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