Visual MMC Open Circuit Fault Real-Time Rapid Detection System

Abstract

With the widespread use of modular multi-stage converters, the demands on their stability are increasing. In particular, problems such as open-circuit and short-circuit faults in their submodules have also attracted considerable attention from all walks of life. On this basis, a machine learning-based fault self-test and sub-module tracking strategy as well as innovative machine learning algorithms are proposed. Starting from the output characteristics of the sub-module, the harmonic components are analysed, the eigenvalues of the current system sub-module during normal operation and during faults are extracted, the eigenvalues are quickly categorised, and after categorisation, a new support vector machine model is put into place for machine learning. The trained machine model is finally embedded on top of the MCU integrated system and a communication transmission module is added on top of it, which can quickly determine the fault item in time when the system is running fault and reduce the maintenance cost later.