends 02 December 2025 12:00 AM UTC
- No Admission Charge
Speakers
Dr. Shuvangkar Das of Electric Power Research Institute (EPRI)
Dr. Shuvangkar Das
Biography:
Dr. Shuvangkar Das is a DER Power Converter Researcher at the Electric Power Research Institute (EPRI), where he works at the intersection of power electronics, inverter-based resources (IBRs), and artificial intelligence to help shape the next generation of renewable-ready power grids. His expertise spans grid-forming and grid-following inverter technologies, EMT simulation, real-time hardware-in-the-loop testing, and model-based firmware development.
Dr. Das earned his Ph.D. in Electrical Engineering from Clarkson University in 2025. His doctoral research focused on reinforcement learning–based controller tuning, real-time grid simulation, and renewable energy grid integration. He has led cross-functional R&D projects, developed industry-grade control solutions, and contributed to open-source firmware libraries that advance the adoption of inverter-based power systems.
Beyond research and engineering, Dr. Das is passionate about communication and knowledge management. He runs a YouTube channel where he simplifies complex topics around productivity, research workflows, and how personal knowledge management improves research productivity, helping students, engineers, and researchers build better systems for learning and innovation.
Through his work, Dr. Das aims to bridge the gap between power systems engineering, AI, and practical implementation, making advanced energy technologies more accessible, explainable, and impactful.