Document Type : Original Article
1School of Mechanical Engineering，Northwestern Polytechnical University, Xi'an 710072, Shaanxi, China
The increasing integration of gas-fired units (GFU) and power-to-gas (P2G) technology has led to the interconnection of natural gas and electricity networks. However, the advanced information and communication equipment in these integrated electricity-gas energy systems give rise to cybersecurity concerns. This research proposes an entropy-based load redistribution (LR) attack detection approach for such integrated networks. The objective is to simultaneously overload multiple electrical lines and gas pipelines using a bi-level LR attack model, while ensuring system security through a defense strategy. Instead of relying on deep learning algorithms, this study leverages entropy-based techniques for attack detection. In order to thoroughly investigate the attack space, an attack detector based on entropy is trained utilizing a combination of normal data and randomly generated LR attacks. The efficacy of the suggested methodology in mitigating the hazards linked to inaccurate data injection is substantiated via simulations conducted on a modified version of the IEEE 118-bus power system, which incorporates a 14-node gas system. The findings indicate that the detector based on entropy exhibits efficacy in detecting both stochastic and purposeful attacks, thereby augmenting the security of interconnected gas and electricity networks.