Abstract
This paper details the hardware implementation of a Universal Converter controlled by an Artificial Neural Network (ANN), utilizing key components such as six Insulated Gate Bipolar Transistors (IGBTs), two inductors, and two capacitors for energy storage and voltage smoothing. A Digital Signal Processor (DSP) serves as the core controller, processing real-time input and feedback signals, including voltage and current measurements, to dynamically manage five operational modes: rectifier buck, inverter boost, DC-DC buck, DC-DC boost, and AC voltage control. The pre-trained ANN algorithm generates pulse-width modulation (PWM) signals to control the switching of the IGBTs, optimizing timing and duty cycles for efficient operation. The system effectively accommodates both AC and DC inputs, ensuring stable outputs with minimal ripple by dynamically selecting the appropriate mode based on load requirements. Experimental results demonstrated that the ANN controller maintained total harmonic distortion (THD) below 5% in rectifier and inverter modes while achieving an overall efficiency of 94–96% in DC-DC modes. The controller’s capability to adapt to real-time feedback significantly improved power conversion quality and reduced switching losses. This study confirms the efficacy of the ANN-controlled Universal Converter in meeting the demands of modern power systems through versatile and adaptive control.
Published Version
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call