FDIA Location Detection for Data-driven Algorithms in Cyber-physical Power Systems

XI Lei; PENG Dianming; CAO Wei; CHEN Hongjun; BAI Fangyan; WANG Wenzhuo

doi:10.13334/j.0258-8013.pcsee.240412

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FDIA Location Detection for Data-driven Algorithms in Cyber-physical Power Systems

更新时间：2026-10-19

- FDIA Location Detection for Data-driven Algorithms in Cyber-physical Power Systems
- Vol. 45, Issue 18, Pages: 7110-7122(2025)
- 作者机构：
  
  1. 梯级水电站运行与控制湖北省重点实验室(三峡大学电气与新能源学院),湖北省,宜昌市,443002
  2. 阳光电源股份有限公司,安徽省,合肥市,230088
- 作者简介：
- 基金信息：
- DOI：10.13334/j.0258-8013.pcsee.240412
  CLC：
- Published：2025
- 稿件说明：
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XI Lei, PENG Dianming, CAO Wei, et al. FDIA Location Detection for Data-driven Algorithms in Cyber-physical Power Systems[J]. 2025, 45(18): 7110-7122.
DOI：

XI Lei, PENG Dianming, CAO Wei, et al. FDIA Location Detection for Data-driven Algorithms in Cyber-physical Power Systems[J]. 2025, 45(18): 7110-7122. DOI： 10.13334/j.0258-8013.pcsee.240412.

摘要

虚假数据注入攻击严重威胁电力信息物理系统的安全。针对传统攻击检测方法无法高精度识别攻击并快速定位受攻击节点的问题，该文提出一种数据驱动算法的电力信息物理系统虚假数据注入攻击定位检测方法。首先，将核极限学习机与自编码器结合为多层核极限学习机，逐层学习电力量测数据。然后，利用融合Tent映射和黄金正弦策略的哈里斯鹰算法为多层核极限学习机的参数寻优，提升寻优速度和收敛精度。最后，在IEEE-14和IEEE-118节点系统对所提算法进行仿真验证。结果表明，与其他算法相比，所提算法具有更优的检测速度、准确率、精确率、召回率和F1值，可快速精准定位受攻击节点。

Abstract

False data injection attacks (FDIA) seriously threaten the security of cyber-physical power systems (CPPS). For the problem that the traditional detection methods are unable to identify attacks accurately and locate the attacked buses quickly

a FDIA localization method is proposed based on data-driven algorithms in CPPS. Firstly

the kernel extreme learning machine is combined with the autoencoder to form a multi-layer kernel extreme learning machine

which learns the power measurement data layer by layer. Then

the Harris hawk optimization

which incorporates the tent mapping and golden sine strategy

is utilized to optimize the parameters of the multi-layer kernel extreme learning machine

in order to improve the optimization speed and convergence accuracy. Finally

the proposed algorithm is verified by simulation in IEEE-14 and IEEE-118 bus test systems. The results show that the proposed algorithm has better detection speed

accuracy

precision

recall

and F1-Sorce compared to other algorithms

and can locate the attacked buses quickly and accurately.

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Related Author

BAI Fangyan

WANG Wenzhuo

PENG Dianming

CHEN Hongjun

XI Lei

CHENG Rui

ZENG Zehua

LI Chengze

Related Institution

Hubei Provincial Key Laboratory for Operation and Control of Cascaded Hydropower Station (China Three Gorges University)

College of Electrical Engineering and New Energy, China Three Gorges University

新能源电力系统国家重点实验室(华北电力大学), 北京市昌平区

State Key Laboratory of Disaster Prevention & Reduction for Power Grid (Changsha University of Science and Technology)

State Grid Hunan Electric Power Company Limited Economic & Technical Research Institute

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