University of Tehran Journal of Solar Energy Research 2588-3097 9 1 2024 01 01 Fault Tolerant Multilevel Inverter Using Artificial Neural Network 1755 1762 95856 10.22059/jser.2024.359461.1309 EN Ehsan Najafi Qom university of technology Roozbeh Rajabi Qom university of technology Nima Bayat Qom university of technology Journal Article 2023 05 28 Inverters have been widely used in renewable energy as a means of converting extracted power to grid standards. However, this power electronics equipment is highly vulnerable to failures due to its complex architecture and components. One of the main sources of failure is semiconductor switches that are critically sensitive to abnormal conditions such as high voltage. With the advent of multilevel inverters, this concern has been raised considerably due to the increase in the number of switches. This paper has proposed a novel method with a neural network that can detect open circuit failure of switches and replace them with some new arrangement in the inverter so that it can run effectively. Simulations with MATLAB/Simulink for a seven level inverter, illustrate that with a switch failure, the multilevel inverter can work successfully. Results also demonstrate that this method is fast and can compensate for the output in less than two cycles. Therefore, it can be used in reliable multilevel applications in which the power flow should be achieved even if a semiconductor switch is broken. https://jser.ut.ac.ir/article_95856_d2065385e343687d5a3d36956a310673.pdf