State Estimation of Power Systems Under Measurement Anomalies

As a product of the integration of information and communication technologies, smart grid has greatly enhanced the efficiency of power system. However, with the development of the smart grid towards deep digitalization and interconnection, state estimation (SE) of power systems is facing dual challenges of a complex measurement environment and threat of cyber-attacks. The integrity and reliability of measurement data are affected by sensor failure, complex environmental noise, and data packet loss, causing state estimation deviations. Meanwhile, in recent years, malicious cyber-attacks, mainly in the form of false data injection into (FDIA) and denial-of-service (DoS), have also threatened the stable operation of power systems. This paper systematically reviews research achievements in related fields. Firstly, an analysis is conducted on the causes and mechanisms of measurement anomalies such as measurement loss, complex noise, and cyber-attacks. Then, the existing identification methods of measurement anomalies are reviewed, and state estimation methods for power systems under measurement anomaly conditions are analyzed from three perspectives: model-driven, data-driven, and hybrid-driven. Finally, advantages and disadvantages of various methods are analyzed, and future research directions are prospected, aiming to provide a reference for building a highly resilient and adaptive smart grid monitoring system.