Vector Control of Grid-Connected Power Electronic Converter using Artificial Neural Networks - Research & Economic Development - The University of Alabama

Vector Control of Grid-Connected Power Electronic Converter using Artificial Neural Networks

The Problem:

Renewable and electric power system applications generally require a three-phase grid-connected DC/AC voltage-source pulse width modulated (PWM) converter. The PWM converter interfaces between AC and DC systems. Current converter systems are limited in the ability to provide consistent performance in renewable and electric power system applications.

The Solution:

Researchers at The University of Alabama have developed a neural-network-based optimal control strategy for vector control of a grid-connected rectifier/inventor in renewable and electric power system applications.  The system includes a grid-connected PWM converter connected between an electrical grid and an energy source. In additional, the overall approach includes a control system, which can include a nester-loop controller controls reactive power.

Configuration of an AC machine in PMSG wind turbine
Configuration of an AC machine in PMSG wind turbine

 

 

 

 

 

 

 

 

Benefits:

• Could be used for grid integration of renewable energies, variable speed wind turbine control, or electrified railways and AC/DC/AC PWM converter controls of motors and drives.
• Improves the current unsymmetrical distribution of variation.
• Examines the action time of the converter and optimizes the action time sequence to improve effectiveness of the converter.

VIEW PATENT INFORMATION HERE

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Patent Information:

For Information, Contact:

Lynnette Scales
Administrative Assistant
The University of Alabama
(205) 348-5433
liscales@ua.edu

Inventors:

Shuhui Li
Keywords: