Part II of this webinar will focus on the-state-of-the-art applications with some R&D work from Pacific Northwest National Laboratory as examples. These include PMU data analytics, uncertainty quantification(UQ), tie-line exchange prediction, adaptive Remedial Action Scheme (RAS) settings using machine learning, power system emergency control using deep reinforcement learning, which powered AlphaGo to beat human Go champions. The ultimate goal of these projects is to leverage state-of-the-art machine learning technologies to make decision-makings in power system control centers?the ?brain? of the grid? adaptive, robust and smart.
Slides for Webinar: Application of Machine Learning in Power Systems- Part 2 presented by Qiuhua Huang & Jason Hou
Posted: 1 Nov 2018
Authors:Qiuhua Huang & Jason Hou
Primary Committee:IEEE Smart Grid Webinar Series
Sponsoring Society Members: Free
IEEE Members: $11.00
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