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.

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

The University of Alabama Research Office of Innovation and Commercialization (OIC) is a non-profit corpo­ration that is responsible for commercializing University of Alabama technologies and for supporting University research. At OIC, we seek parties that are interested in learning more about our technologies and commercialization opportunities, and we welcome any inquiries you may have.