Dynamic State Estimation of Smart Distribution Grids Using Compressed Measurements

Abstract

State estimation has a special role in the real-time control and monitoring of smart distribution networks. State estimation process is typically based on network topology and measurements sent from meters. Employing an accurate state estimation algorithm as well as transferring high volumes of measurements are serious challenges in large scale grids. In this paper, compressive sensing is used to reduce the measurement data volume, before transmission, to alleviate problems such as lack of storage space, interference and delay. In this paper, a modified extended Kalman filter algorithm is proposed which estimates states from compressed data directly without applying the reconstruction procedure. The main differences between the proposed method and EKF are the network dynamic modeling approach and the states correction mechanism. The IEEE 33-node distribution network with two DGs is employed to illustrate the effective performance of the proposed method. Results show that the states of the test feeder are accurately estimated even with only 50% compressed measurements.