Abstract:  [Objectives] To enhance the resilience of the integrated energy system (IES) under false data injection attacks, this study aims to investigate the issue of resilience improvement after the IES is subjected to false data injection attacks (FDIA). [Methods] Construct a resilience enhancement framework for systems subjected to FDIA attacks, and establish a system resilience assessment method that considers both security and economy. Based on analyzing the attack mechanisms of energy flow and information flow, this paper proposes an energy flow FDIA detection method based on continuous wavelet transform (CWT)+convolutional neural network (CNN), as well as an information flow FDIA detection method based on CWT+general regression neural network (GRNN) model. Further, analyze the impact of FDIA attacks on system scheduling, establish an optimized scheduling model, and enhance the resilience of the system after network attacks. [Results] The comprehensive energy system resilience enhancement strategy with CWY+GRNN detection model is superior to the enhancement strategy without detection model, with 22.79% higher safety and reliability, 12.89% higher operational economy, and 19.82% higher resilience enhancement level. The comprehensive energy system resilience enhancement strategy with detection models is close to the level of operation without network attacks. [Conclusions] The comprehensive energy system resilience enhancement strategy proposed by the CWT+GRNN detection model can significantly improve system resilience after an FDIA attack, and the effect of system resilience enhancement is close to the level of normal operation.

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