Resilience Enhancement Strategies for Integrated Energy Systems Against False Data Injection Attacks

doi:10.12096/j.2096-4528.pgt.260212

Abstract

Abstract:

Objectives The openness of the network makes integrated energy systems vulnerable to cyber attacks, focusing on the resilience enhancement of integrated energy systems (IES) against false data injection attacks (FDIA). Methods A resilience enhancement framework is developed for IES under FDIA, and a system resilience assessment method is established, with the inclusion of both security and economic factors. Based on the analysis of energy flow and information flow attack mechanisms, an FDIA detection method for energy flow is proposed using continuous wavelet transform (CWT) and convolutional neural network (CNN). Additionally, an FDIA detection method for information flow is developed using CWT and general regression neural network (GRNN) model. The effect of FDIA on system scheduling is further analyzed, and an optimized scheduling model is established to enhance system resilience after experiencing cyberattacks. Results Compared with strategies without detection models, the resilience enhancement strategy incorporating CWT+GRNN detection models demonstrates better performance, with a 22.79% improvement in security and reliability, a 12.89% increase in operational economy, and a 19.82% higher resilience level. The resilience enhancement strategy incorporating detection models achieves performance levels close to those of normal operation without cyberattacks. Conclusions The proposed resilience enhancement strategy for IES based on CWT+GRNN detection model significantly improves system resilience after FDIA, bringing the system performance close to its normal operation level.

Key words: electricity-heat-gas, integrated energy system(IES), false data injection attack(FDIA), attack detection, optimized scheduling, resilience enhancement

CLC Number:

TK 01

Lizhen WU, Yongpeng ZHANG, Jianping WEI, Wei CHEN. Resilience Enhancement Strategies for Integrated Energy Systems Against False Data Injection Attacks[J]. Power Generation Technology, 2026, 47(2): 345-358.

Figures/Tables 14

References 38

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节点编号	有功功率	无功功率	有功上限	有功下限	无功上限	无功下限
1	—	—	1.0	0.5	1.0	-0.20
2	0.8	—	0.8	0.2	0.6	-0.20
5	0.5	—	0.5	0.15	0.624 5	-0.15
8	0.2	—	0.35	0.1	0.487 3	-0.15
11	0.2	—	0.3	0.1	0.4	-0.10
13	0.2	—	0.4	0.12	0.447 2	-0.15

节点编号	有功功率	无功功率	有功上限	有功下限	无功上限	无功下限
1	—	—	1.0	0.5	1.0	-0.20
2	0.8	—	0.8	0.2	0.6	-0.20
5	0.5	—	0.5	0.15	0.624 5	-0.15
8	0.2	—	0.35	0.1	0.487 3	-0.15
11	0.2	—	0.3	0.1	0.4	-0.10
13	0.2	—	0.4	0.12	0.447 2	-0.15

检测方法	实际标签	测试数据	确认受到攻击	确定为正常	检测精度/%
本文方法	受到攻击	1 247	1 178	69	94.47
本文方法	正常	1 247	32	1 215	97.43
ANN	受到攻击	1 247	1 124	123	90.14
ANN	正常	1 247	57	1 190	95.43
2类SVM	受到攻击	1 247	1 159	88	92.94
2类SVM	正常	1 247	44	1 203	96.47
单类SVM	受到攻击	1 247	783	464	92.79
单类SVM	正常	1 247	76	1 171	93.91

检测方法	实际标签	测试数据	确认受到攻击	确定为正常	检测精度/%
本文方法	受到攻击	1 247	1 178	69	94.47
本文方法	正常	1 247	32	1 215	97.43
ANN	受到攻击	1 247	1 124	123	90.14
ANN	正常	1 247	57	1 190	95.43
2类SVM	受到攻击	1 247	1 159	88	92.94
2类SVM	正常	1 247	44	1 203	96.47
单类SVM	受到攻击	1 247	783	464	92.79
单类SVM	正常	1 247	76	1 171	93.91

初始信号	训练数据	攻击检测精度/%
初始信号	训练数据	1个样本	2个样本	3个样本
电价	没有噪声	87.47	97.36	98.56
电价	带有噪声	85.00	94.80	96.10
PV发电	没有噪声	91.81	94.95	96.39
PV发电	带有噪声	89.50	92.80	94.20