Deep Learning-Enabled System Diagnosis in Microgrids: A Feature-Feedback GAN Approach

TL;DR

This paper introduces a novel two-stage system for detecting and classifying faults and cyberattacks in inverter-based microgrids, utilizing a feature-feedback GAN and traditional machine learning methods for enhanced robustness.

Contribution

It proposes a new feature-feedback GAN architecture for improved anomaly detection and integrates multiple machine learning classifiers for fault localization in microgrids.

Findings

F2GAN outperforms conventional GANs in fault detection accuracy.

The framework effectively distinguishes between physical faults and cyberattacks.

High accuracy in classifying single and multi-switch faults in simulated environments.

Abstract

The increasing integration of inverter-based resources (IBRs) and communication networks has brought both modernization and new vulnerabilities to the power system infrastructure. These vulnerabilities expose the system to internal faults and cyber threats, particularly False Data Injection (FDI) attacks, which can closely mimic real fault scenarios. Hence, this work presents a two-stage fault and cyberattack detection framework tailored for inverter-based microgrids. Stage 1 introduces an unsupervised learning model Feature Feedback Generative Adversarial Network (F2GAN), to distinguish between genuine internal faults and cyber-induced anomalies in microgrids. Compared to conventional GAN architectures, F2GAN demonstrates improved system diagnosis and greater adaptability to zero-day attacks through its feature-feedback mechanism. In Stage 2, supervised machine learning techniques, including Support Vector Machines (SVM), k-Nearest Neighbors (KNN), Decision Trees (DT), and Artificial Neural Networks (ANN) are applied to localize and classify faults within inverter switches, distinguishing between single-switch and multi-switch faults. The proposed framework is validated on a simulated microgrid environment, illustrating robust performance in detecting and classifying both physical and cyber-related disturbances in power electronic-dominated systems.