Observability analysis of power distribution systems with distributed energy resources using correlational measurements

Abstract

In this thesis, an observability analysis framework is proposed for power distribution systems. The framework analyzes the sufficiency of the available measurements for monitoring the system. In the areas of the network where sufficient sensors have been deployed, the complex voltage at the nodes may be estimated. The framework also provides a metric that quantifies the accuracy of these voltage estimates. Due to the cost of sensors and the size of distribution systems, it is frequently the case that the available measurements are insufficient for complete observability of the system. In this thesis, the author proposes the use of Correlational Measurements (CMs) for improving distribution system observability by leveraging correlation between loads and between Distributed Energy Resources (DERs). Load correlation due to class-residential, commercial, industrial, etc.- is captured via correlational load measurements, the first type of CM. Injection correlation of DERs-such as wind turbines located in proximity-is captured via correlational DER measurements, the second type of CM. This thesis presents the CM formulation and derives modified node voltage and branch current based state estimators (NVSE and BCSE) accommodating CMs. Additionally, this thesis contains an on-line parameter estimation scheme for CMs that improves the accuracy of state estimates over time. The observability analysis framework with CMs is demonstrated through simulations on the IEEE 123 Node Distribution Test Feeder. It is shown that the introduction of CMs leads to improved observability of the system with only a 0.7% decrease in state estimation accuracy for NVSE and a 0.5% increase in state estimation accuracy for BCSE.