Cascade transformer multilevel inverters (CT-MLI) are DC–AC converters used in medium and high power applications to provide standardized AC output. Despite their numerous advantages and robustness, these devices are highly susceptible to fault events because of their high amount of components. Therefore, early failure detection enables turning off the power system avoiding the propagation of the fault to the connected loads. Beyond that, converter operation can be reconfigured to tolerate the fault and activate a fail lag facilitating the subsequent corrective maintenance. The techniques proposed so far required several sensors, which is not practical. Therefore, in this study, we propose an automatic fault detection algorithm for cascade multilevel inverters based on pattern recognition, that only requires a sensor located at the output of the inverter. Naive Bayes, decision tree, nearest neighbor, and support vector machine were tested as classifiers using cross validation. The proposed method showed high detection accuracy when all the obtained descriptors were employed, being the K-NN the classifier showing superior performance. Furthermore, an evaluation was developed to determine the minimum number of descriptors required for the effective operation of the detection system, reducing the computational costand simplifying its implementation. The method was validated by using simulation results obtained from a multilevel inverter circuit model.
This work describes in detail a computational tool designed to study performance indicators of a four-stage transformer-based single-phase cascaded multilevel inverter. The proposed system integrates simulation, on-line measurement, control and signal processing providing automating testing functionality to optimize the performance of the inverter with base on indicators such as Total Harmonic Distortion (THD), partial and global efficiency and power balance between the stages. The computational component of the tool was developed in LabVIEW providing not only didactic interactivity with the user through the Human-Machine Interface (HMI) but also a reliable interconnection with the power converter and the instruments of the experimental setup. The hardware component was developed integrating the power converter prototype, an acquisition card and electronic circuits providing measurement, conditioning, digital control and gate driving functions. Experimental results obtained from automatic tests are presented showing potentiality of the tool to support research activities related with this type of power converters.