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Wang, C., Lizana, F. R., Li, Z., Peterchev, A. V., Goetz, S. M..  2017.  Submodule short-circuit fault diagnosis based on wavelet transform and support vector machines for modular multilevel converter with series and parallel connectivity. IECON 2017 - 43rd Annual Conference of the IEEE Industrial Electronics Society. :3239–3244.

The modular multilevel converter with series and parallel connectivity was shown to provide advantages in several industrial applications. Its reliability largely depends on the absence of failures in the power semiconductors. We propose and analyze a fault-diagnosis technique to identify shorted switches based on features generated through wavelet transform of the converter output and subsequent classification in support vector machines. The multi-class support vector machine is trained with multiple recordings of the output of each fault condition as well as the converter under normal operation. Simulation results reveal that the proposed method has high classification latency and high robustness. Except for the monitoring of the output, which is required for the converter control in any case, this method does not require additional module sensors.