Data-driven load frequency control for stochastic power systems : a deep reinforcement learning method with continuous action search

Data-driven load frequency control for stochastic power systems : a deep reinforcement learning method with continuous action search

This letter proposes a data-driven, model-free method for load frequency control (LFC) against renewable energy uncertainties based on deep reinforcement learning (DRL) in continuous action domain. The proposed method can nonlinearly derive control strategies to minimize frequency deviation with fas...

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Main Authors: Yan, Ziming, Xu, Yan
Other Authors: School of Electrical and Electronic Engineering
Format: Article
Language:English
Published: 2020
Subjects:
Engineering::Electrical and electronic engineering
Continuous Action Search
Deep Reinforcement Learning
Online Access:https://hdl.handle.net/10356/141500
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Institution: Nanyang Technological University
Language: English
id sg-ntu-dr.10356-141500
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spelling sg-ntu-dr.10356-1415002020-06-09T01:47:22Z Data-driven load frequency control for stochastic power systems : a deep reinforcement learning method with continuous action search Yan, Ziming Xu, Yan School of Electrical and Electronic Engineering Engineering::Electrical and electronic engineering Continuous Action Search Deep Reinforcement Learning This letter proposes a data-driven, model-free method for load frequency control (LFC) against renewable energy uncertainties based on deep reinforcement learning (DRL) in continuous action domain. The proposed method can nonlinearly derive control strategies to minimize frequency deviation with faster response speed and stronger adaptability for unmolded system dynamics. It consists of offline optimization of LFC strategies with DRL and continuous action search, and online control with policy network where features are extracted by stacked denoising auto-encoders. Numerical simulations verify the effectiveness and advantages of proposed method over existing approaches. MOE (Min. of Education, S’pore) 2020-06-09T01:47:22Z 2020-06-09T01:47:22Z 2018 Journal Article Yan, Z., & Xu, Y. (2019). Data-driven load frequency control for stochastic power systems : a deep reinforcement learning method with continuous action search. IEEE Transactions on Power Systems, 34(2), 1653-1656. doi:10.1109/TPWRS.2018.2881359 0885-8950 https://hdl.handle.net/10356/141500 10.1109/TPWRS.2018.2881359 2-s2.0-85056584019 2 34 1653 1656 en IEEE Transactions on Power Systems © 2018 IEEE. All rights reserved.
institution Nanyang Technological University
building NTU Library
country Singapore
collection DR-NTU
language English
topic Engineering::Electrical and electronic engineering
Continuous Action Search
Deep Reinforcement Learning
spellingShingle Engineering::Electrical and electronic engineering
Continuous Action Search
Deep Reinforcement Learning
Yan, Ziming
Xu, Yan
Data-driven load frequency control for stochastic power systems : a deep reinforcement learning method with continuous action search
description This letter proposes a data-driven, model-free method for load frequency control (LFC) against renewable energy uncertainties based on deep reinforcement learning (DRL) in continuous action domain. The proposed method can nonlinearly derive control strategies to minimize frequency deviation with faster response speed and stronger adaptability for unmolded system dynamics. It consists of offline optimization of LFC strategies with DRL and continuous action search, and online control with policy network where features are extracted by stacked denoising auto-encoders. Numerical simulations verify the effectiveness and advantages of proposed method over existing approaches.
author2 School of Electrical and Electronic Engineering
author_facet School of Electrical and Electronic Engineering
Yan, Ziming
Xu, Yan
format Article
author Yan, Ziming
Xu, Yan
author_sort Yan, Ziming
title Data-driven load frequency control for stochastic power systems : a deep reinforcement learning method with continuous action search
title_short Data-driven load frequency control for stochastic power systems : a deep reinforcement learning method with continuous action search
title_full Data-driven load frequency control for stochastic power systems : a deep reinforcement learning method with continuous action search
title_fullStr Data-driven load frequency control for stochastic power systems : a deep reinforcement learning method with continuous action search
title_full_unstemmed Data-driven load frequency control for stochastic power systems : a deep reinforcement learning method with continuous action search
title_sort data-driven load frequency control for stochastic power systems : a deep reinforcement learning method with continuous action search
publishDate 2020
url https://hdl.handle.net/10356/141500
_version_ 1681058173911302144

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