Uncertainty Error Modeling for Non-Linear State Estimation With Unsynchronized SCADA and $\mu$PMU Measurements

TL;DR

This paper introduces a non-linear state estimation method for distribution systems that accounts for uncertainties from unsynchronized SCADA and $$PMU measurements, improving reliability under dynamic load conditions.

Contribution

It proposes a novel framework that models load uncertainty errors by updating measurement variances, enabling better handling of unsynchronized and dynamic data in state estimation.

Findings

Effective modeling of load uncertainty improves estimation accuracy.

Time-varying weights enhance robustness to measurement synchronization issues.

Case studies demonstrate improved performance on the 33-Bus system.

Abstract

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.