Diagnosis of Faulty IGBT Switches in Multi-Level Inverter Using ANFIS Technique

Abstract

Consistent performance of multilevel inverters is frequently downgraded by the fault in power electronic components such as IGBT switches, which is a major obstacle in operation of motor and drive. Identification of open & short circuit problems of IGBT switches in multilevel inverters is constantly a broadly explored area. Several procedures have been suggested to recognise the faulty IGBT switches of inverters based on current measurement and analysis. Bearing in mind the downsides of load current centred fault detection techniques, in this paper, inverter output voltage is considered for fault estimation purpose, because it is independent of load variations. Adaptive neuro-fuzzy inference system (ANFIS) is a dominant technology in the grouping of difficult patterns. In the present work, classification of faulty IGBT switches is carried out through ANFIS technique. Results show that ANFIS reduces number of inputs and size of the network and hence avoids the usage of any in-between numerical processes to decrease the size of the network and in turn lessens the processing time. The values of root mean square error lies in the range of  acceptable limit and it shows the effectiveness of the ANFIS based approach in the identification of both open & short circuit problems of IGBT switches of multilevel inverters.