不确定网络攻击下混合储能电力系统的模糊自适应事件触发频率调节

IF 7.5 1区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Expert Systems with Applications Pub Date : 2025-04-29 DOI:10.1016/j.eswa.2025.127867

Lin Gan , Kaibo Shi , Jin Yang , Hao Chen , Zhenzhen Zhang , Shouming Zhong

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引用次数: 0

摘要

研究了不确定网络攻击下混合储能电力系统的频率调节问题。首先，建立了不确定网络攻击下具有混合储能的多区域互联电力系统切换模型。然后，提出了一种模糊自适应事件触发机制来减少数据传输。在此基础上，设计了控制恢复意义上的非脆弱控制器。进一步，提出一种改进的分段非对称Lyapunov泛函方法，建立了具有较少保守性的H∞指数稳定性判据。最后，采用粒子群优化算法对频率调节性能进行优化。仿真算例验证了所提策略在经济运行中的有效性。结果表明，所提出的控制策略在提高频率调节性能和减少通信资源占用的同时，具有提高电力系统运行稳定性的智能作用。同时，在不确定的网络攻击存在的情况下，总能耗降低。

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Fuzzy adaptive event-triggered frequency regulation for power system with hybrid energy storage under uncertain cyber-attacks

This paper investigates the frequency regulation problem of power system with hybrid energy storage under uncertain cyber-attacks. Firstly, a switching model of multi-area interconnected power systems with hybrid energy storage under uncertain cyber-attacks is established. Then, a fuzzy adaptive event-triggered mechanism is proposed to reduce the data transmission. Based on this mechanism, a non-fragile controller is designed in the sense of control recovery. Further, the

H_{\infty}

exponential stability criterion with less conservatism is established by proposing an improved piecewise asymmetric Lyapunov functional method. Finally, the particle swarm optimization algorithm is used to optimize the frequency regulation performance. Simulation examples are provided to validate the proposed strategies regarding the effectiveness in economic operation. The results demonstrate that the developed control strategy has intelligence to improve the power system operation stability with enhanced frequency regulation performance and less communication resources occupation. Meanwhile, total energy consumption is reduced in the presence of uncertain cyber-attacks.

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来源期刊

Expert Systems with Applications 工程技术-工程：电子与电气

CiteScore

13.80

自引率

10.60%

发文量

2045

审稿时长

8.7 months

期刊介绍： Expert Systems With Applications is an international journal dedicated to the exchange of information on expert and intelligent systems used globally in industry, government, and universities. The journal emphasizes original papers covering the design, development, testing, implementation, and management of these systems, offering practical guidelines. It spans various sectors such as finance, engineering, marketing, law, project management, information management, medicine, and more. The journal also welcomes papers on multi-agent systems, knowledge management, neural networks, knowledge discovery, data mining, and other related areas, excluding applications to military/defense systems.