The rapid expansion of electric vehicle (EV) fast-charging infrastructure has increased the demand for reliable and real-time fault diagnosis techniques in power electronic converters. The Vienna rectifier and the DC/DC converter are widely adopted in DC charging stations due to their high efficiency, low harmonic distortion, and reduced component count; however, they remain vulnerable to open-circuit and short-circuit faults in semiconductor devices and DC-link components. Signal-based diagnostic methods that rely on grid-synchronized estimators have gained attention due to their low computational burden and suitability for real-time implementation. Among these, Second-Order Generalized Integrator (SOGI)-based Frequency-Locked Loop (FLL) structures can be used for frequency, amplitude, and harmonic estimation. Despite their widespread use in synchronization and power quality monitoring, a systematic comparison of different SOGI-based structures for converter fault diagnosis—particularly under real-time operating conditions—has not yet been reported. This work addresses this gap by comparatively evaluating multiple SOGI-FLL variants for fault diagnosis in rectifiers used in EV DC charging applications.

This paper will present a comparative analysis of different SOGI-based grid estimation structures. Each structure is integrated into an identical fault diagnosis framework based on total harmonic distortion (THD) estimation, frequency deviation, and amplitude variation of the measured signals. The diagnostic method is applied to a three-phase Vienna rectifier model subjected to representative open-circuit and short-circuit fault scenarios affecting switching devices and DC-link components. To ensure a fair comparison, all SOGI variants are tuned using equivalent nominal parameters and evaluated under identical operating conditions. Performance metrics include fault detection time, sensitivity to fault severity, robustness to noise and grid disturbances, and computational complexity. To validate real-time feasibility and practical applicability, the proposed framework is implemented and experimentally tested using a Typhoon Hardware-in-the-Loop (HIL) simulation platform, enabling realistic fault injection, switching behavior, and sensor non-idealities.

The comparative results demonstrate that all investigated SOGI-based structures are capable of detecting converter faults using THD-based indicators; however, significant differences are observed in detection speed, noise immunity, and stability under distorted grid conditions. Hardware-in-the-Loop experiments confirm the simulation findings and validate the real-time operation of the diagnostic algorithms, with detection delays remaining within a few tens of milliseconds for all studied fault cases.

This study provides a systematic and experimentally validated comparison of SOGI structures for fault diagnosis in Vienna rectifiers. The results highlight clear trade-offs between estimation accuracy, robustness, and computational complexity, offering practical design guidelines for selecting suitable SOGI configurations in real-time diagnostic applications. The successful implementation on a Typhoon HIL platform confirms the feasibility of SOGI-based THD estimation as a reliable and interpretable diagnostic tool for EV DC charging systems. The findings support the extension of this methodology to other converter topologies and real-world charging infrastructure.