Cyber-physical power system FDIA detection based on seahorse optimized deep extreme learning machine
DOI：10.19783/j.cnki.pspc.246133
Key Words:cyber-physical power system  false data injection attack  sea-horse optimization  deep extreme learning machine
Author NameAffiliation
XI Lei1,2 1. College of Electrical Engineering and New Energy, China Three Gorges University, Yichang 443002, China
2. Hubei Provincial Key Laboratory for Operation and Control of Cascaded Hydropower Station, Yichang 443002, China 
BAI Fangyan1 1. College of Electrical Engineering and New Energy, China Three Gorges University, Yichang 443002, China
2. Hubei Provincial Key Laboratory for Operation and Control of Cascaded Hydropower Station, Yichang 443002, China 
WANG Wenzhuo1 1. College of Electrical Engineering and New Energy, China Three Gorges University, Yichang 443002, China
2. Hubei Provincial Key Laboratory for Operation and Control of Cascaded Hydropower Station, Yichang 443002, China 
PENG Dianming1 1. College of Electrical Engineering and New Energy, China Three Gorges University, Yichang 443002, China
2. Hubei Provincial Key Laboratory for Operation and Control of Cascaded Hydropower Station, Yichang 443002, China 
CHEN Hongjun1 1. College of Electrical Engineering and New Energy, China Three Gorges University, Yichang 443002, China
2. Hubei Provincial Key Laboratory for Operation and Control of Cascaded Hydropower Station, Yichang 443002, China 
LI Zongze1 1. College of Electrical Engineering and New Energy, China Three Gorges University, Yichang 443002, China
2. Hubei Provincial Key Laboratory for Operation and Control of Cascaded Hydropower Station, Yichang 443002, China 
Hits: 1116
Download times: 220
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.
View Full Text  View/Add Comment  Download reader