Diagnosis of Open-Switch Faults in Grid-Tied Three-Level NPC Inverters With Parameter Uncertainty Using Variable Forgetting Factor Bias-Compensation Recursive Least Squares

Tackling the challenge of open-switch (OS) fault diagnostics in grid-tied three-level neutral point clamped (NPC) inverters with parameter uncertainty, this paper introduces a fault diagnosis method that integrates a variable forgetting factor bias-compensation recursive least squares (VFFBCRLS) algorithm with a novel discrete disturbance sliding mode observer (DSMO) for three-level inverters. The proposed approach initially employs a VFFBCRlS algorithm to obtain the uncertain parameters of the inverter. Building upon this foundation, a novel discrete DSMO is introduced to obtain the output currents rapidly and accurately. Then, an adaptive fault detection variable is constructed based on the norm of the residual between the measured and the estimated currents, ensuring the accuracy and robustness of the detection algorithm. Finally, a precise identification of OS faults in grid-tied inverters is achieved through the establishment of a localization mechanism. The hardware-in-the-loop (HIL) test results provide validation for the efficacy and robustness of the proposed method.