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

• URL: http://arxiv.org/abs/2507.06890v1
• Date: Wed, 09 Jul 2025 14:27:40 GMT
• Title: A Single-Point Measurement Framework for Robust Cyber-Attack Diagnosis in Smart Microgrids Using Dual Fractional-Order Feature Analysis
• Authors: Yifan Wang,
• Abstract summary: Cyber-attacks jeopardize the safe operation of smart microgrids.<n>This paper proposes a Fractional-Order Memory-Enhanced Attack-Diagnosis Scheme (FO-MADS)<n>It achieves low-latency fault localisation and cyber-attack detection using only one VPQ (Voltage-Power-Reactive-power) sensor.
• Score: 7.752913924557325
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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