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Deep Reinforcement Learning for Power System Stability Control and Operation

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Hosted by IEEE PES

Join us to find out about Deep Reinforcement Learning (DRL) and its application to Power Systems Operation and Control, the drawbacks, and the solutions.  

DRL has been extensively applied in domains such as gaming and robotics, but has issues with sample inefficiency, scalability, adaptability and trustworthiness. Dr. Huang will share their work on development of new DRL algorithms using physical understanding of the grid, and tools for power system emergency control and corrective operation.

Dr. Qiuhua Huang is currently a senior power system research engineer at Pacific Northwest National Laboratory (PNNL). He leads and manages several U.S. DOE-funded projects, including a major ARPA-E OPEN project on intelligent real-time grid emergency control.

Online Location: https://gatech.webex.com/gatech/j.php?MTID=m0af9065fc3aeb183565a7148d6fefbfa

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  • Workflow status: Published
  • Created by: Kristen Bailey
  • Created: 10/18/2021
  • Modified By: Kristen Bailey
  • Modified: 10/18/2021

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