使用基于变压器的卡尔曼过器估计器提高分布式直流微电网对网络攻击的弹性
Seyyed Mohammad Hosseini Rostami1, Mahdi Pourgholi2, Hadi Asharioun1
1Faculty of Electrical Engineering, Shahid Beheshti University, Tehran, Iran.
Scientific reports
|February 25, 2025
概括
本研究介绍了一种基于变压器的卡尔曼波器 (TKF),以提高DC微电网对攻击的网络安全性. 这种新的方法提高了系统稳定性和数据估计准确性在杂的环境中.
科学领域:
- 电气工程 电气工程
- 网络安全 网络安全
- 控制系统 控制系统
背景情况:
- 分布式直流微电网容易受到FDI,DoS和延迟攻击等网络攻击.
- 现有的方法在微电网操作中与杂的数据和高维数据提取作斗争.
研究的目的:
- 开发一个数据驱动的方法来提高分布式直流微电网对网络攻击的弹性.
- 在杂的环境中提高信号传输预测的准确性.
主要方法:
- 开发了一个基于变压器的卡尔曼波器 (TKF) 估计器.
- 集成了一个自动回归集成移动平均线 (ARIMA) 模型用于状态空间表示.
- 利用深度学习,结合变压器和长短期记忆 (LSTM) 网络来提取数据.
主要成果:
- 通过模拟,证明了TKF估计器在各种攻击场景下保持微电网稳定性的有效性.
- 在估计准确度和系统性能方面取得了显著的改进.
- 验证了针对网络威胁的拟议方法的稳定性.
结论:
- 拟议的TKF方法有效地提高了DC微电网对网络攻击的弹性.
- 深度学习和先进的过技术的整合显示出未来微网安全的前景.
- 进一步的研究可以探索先进的过和深度学习,以提高适应非线性性的能力.
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