Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/141500
Title: Data-driven load frequency control for stochastic power systems : a deep reinforcement learning method with continuous action search
Authors: Yan, Ziming
Xu, Yan
Keywords: Engineering::Electrical and electronic engineering
Issue Date: 2018
Source: 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
Journal: IEEE Transactions on Power Systems
Abstract: 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.
URI: https://hdl.handle.net/10356/141500
ISSN: 0885-8950
DOI: 10.1109/TPWRS.2018.2881359
Rights: © 2018 IEEE. All rights reserved.
Fulltext Permission: none
Fulltext Availability: No Fulltext
Appears in Collections:EEE Journal Articles

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