High-Impedance Non-Linear Fault Detection via Eigenvalue Analysis with low PMU Sampling Rates

This work presents a hybrid data-driven and physics-based framework for high-impedance fault detection in power systems. An innovative method based on eigenvalue analysis is expanded and validated. Phasor Measurement Unit data is used to estimate eigenvalues corresponding to the powerlines being monitored. Eigenvalue statistics are then tracked and evaluated. Faults are detected as they drive eigenvalues outside of their normal zones. This technique holds several advantages over contemporary techniques: It utilizes technology that is already deployed in the field, it offers a significant degree of generality, and so far it has displayed a very high-level of sensitivity without sacrificing accuracy. Validation is performed in the form of simulations based in the IEEE 13 Node System and non-linear fault models. Test results are encouraging, indicating potential for real-life applications.

Automated summary

This work presents a hybrid data-driven and physics-based framework for detecting high-impedance faults in power systems. It expands and validates an innovative method based on eigenvalue analysis. Phasor Measurement Unit data is used to estimate eigenvalues corresponding to the monitored powerlines, and faults are detected by tracking and evaluating the statistics of these eigenvalues. This technique offers advantages in terms of deployed technology, generality, and sensitivity without sacrificing accuracy, as demonstrated through simulations and encouraging test results.