Open circuit fault diagnosis and fault classification in multi-level inverter using fuzzy inference system

Multi-level inverters (MLIs) have been successfully used to integrated the renewable energy sources (RES) into microgrids. However, operation of MLI is affected when an open circuit fault (OCF) or a short occurs. Among these kinds faults, there high prevalence faults in MLI. Any must be identified and classified as soon possible maintain reliability power supply. This work focused on developing Fuzzy Inference System (FIS) for detecting classifying Cascaded H-Bridge Multi-Level Inverter (CHMLI), thereby improving diagnosis accuracy efficiency. In CHMLI, gate pulse generated by width modulation (PWM) technique. The Mamdani Logic Controller (FLC) identifies categorizes different OCFs. logic rules are designed simultaneously using fundamental Discrete Fourier components voltage current. Several combinations studied switches MLI, along with effect inception angle. Furthermore, test results support feasibility proposed fuzzy-based classification scheme practical context. A real-time simulation obtained help FPGA-based OPAL-RT 4510 demonstrates robustness effectiveness topology. All types locations considered multiple cases switch failure.