Hybrid Deep Learning for Dynamic Total Transfer Capability Control

Qiu Gao, Youbo Liu, Junbo Zhao, Junyong Liu, Chi Yung Chung

Research output: Journal article publicationJournal articleAcademic researchpeer-review

12 Citations (Scopus)

Abstract

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.

Original languageEnglish
Article number9349169
Pages (from-to)2733-2736
Number of pages4
JournalIEEE Transactions on Power Systems
Volume36
Issue number3
DOIs
Publication statusPublished - May 2021
Externally publishedYes

Keywords

  • artificial intelligence
  • deep learning
  • deep reinforcement learning
  • distributed proximal policy optimization
  • power system dynamics
  • Total transfer capability

ASJC Scopus subject areas

  • Energy Engineering and Power Technology
  • Electrical and Electronic Engineering

Fingerprint

Dive into the research topics of 'Hybrid Deep Learning for Dynamic Total Transfer Capability Control'. Together they form a unique fingerprint.

Cite this