Related Experiment Video
Updated: Jun 13, 2025
06:04
Experimental Investigation of the Hierarchical Control in DC Microgrids Using a Real-time Simulator
Published on: February 14, 2025
260
Design and implementation of a universal converter for microgrid applications using approximate dynamic programming
K Suresh1, E Parimalasundar2, B Hemanth Kumar2
1Department of Electrical and Electronics Engineering, Christ Deemed to Be University, Bangalore, India.
Scientific Reports
|September 8, 2024
Summary
This study presents a novel universal DC-DC and DC-AC converter for microgrids, designed with Approximate Dynamic Programming and Artificial Neural Networks (ADP-ANN). It efficiently handles battery and AC power, reducing component needs for versatile energy applications.
Area of Science:
- Electrical Engineering
- Power Electronics
- Artificial Intelligence
Background:
- Microgrids require versatile power conversion solutions.
- Existing converters often have component redundancy and limited operational modes.
- Efficient integration of diverse energy sources (battery, AC) is crucial.
Purpose of the Study:
- To introduce a novel universal DC-DC and DC-AC converter design.
- To enhance efficiency and reduce component count in microgrid power conversion.
- To leverage Approximate Dynamic Programming and Artificial Neural Networks (ADP-ANN) for optimal control.
Main Methods:
- Design of a universal converter utilizing identical terminals and switches for chopper and inverter functions.
- Implementation of Approximate Dynamic Programming and Artificial Neural Networks (ADP-ANN) for control.
- Development and testing of a 3000W prototype across five operational modes.
Main Results:
- The converter demonstrated efficient operation with both DC (battery) and AC inputs.
- Minimal switch usage and component redundancy were achieved.
- Experimental validation confirmed system robustness and adaptability in various microgrid scenarios.
Conclusions:
- The novel universal converter offers reduced complexity and increased efficiency for microgrids.
- ADP-ANN integration enables optimal performance and adaptability.
- Potential applications include microgrids, electric vehicles, and renewable energy systems.
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