Deep Learning for Power System Operation and Planning
* 21PESGM2576, Deep Learning based Model-free Robust Load Restoration to Enhance Bulk System Resilience with Wind Power Penetration: J. ZHAO, University of Tennessee Knoxville, F. LI, University of Tennessee Knoxville * 21PESGM2577, Machine Learning of Distribution System Planning Models, M. RENO, Sandia National Laboratories * 21PESGM2578, Deep Transfer Reinforcement Learning: from walking robots to intelligent power grid, W. YU, Google * 21PESGM2579, Using Machine Learning for Power System Stability and Operation, J. TAN, National Renewable Energy Laboratory * 21PESGM2580, Deep reinforcement learning based frequency control of stochastic power systems, Y. XU, Nanyang Technological University * 21PESGM2581, Towards Interpretable Deep learning for Active Distribution Networks, N. DUAN, Lawrence Livermore National Laboratory
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Members: $5.00PES
IEEE Members: $10.00
Non-members: $20.00
26 Jul 2021
Deep Learning (DL) and Artificial Intelligence (AI) is the emerging technology for realizing the next generation smart grid. In recent years, significant efforts have been devoted to exploring the potentials of DL and AI for solving the complex power system problems, from generations all the way down to the demand side. In this panel, the focus will be given to the application of DL in broad areas of power system operation and planning. Experts from academia and industry will share their original ideas and insights to this challenging and inspiring topic.
Chairs:
Fangxing Li, University of Tennessee Knoxville, Di Shi, AINERGY LLC
Sponsor Committees:
Sponsored By: (PSOPE) Technologies &, Innovation Subcommittee
