Neural Net Grid Control
University of Alabama, AL, United States
The innovation consists of artificial neural network vector control technology for control of power electronic converters for a large number of application areas, including renewables, microgrids, high-voltage dc transmission, and smart grids, to significantly improve overall system reliability, enhance volt-var management, and reduce harmonics and converter switching frequency.
Primary Application Area: Energy & Efficiency
Technology Development Status: Prototype
Technology Readiness Level: TRL 3
FIGURES OF MERIT
Value Proposition: Our innovation will overcome the deficiencies of conventional standard vector controllers. Technically, our technology would result in optimal controller and best performance for grid integration of renewable resources and for operation and management of electric power systems.
Organization Type: Early-stage Startup (Seed)
Showcase Booth #: 23T
GOVT/EXTERNAL FUNDING SOURCES
Government Funding/Support to Date: National Science Foundation
Primary Sources of Funding: Federal Grant, University
Looking for: Both Funding and Development Partners