Single-Submodule Open-Circuit Fault Diagnosis for a Modular Multi-level Converter Using Artificial Intelligent-based Techniques

Abstract

Modular multi-level converters (MMCs) are one of the promising topologies in recent years for medium or high voltage applications. They are considered as the next generation DC/AC converters for medium/high voltage (MV/HV) motor drive applications due to their transform-less structures, high efficiency and modularity. Reliability is one of the most important challenges in MMCs, since a large number of power switching devices are used and each of these devices can be considered as a potential failure point. It is significant to detect and locate the fault accurately and apply appropriate protections in a timely manner. This paper investigates the behavior of the failure submodules and healthy submodules and use artificial neural network (ANN) classification algorithms for single-submodule open-circuit fault diagnosis. The ANN algorithm is implemented in field programmable gate array (FPGA) and the ANN parameters training is completed offline in Matlab. Experimental results of the proposed fault diagnosis for a single-module open-circuit fault are presented in this paper.