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Development a partially observable Markov decision processes-based intelligent assistant for power grids using Monte Carlo tree search

Citation

Tomin, NV and Kurbatsky, V and Negnevitsky, M, Development a partially observable Markov decision processes-based intelligent assistant for power grids using Monte Carlo tree search, Proceedings of the 10th International Scientific Symposium on Electrical Power Engineering, 16-18 September 2019, Stara Lesna, Slovakia, pp. 389-393. ISBN 9781510888715 (2019) [Refereed Conference Paper]

Copyright Statement

Copyright 2019 Elsevier

Official URL: https://www.scopus.com/record/display.uri?eid=2-s2...

Abstract

Autonomous control systems will make much "smarter" used automatic controls of modern power grids, as well as partially or completely replace the system operator, which may not be able sometimes to adequately respond to critical conditions due to psychological stress. Development of such systems can be solved by Monte-Carlo tree search algorithm that simulate ahead into the future, evaluate future states, and back-up those evaluations to the root of a search tree. We use the formalism of POMDPs (Partially Observable Markov Decision Processes) as the core of an intelligent assistant for power system control and dispatch. We demonstrate the feasibility of the approach to resolve the voltage and reactive power control in substation.

Item Details

Item Type:Refereed Conference Paper
Keywords:power system, monte carlo tree search, control partially observable, markov decision processes, reinforcement learning
Research Division:Engineering
Research Group:Electrical and Electronic Engineering
Research Field:Renewable Power and Energy Systems Engineering (excl. Solar Cells)
Objective Division:Energy
Objective Group:Energy Conservation and Efficiency
Objective Field:Industrial Energy Conservation and Efficiency
UTAS Author:Negnevitsky, M (Professor Michael Negnevitsky)
ID Code:137463
Year Published:2019
Deposited By:Engineering
Deposited On:2020-02-14
Last Modified:2020-04-09
Downloads:0

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