基于改进的Kalman/H∞联合过的网络物理动力系统中的动态负载改变攻击检测
Jian Li1, Yunfeng Wang1, He Ren2
1School of Automation Engineering, Northeast Electric Power University, 132012, Jilin, China.
ISA transactions
|October 8, 2025
概括
本研究介绍了网络物理动力系统 (CPPS) 中动态负载改变攻击 (DLAA) 的新检测方案. 拟议的方法提高了状态估计的准确性,并改善了针对网络攻击和噪音干扰的攻击检测.
科学领域:
- 网络物理动力系统 (CPPSs)
- 控制系统工程 控制系统工程
- 网络安全 网络安全
背景情况:
- 网络物理电力系统 (CPPS) 容易受到动态负载改变攻击 (DLAA) 的攻击.
- 准确的状态估计和强大的攻击检测对于CPPS的稳定性和安全性至关重要.
- 现有的方法与未知统计噪声和非高斯干扰作斗争.
研究的目的:
- 为CPPS中闭环DLAA提出一个先进的攻击检测方案.
- 为了提高状态估计和攻击检测能力的准确性.
- 为了应对DLAA和噪声干扰在CPPSs带来的挑战.
主要方法:
- 开发了一个离散时间的CPPS模型,包括DLAA和未知统计噪声.
- 为状态估计提出了改进的Kalman/H∞共过器,利用MFAKF用于高斯噪声和H∞过器用于非高斯干扰.
- 设计了一种基于状态估计偏差的异常检测的共弦相似度匹配算法.
主要成果:
- 拟议的MFAKF-HF显著减少了RMSE,与MFAKF相比降低了75%,与H∞相比降低了62%,在IEEE三机六总线系统中对状态 ω1进行过.
- 在DLAA和噪声条件下,在状态估计中表现出更好的准确性.
- 验证了综合估计和检测方案的稳定性和有效性.
结论:
- 开发的方案在CPPS中在状态估计和DLAA检测方面提供了卓越的性能.
- 结合MFAKF和H∞过,可以有效地处理各种噪声特征.
- 协弦值相似性匹配为识别电力系统中的网络攻击提供了一种可靠的方法.
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