School of Electrical and Power Engineering, Hohai University, Nanjing 211100, China

With the development of advanced metering infrastructure and the wide application of smart meters, the rich terminal measurement information is provided for the three-phase state estimation of distribution networks. At the same time, a large amount of smart meter data puts forward higher communication bandwidth and real-time storage requirements to the communication system of distribution networks. In order to alleviate the phenomenon of the measurement congestion and delay, this paper introduces an event-triggered mechanism instead of the traditional periodic sampling of measurement data, which ensures the timely uploading of effective measurement information while reducing the communication cost and investment. On this basis, for the real-time state sensing problem of distribution network, this paper proposes a three-phase dynamic state estimation method based on the robust ensemble Kalman filter, which can maintain estimation accuracy similar to the weighted least squares method in normal operation scenarios. The method also possesses strong robustness against bad data.

This work is supported by National Natural Science Foundation of China (No. 52207090).