Location Detection of False Data Injection Attacks in Power Grid Based on Improved Deep Extreme Learning Machine

doi:10.12096/j.2096-4528.pgt.24115

Abstract

Abstract:

Objectives Power systems are facing threats of false data injection attacks. Existing detection methods for false data injection attacks have the problems of insufficient feature learning ability and slow detection speed, making it difficult to locate false data injection attacks rapidly and accurately. Therefore, this study proposes a method for locating false data injection attacks in power grid based on deep extreme learning machine optimized by Levy flying sparrow search algorithm. Methods The proposed method uses a deep extreme learning machine as the feature extraction algorithm and basic classifier to achieve rapid and accurate attack location. At the same time, a Levy flying sparrow search algorithm with strong local search ability is employed to optimize the initial weight and bias to further improve the location detection accuracy of the method. Results Extensive simulation analyses are conducted on IEEE-14 and IEEE-57 bus power systems. The proposed method achieves a detection accuracy rate of over 94%. Conclusions Compared with other detection methods, the proposed method demonstrates better detection accuracy and enables faster location detection of false data injection attacks.

Key words: power grid, artifical intelligence(AI), power system, false data injection attack, deep extreme learning machine, Levy flying, sparrow search algorithm, location detection, feature extraction

CLC Number:

TK 01

Lei XI, Yixiao WANG, Yahui XIONG, Lu DONG. Location Detection of False Data Injection Attacks in Power Grid Based on Improved Deep Extreme Learning Machine[J]. Power Generation Technology, 2025, 46(3): 521-531.

Figures/Tables 15

Fig. 1 Structure of the FDIA

Fig. 2 Structure of FDIA location detection model based on DELM

Fig. 3 Flowchart of location detection method based on LSA-DELM

Fig. 4 Maximum normalized residuals before and after attack in example 1

Fig. 5 Maximum normalized residuals before and after attack in example 2

Fig. 6 Comparison of measured data before and after attack in example 1

Fig. 7 Comparison of measured data before and after attack in example 2

Fig. 8 Comparison of state variables before and after attack in example 1

Fig. 9 Comparison of state variables before and after attack in example 2

Fig. 10 Detection accuracy rates at different nodes in example 1

Fig. 11 Detection accuracy rates at different nodes in example 2

Fig. 12 Results of location detection indicators in example 1

Fig. 13 Results of location detection indicators in example 2

Fig. 14 ROC curves of IEEE-14 bus power system

Fig. 15 ROC curves of IEEE-57 bus power system

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