Deep Reinforcement Learning Enabled Bi-Level Robust Parameter Optimization of Hydropower-Dominated Systems for Damping Ultra-Low Frequency Oscillation

Deep Reinforcement Learning Enabled Bi-Level Robust Parameter Optimization of Hydropower-Dominated Systems for Damping Ultra-Low Frequency Oscillation

This paper proposes a robust and computationally efficient control method for damping ultra-low frequency oscillations (ULFOs) in hydropower-dominated systems. Unlike the existing robust optimization based control formulation that can only deal with a limited number of operating conditions, the prop...

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Bibliographic Details
Main Authors: Guozhou Zhang, Junbo Zhao, Weihao Hu, Di Cao, Nan Duan, Zhe Chen, Frede Blaabjerg
Format: Article
Language:English
Published: IEEE 2023-01-01
Series:Journal of Modern Power Systems and Clean Energy
Subjects:
Bi-level robust parameter optimization
deep reinforcement learning
deep deterministic policy gradient
ultra-low frequency oscillation
damping control stability
Online Access:https://ieeexplore.ieee.org/document/10081257/

Internet

https://ieeexplore.ieee.org/document/10081257/

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