电力系统保护与控制2025，Vol.53Issue(4)：14-26,13.DOI:10.19783/j.cnki.pspc.246133
基于海马优化深层极限学习机的电力信息物理系统FDIA检测
Cyber-physical power system FDIA detection based on seahorse optimized deep extreme learning machine
摘要
Abstract
False data injection attack(FDIA)poses a serious threat to the security and stability of cyber-physical power systems.To address the limitations of the existing FDIA detection algorithms,ones that fail to precisely locate the attacked positions,an FDIA detection and localization algorithm based on elite cosine variation fusion of the seahorse optimization algorithm optimized deep extreme learning machine(DELM)is proposed.First,the algorithm combines the extreme learning machine and an auto-encoder to obtain the DELM with strong feature expression ability.Then,the bias and input weight of the DELM are optimized by the seahorse optimization,to improve the algorithmic index instability.Meanwhile,the elite cosine variation algorithm is introduced in the predation stage to further improve the convergence and accuracy of the DELM.Finally,system measurement data are used as input features to obtain the bus state labels using DELM,to realize the localization of the contaminated state variables.Through a large number of simulation comparative analyses in the IEEE 14-bus and 57-bus systems,it is verified that the proposed algorithm has obvious advantages in the detection and localization performance,such as accuracy,precision,recall,and F1 score,and it can achieve the precise localization of an FDIA.关键词
电力信息物理系统/虚假数据注入攻击/海马优化算法/深层极限学习机Key words
cyber-physical power system/false data injection attack/sea-horse optimization/deep extreme learning machine引用本文复制引用
席磊,白芳岩,王文卓,彭典名,陈洪军,李宗泽..基于海马优化深层极限学习机的电力信息物理系统FDIA检测[J].电力系统保护与控制,2025,53(4):14-26,13.基金项目
This work is supported by the National Natural Science Foundation of China(No.52277108 and No.52477104). 国家自然科学基金项目资助(52277108,52477104) （No.52277108 and No.52477104）
