A Q-Learning Based Dynamic Event-Triggered Control for Load Frequency Regulation of Power Systems with Denial-of-Service Attacks

被引：3

作者：

Chen, Pengcheng ^{[1
]}

Liu, Shichao ^{[1
]}

Zhang, Dan ^{[2
]}

机构：

[1] Carleton Univ, Dept Elect, Ottawa, ON K1S 5B6, Canada

[2] Zhejiang Univ Technol, Dept Automat, Hangzhou 310023, Peoples R China

来源：

PROCEEDINGS OF 2021 IEEE 30TH INTERNATIONAL SYMPOSIUM ON INDUSTRIAL ELECTRONICS (ISIE) | 2021年

关键词：

Q-learning algorithm; Load frequency control; Event-triggered scheme; Denial-of-Service attacks; DELAY;

D O I：

10.1109/ISIE45552.2021.9576200

中图分类号：

T [工业技术];

学科分类号：

08 ;

摘要：

This work proposes a novel Q-learning based dynamic event-triggered strategy integrated with decentralized output feedback controller for the load frequency regulation scheme to alleviate the influence of Denial-of-Service(DoS) attacks with the given maximum attack duration and load fluctuations. The Q-learning algorithm is applied in dynamic event-triggered strategy to optimize the threshold of the release condition through maximizing the cumulative discount reward, which can greatly reduce the amount of data transmission and preserve a satisfactory system performance. A three-area power system with LFC scheme is simulated to affirm the effectiveness of the proposed results.

引用

页数：5

共 17 条

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