Optimal Active Fault Detection in Inverter-Based Grids

TL;DR

This paper develops optimal perturbation sequences for active fault detection in inverter-based grids, enhancing detection confidence and speed while balancing control performance degradation.

Contribution

It introduces a method to construct optimal perturbations for use with the Multiple Model Kalman Filter, improving fault detection in converter-based grids.

Findings

Optimal input sequences increase fault detection confidence.

Detection time is reduced with the proposed method.

The approach is robust to parameter variations.

Abstract

Ground faults in converter-based grids can be difficult to detect because, unlike in grids with synchronous machines, they often do not result in large currents. One recent strategy is for each converter to inject a perturbation that makes faults easier to distinguish from normal operation. In this paper, we construct optimal perturbation sequences for use with the Multiple Model Kalman Filter. The perturbations maximize the difference between faulty and fault-free operation while respecting limits on performance degradation. Simulations show that the optimal input sequence increases the confidence of fault detection while decreasing detection time. It is shown that there is a tradeoff between detection and degradation of the control performance, and that the method is robust to parameter variations.