F2GAN: A Feature-Feedback Generative Framework for Reliable AI-Based Fault Diagnosis in Inverter-Dominated Microgrids

Abstract

Enhancing the reliability of AI based fault diagnosis in inverter dominated microgrids requires diverse and statistically balanced datasets. However, the scarcity and imbalance of high fidelity fault data, especially for rare inverter malfunctions and extreme external line faults, limit dependable model training and validation. This paper introduces a unified framework that models a detailed inverter dominated microgrid and systematically generates multiple internal and external fault scenarios to mitigate data scarcity and class imbalance. An enhanced generative model called F2GAN (Feature Feedback GAN) is developed to synthesize high dimensional tabular fault data with improved realism and statistical alignment. Unlike conventional GANs, F2GAN integrates multi level feedback based on mean variance, correlation, and feature matching losses, enabling the generator to refine output distributions toward real fault feature spaces. The generated datasets are evaluated through quantitative and qualitative analyses. Train on Synthetic, Test on Real (TSTR) experiments demonstrate strong generalization of machine learning classifiers trained exclusively on F2GAN samples. The framework is validated on a hardware-in-the-loop (HIL) fault diagnosis platform integrated with a real time simulator and graphical interface, achieving 100 % diagnostic accuracy under real-time testing. Results confirm that F2GAN effectively bridges the gap between simulated and real world microgrid fault datasets