Fast economic dispatch with false data injection attack in electricity-gas cyber-physical system: A data-driven approach

For data-driven fast economic dispatch in the electricity-gas cyber-physical system (EGCPS), the strong reliance on communication networks makes it vulnerable to intentional cyberattacks. False data injection attack (FDIA) is emerging as a severe threat to secure system operation. To enhance the cybersecurity and computational efficiency in data-driven fast economic dispatch, a data-driven approach is proposed to serve both exact locational detection of FDIA and fast economic dispatch in real time. The data-driven approach concatenates a residual network (ResNet) and attention long short term memory model (ALSTM) based on LSTM and attention mechanism (called "ResNet-ALSTM"), which can achieve temporal correlations and feature extraction of large amounts of measurements. The proposed ResNet-ALSTM can serve as a multi-label classifier to detect the outlier locations of tampered measurements in the power system of the EGCPS. Further, the Fast Dynamic Time Warping algorithm can timely recover the tampered measurements of power system after FDIA. Finally, the proposed ResNet-ALSTM can achieve fast economic dispatch in the EGCPS with the recovered measurements of power system. The performances of the proposed methods are experimentally evaluated on IEEE 24-bus power system and Belgian 20-node gas system. The results demonstrate that the proposed methods can achieve the locational detection of FDIA, tampered data recovery and data-driven fast economic dispatch with high accuracy and computational efficiency. This research is a promising solution for safeguarding secure system operation of EGCPS against FDIA.