A Single-Point Measurement Framework for Robust Cyber-Attack Diagnosis in Smart Microgrids Using Dual Fractional-Order Feature Analysis

TL;DR

This paper introduces FO-MADS, a low-latency, single-sensor cyber-attack diagnosis framework for smart microgrids that leverages fractional-order features and robust training to accurately detect and localize attacks.

Contribution

It proposes a novel single-sensor diagnostic scheme using dual fractional-order features and a hierarchical classifier, improving robustness and accuracy in cyber-attack detection in microgrids.

Findings

Achieves over 92% accuracy across various attack scenarios.

Effectively localizes affected inverters and isolates faulty switches.

Maintains high detection performance under noise and data manipulation.

Abstract

Cyber-attacks jeopardize the safe operation of smart microgrids. At the same time, existing diagnostic methods either depend on expensive multi-point instrumentation or stringent modelling assumptions that are untenable under single-sensor constraints. This paper proposes a Fractional-Order Memory-Enhanced Attack-Diagnosis Scheme (FO-MADS) that achieves low-latency fault localisation and cyber-attack detection using only one VPQ (Voltage-Power-Reactive-power) sensor. 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 prioritise the most challenging samples. Experiments on a four-inverter 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 smart microgrids.