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A mathematical framework for incorporating asynchronous, time-delayed measurements into distribution system state estimation
Dissertation

A mathematical framework for incorporating asynchronous, time-delayed measurements into distribution system state estimation

Keith A. Zuckerman
Doctor of Philosophy (Ph.D.), Drexel University
Jun 2026
DOI:
https://doi.org/10.17918/00011497
pdf
Zuckerman_Keith_202611.95 MB
PDF Embargoed Access, Embargo ends: 30 Jun 2027

Abstract

Power systems
Distribution system state estimation is a fundamental analysis tool for a variety of advanced distribution automation and control applications. The primary inputs to the state estimator are power system measurements. These measurements are sampled asynchronously, and the communication infrastructure used to transmit them to the control center introduces delays. Because of these delays, the measurements may not accurately reflect the state of the system at the present time. This thesis focuses on the development of a mathematical framework for incorporating asynchronous, time-delayed measurements into distribution system state estimation. By leveraging short-term injection forecasts, measurement models are developed to mitigate temporal discrepancies. A measurement value adjustment model is proposed to update delayed measurements to more accurately reflect the true value of the measured quantity at the state estimation time. Extending this framework, a measurement weight selection model is developed to embed spatial and temporal uncertainty into the estimation process. These measurement models are incorporated into a weighted least squares static state estimation formulation. Simulations are presented demonstrating the implementation of the proposed models on a real 394-bus distribution system. An 8-bus, 17-node hardware platform for emulating asynchronous, time-delayed measurements is also presented, and experiments are conducted to validate the proposed mathematical framework. The simulation and experimental results show significant improvements in state estimation accuracy relative to traditional approaches.

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