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人工智能用于网络物理电力电子转换器的网络安全监控:一个直流/直流电力转换器案例研究

Mohammad Reza Habibi¹, Josep M Guerrero^2,3,4, Juan C Vasquez²

¹AAU Energy, Aalborg University, Aalborg, Denmark. mre@energy.aau.dk.

Scientific reports

|September 27, 2024

概括

人工智能 (AI) 检测和量化对电力转换器的虚假数据注入网络攻击. 这种人工智能策略有效地监控系统,即使有噪音和通信延迟,确保功率转换器的安全性.

科学领域:

网络物理系统网络物理系统
动力电子产品的动力电子.
人工智能的人工智能

背景情况:

电力电子转换器在微电网等应用中至关重要,将电力资源连接到应用程序.
这些系统是网络物理的,容易受到网络攻击,需要强大的监控和检测策略.
精确的电压和电流测量对于控制功率转换器至关重要,因此数据完整性至关重要.

研究的目的:

开发和评估基于人工智能 (AI) 的战略,用于检测和量化对功率转换器的虚假数据注入网络攻击.
在噪音和通信链路延迟的情况下评估拟议的AI方法的性能.
为了比较不同的AI技术的有效性,包括人工神经网络 (ANN) 和特定的网络架构.

主要方法:

人工智能 (AI) 技术,特别是人工神经网络 (ANN),用于检测和计算虚假数据.
测试了两种类型的ANN,即feedforward和基于长短期内存 (LSTM) 的网络.
该方法在不同的信号噪声比率和对称和非对称的通信延迟下进行了严格的评估.

主要成果:

基于人工智能的策略成功地识别和计算了恒定和时间变化的虚假数据.
提出的方法即使在不同程度的噪音和通信延迟的情况下也表现出有效性.
接和LSTM网络都显示出检测网络攻击的能力,LSTM可能提供更复杂的分析.

结论:

开发的AI策略提供了一种可靠的方法来监控功率转换器系统并检测假数据注入网络攻击.
针对噪音和通信延迟的AI方法的稳定性确保了它在现实场景中的实际适用性.
进一步的研究可以探索先进的AI模型和混合技术,以加强电力系统中网络攻击的检测.

关键词:

人工智能的人工智能网络物理系统网络物理系统网络安全网络安全虚假数据注入攻击的攻击.电力转换器电力转换器

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