Real-Time Status Estimation of Active Distribution Network Based on Multi-source Asynchronous Data Fusion

With the development of intelligent regulation and control of electricity market and distribution network, higher requirements are put forward for state estimation. Multiple measurements coexist in the distribution network, and it is difficult to integrate multi-source asynchronous data. In this regard, according to the sampling characteristics of multi-source measurement data, a joint state estimation framework is constructed, which includes three modes: static estimation, dynamic and static combination and dynamic estimation. The SM sampling period is the static estimation period, the SCADA sampling period is the static estimation small period, and the PMU sampling period is the dynamic estimation period. When SCADA real-time measurement data is collected, static estimation is performed based on PMU, SCADA, SM, and dynamic and static are combined to feed the static estimation results to the dynamic estimation and prediction step for replacement correction. When SM real-time measurement data is collected, static estimation is performed based on PMU, SCADA, and SM. The simulation results show that the joint framework scans the active distribution network at millisecond rate and captures its dynamics, estimates the steady state of the active distribution network at a second-level rate, integrates multi-source asynchronous data with slow time scale, improves the dynamic estimation accuracy within the framework, and meets the millisecond-level real-time monitoring requirements of active distribution network.