Hybrid Deep Learning for Dynamic Total Transfer Capability Control

This letter proposes a data-driven hybrid deep learning method for dynamic total transfer capability (TTC) control. It leverages deep learning (DL) to achieve fast prediction of TTC and reduce the problem complexity, while the deep reinforcement learning (DRL) method, e.g., proximal policy optimizat...

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Bibliographic Details
Published in:IEEE transactions on power systems 2021-05, Vol.36 (3), p.2733-2736
Main Authors: Gao, Qiu, Liu, Youbo, Zhao, Junbo, Liu, Junyong, Chung, Chi Yung
Format: Article
Language:English
Subjects:
artificial intelligence
Deep learning
deep reinforcement learning
distributed proximal policy optimization
Generators
Optimization
Power system dynamics
Security
Stability criteria
Total transfer capability
Training
Transient analysis
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Summary:This letter proposes a data-driven hybrid deep learning method for dynamic total transfer capability (TTC) control. It leverages deep learning (DL) to achieve fast prediction of TTC and reduce the problem complexity, while the deep reinforcement learning (DRL) method, e.g., proximal policy optimization (PPO), is enhanced by competitive learning (CL) to obtain a better generalization of the DRL agents. This also allows us to deal with system stochasticity. Comparison results with other model-based alternatives on the IEEE 39-bus system highlight the advantages of the proposed method for variable unseen and insecure scenarios.
ISSN:0885-8950
1558-0679
DOI:10.1109/TPWRS.2021.3057523