Open circuit fault diagnosis technique for inverter switches and gate drive malfunction

Open circuit faults (OCFs) in voltage source inverters (VSIs) can significantly affect their performance and reliability. In this paper, a novel fault diagnosis technique (FDT)is presented for the detection and classification of two types of OCFs in VSIs: gate drive malfunction (GDM) and open switch fault (OSF). the effect of these OCFs on the output current of the VSI is analysed, this shows that they can be identified and distinguished using the average and root mean square (RMS) ratio of the current parameters. The proposed FDT is simple to implement and can identify switch faults with quick response, without the need for additional equipment. In this work the authors adopted the ensemble bagged tree classification method to detect and classify the GDM and OSF, the results show the credibility of the proposed technique in identifying different open circuit faults

Multiple Open Switch Fault Diagnosis of Three Phase Voltage Source Inverter Using Ensemble Bagged Tree Machine Learning Technique

Three-phase converters based on insulated-gate bipolar transistors (IGBTs) are widely used in various industrial applications. Faults in IGBTs can significantly affect the operation and safety of the power electronic equipment and loads. It is critical to accurately detect power inverter faults as soon as they occur to ensure system availability and high-power quality. This study provides a novel integration of signal and data-driven fault-diagnosis approaches for detecting open-circuit switch faults in three-phase inverters. The proposed technique uses the average root-mean-square (RMS) ratio of the phase current as the key extraction feature. This feature can be used to estimate the fault types and faulty switches (es) irrespective of changes in the running load. Ensemble-bagged machine learning classification was used to accurately predict the faulty switch of the inverter. The results demonstrate the ability of the proposed fault diagnosis technique to identify single-, double-, and triple-switch fault (s). The experimental results also attested to the simulation of multiple fault diagnosis. A unique feature of this technique is its ability to estimate faulty switches under various inverter-operating conditions