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Design and Implementation of universal converter using ANN controller
K Suresh1, E Parimalasundar2, A Arunraja1
1Department of EEE(1), Department of ECE (3), Christ University, Bangalore, India.
This study presents an Artificial Neural Network (ANN) controlled Universal Converter, demonstrating efficient and adaptive power conversion. The ANN controller ensures stable outputs and high efficiency across various modes, meeting modern power system demands.
Area of Science:
- Electrical Engineering
- Power Electronics
- Artificial Intelligence
Background:
- Modern power systems require versatile and efficient power conversion solutions.
- Traditional converters often face limitations in adaptability and efficiency across diverse operating conditions.
Purpose of the Study:
- To detail the hardware implementation of a Universal Converter controlled by an Artificial Neural Network (ANN).
- To evaluate the ANN controller's performance in managing multiple operational modes and ensuring power quality.
Main Methods:
- Hardware implementation using Insulated Gate Bipolar Transistors (IGBTs), inductors, and capacitors.
- Control by a Digital Signal Processor (DSP) executing a pre-trained ANN algorithm for pulse-width modulation (PWM) signal generation.
- Dynamic mode selection based on real-time feedback for five operational modes: rectifier buck, inverter boost, DC-DC buck, DC-DC boost, and AC voltage control.
Main Results:
- ANN controller maintained total harmonic distortion (THD) below 5% in rectifier and inverter modes.
- Achieved overall efficiency of 94-96% in DC-DC modes.
- Demonstrated adaptive control, improved power conversion quality, and reduced switching losses.
Conclusions:
- The ANN-controlled Universal Converter effectively meets modern power system demands.
- The system offers versatile and adaptive control for efficient power conversion.
- The ANN controller significantly enhances power quality and operational efficiency.
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