Optimal Active Fault Detection in Inverter-Based Grids

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 brief, we construct optimal perturbation sequences for use with the multiple-model Kalman filter (MMKF). 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 trade-off between detection and degradation of the control performance, and that the method is robust to parameter variations.