Improvement of IoT-based industrial motor drive including model predictive control and multilevel inverter

Induction motor which is one of the alternating current (AC) motors, is widely used in industrial applications. Speed and torque control of the induction motor is provided by generating the appropriate voltage and current using the inverter. Development studies on induction motor drives with multilevel inverter topologies, which have advantages such as lower total harmonic distortion, higher efficiency and smaller filter size compared to two level inverter topologies, are increasing day by day. Finite control set model predictive control (FCS-MPC) methods were developed for torque and flux control of the induction motor in this thesis. The neutral point clamped (NPC) and active neutral point clamped (ANPC) inverters have been designed as multilevel inverter topology. The remote monitoring and control feature of the induction motor drive developed with multilevel inverter is realized with an internet of things (IoT) based module. In addition, sensorless FCS-MPC control methods have been improved by obtaining the speed value of the controlled induction motor using sliding mode observer (SMO) and model reference adaptive system (MRAS) speed estimation methods.