Cyber-Resilient Fault Diagnosis Methodology in Inverter-Based Resource-Dominated Microgrids with Single-Point Measurement

TL;DR

This paper introduces FO-MADS, a novel fault diagnosis scheme for inverter-based microgrids that uses only a single measurement point and employs fractional derivatives and adversarial training to improve cyber-physical resilience.

Contribution

It proposes a cost-effective, single-point measurement fault diagnosis method with a dual fractional-order feature library and a hierarchical classifier, enhanced by adversarial training for robustness.

Findings

Achieves over 92% accuracy across various fault and attack scenarios.

Maintains 96.7% accuracy under attack-free conditions.

Demonstrates effectiveness on a four-inverter microgrid testbed.

Abstract

Cyber-attacks jeopardize the safe operation of inverter-based resource-dominated microgrids (IBR-dominated microgrids). At the same time, existing diagnostic methods either depend on expensive multi-point instrumentation or stringent modeling assumptions that are untenable under single-point measurement constraints. This paper proposes a Fractional-Order Memory-Enhanced Attack-Diagnosis Scheme (FO-MADS) that achieves timely fault localization and cyber-resilient fault diagnosis using only one VPQ (voltage, active power, reactive power) measurement point. FO-MADS first constructs a dual fractional-order feature library by jointly applying Caputo and Gr\"unwald-Letnikov derivatives, thereby amplifying micro-perturbations and slow drifts in the VPQ signal. A two-stage hierarchical classifier then pinpoints the affected inverter and isolates the faulty IGBT switch, effectively alleviating class imbalance. Robustness is further strengthened through Progressive Memory-Replay Adversarial Training (PMR-AT), whose attack-aware loss is dynamically re-weighted via Online Hard Example Mining (OHEM) to prioritize the most challenging samples. Experiments on a four-inverter IBR-dominated microgrid testbed comprising 1 normal and 24 fault classes under four attack scenarios demonstrate diagnostic accuracies of 96.6% (bias), 94.0% (noise), 92.8% (data replacement), and 95.7% (replay), while sustaining 96.7% under attack-free conditions. These results establish FO-MADS as a cost-effective and readily deployable solution that markedly enhances the cyber-physical resilience of IBR-dominated microgrids.