March 30, 2023
Conference Paper

High Impedance Fault Detection Through Quasi-Static State Estimation: A Parameter Error Modeling Approach

Abstract

This paper presents a model for detecting high impedance faults using parameter error modeling and a two step per-phase weighted-least squares state estimation process. The proposed scheme leverages the use of Phasor Measurement Units and synthetic measurements to identify per-phase power flow and injection measurements which indicate a parameter error through ?2 Hypothesis Testing applied to the composed measurement error. Although current and voltage waveforms are commonly analyzed for high-impedance fault detection, wide area power flow and injection measurements, which are already inherent to the state estimation process, also show promise for real-world high-impedance fault detection applications. The error distributions after detection share the measurement function error spread observed in proven parameter error diagnostics and can be applied to high-impedance fault identification. Further, this error spread across measurement functions related to the fault will be clearly discerned from measurement error. Case studies are performed on the IEEE 33-Bus Distribution System along with the proposed model in Simulink.

Published: March 30, 2023

Citation

Cooper A., A. Bretas, S. Meyn, and N.G. Bretas. 2023. High Impedance Fault Detection Through Quasi-Static State Estimation: A Parameter Error Modeling Approach. In IEEE Power & Energy Society Innovative Smart Grid Technologies Conference (ISGT 2023), January 16-19, 2023, Washington, DC, 1-5. Piscataway, New Jersey:IEEE. PNNL-SA-177113. doi:10.1109/ISGT51731.2023.10066369

Research topics