Uncertainty Error Modeling for Non-Linear State Estimation With Unsynchronized SCADA and μPMU Measurements

Distribution systems of the future smart grid require enhancements to the reliability of distribution system state estimation (DSSE) in the face of low measurement redundancy, unsynchronized measurements, and dynamic load profiles. Micro phasor measurement units (mu PMUs) facilitate co-synchronized measurements with high granularity, albeit at an often prohibitively expensive installation cost. Supervisory control and data acquisition (SCADA) measurements can supplement mu PMU data, although they are received at a slower sampling rate. Further complicating matters is the uncertainty associated with load dynamics and unsynchronized measurements-not only are the SCADA and mu PMU measurements not synchronized with each other, but the SCADA measurements themselves are received at different time intervals with respect to one another. This paper proposes a non-linear state estimation framework which models dynamic load uncertainty error by updating the variances of the unsynchronized measurements, leading to a time-varying system of weights in the weighted least squares state estimator. Case studies are performed on the 33-Bus Distribution System in MATPOWER, using Ornstein-Uhlenbeck stochastic processes to simulate dynamic load conditions.

Automated summary

This paper proposes a non-linear state estimation framework for the future smart grid distribution systems. It aims to enhance the reliability of distribution system state estimation by utilizing micro phasor measurement units (mu PMUs) and supervisory control and data acquisition (SCADA) measurements. The framework also considers the uncertainty of load dynamics and unsynchronized measurements by updating the variances of the unsynchronized measurements, resulting in improved accuracy of state estimation.