Computational Approaches for Bad Data Handling in Power System Synchrophasor Networks

摘要

The state estimators used in real-time power system control centers now process bad data as a standard routine. With the introduction and deployment of phasor measurement units (PMUs), it is possible to model power systems, even with their time-varying nature, in real-time. However, PMUs remain vulnerable to providing bad data for several reasons. In this paper, a new intelligent framework, the cellular computational network (CCN), is introduced for the decentralized predictive modeling and dynamic state estimation (DSE) of a power system with PMU data. The CCN-based DSE is resilient to interactions between multiple segments of bad data from one or more PMUs.