Resilient Recursive Filtering for Integrated Electricity‐Gas Systems under Uncertain Communication Links

This paper investigates the resilient state estimation problem for a class of integrated electricity‐gas systems (IEGSs) under uncertain communication links. Building upon a unified dynamic modeling framework, a comprehensive state‐space model is developed to characterize the coupled electricity‐gas dynamics. Sensor measurements are transmitted to remote estimators via bandwidth‐constrained digital channels, necessitating the implementation of signal quantization. In addition, the channel variations are modeled using normally distributed random variables to realistically capture communication uncertainties. Unlike conventional estimation approaches, the proposed scheme systematically incorporates three critical factors including quantization errors, linearization errors, and channel uncertainties, thereby ensuring robust state estimation performance under practical communication constraints. The effectiveness of the proposed resilient filtering scheme is demonstrated through simulations on an IEGS test case, consisting of a 14‐bus power network and a 7‐node gas grid.