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

ObjectivesThe 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).MethodsA 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.ResultsCompared 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.ConclusionsThe 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.