Simultaneous Dynamic State Estimation and Fault Data Detection in Frequency Control Loop of Interconnected Multi-area Power Systems

Accurate state information plays a pivotal role in ensuring the stable frequency of power systems.  Because of the interplay between digital and physical components in these systems, they are susceptible to sudden disturbances and the risk of False Data Injection Attacks (FDIAs), potentially resulting in power grid malfunctions and instability. This paper introduces a novel stochastic estimation framework designed for real-time monitoring of dynamic states within the Load Frequency Control (LFC) system of multi-region interconnected power systems in a decentralized manner. Beyond state estimation, investigation of the disruptions induced by FDIA in regions engaged in mutual electrical power exchange is also an objective. A decentralized Dynamic State Estimation (DSE) framework is proposed to comprehensively monitor the states of individual power areas, incorporating renewable energy sources (RESs) and storage systems. The proposed method combines the strengths of the unscented Kalman filter (UKF) and the fault data detection (FDI) framework to achieve a more reliable and accurate DSE of a multi-source power system area integrated with other areas. An elevation in the residual function beyond a predetermined threshold indicates an FDIA. A simulation study is executed under different possibilities of fault occurrence. The results indicate that UKF act as a recursive estimator to provide accurate state information. Further the proposed DSE framework investigates any fault situation with alarm indication that has a potential for the efficient and effective monitoring of distribution systems with RES integration in smart grids.