An Online Multiple Open-Switch Fault Diagnosis Method for T-Type Three-Level Inverters Based on Multimodal Deep Residual Filter Network

Abstract

With the development of the processing capacity of the embedded chip, it is possible to implement a machine learning algorithm in the embedded system. To achieve the fault status without poor portability, tricky threshold selection and complex rulemaking, this paper proposes a multi-modal deep residual filter network for online multiple open-switch fault diagnosis of T-type three-level inverter. It contains low-rank matrix fusion (LMF), deep residual filter network (DRFN), and cross transformer mechanism. The LMF fuses the voltage signal and the current signal for obtaining the unified representation. And then, the DRFN filters noise adaptively and extracts information effectively. Finally, the cross-transformer mechanism output the fault state of the T-type three-level inverter. The data sets consist of the dc-link voltage and load side current of the inverter control system. The data time window is selected as 20 ms. Through the real-time calculation of online monitored data, the experimental results show the effectiveness of the proposed fault diagnosis approach. Moreover, the accuracy of fault diagnosis obtains 99.18% and the average open-circuit fault diagnosis time is 21 ms.