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

TL;DR

This paper introduces a novel high-impedance fault detection method using parameter error modeling and state estimation, leveraging phasor measurements and hypothesis testing to improve detection accuracy.

Contribution

It proposes a new detection scheme combining parameter error modeling with weighted least squares state estimation for high-impedance faults.

Findings

Effective detection of high-impedance faults demonstrated in Simulink case studies.

Utilizes wide-area power flow measurements for real-world fault detection.

Error distribution analysis helps distinguish faults from measurement errors.

Abstract

This paper presents a model for detecting high-impedance faults (HIFs) using parameter error modeling and a two-step per-phase weighted least squares state estimation (SE) 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 $\chi^2$ Hypothesis Testing applied to the composed measurement error (CME). 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 SE 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 HIF identification. Further, this error spread related to the HIF will be clearly discerned from measurement error. Case studies are performed on the 33-Bus Distribution System in Simulink.