Authors:
Shuhui Li
1
;
Xingang Fu
1
;
Ishan Jaithwa
1
;
Eduardo Alonso
2
;
Michael Fairbank
2
and
Donald C. Wunsch
3
Affiliations:
1
The University of Alabama, United States
;
2
City University London, United Kingdom
;
3
Missouri University of Science and Technology, United States
Keyword(s):
Neural Network Control, Microgrid, Distributed Energy Resources, Grid-Connected Converter.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Artificial Intelligence and Decision Support Systems
;
Biomedical Engineering
;
Biomedical Signal Processing
;
Complex Artificial Neural Network Based Systems and Dynamics
;
Computational Intelligence
;
Computational Neuroscience
;
Enterprise Information Systems
;
Health Engineering and Technology Applications
;
Human-Computer Interaction
;
Methodologies and Methods
;
Neural Network Software and Applications
;
Neural Networks
;
Neurocomputing
;
Neurotechnology, Electronics and Informatics
;
Pattern Recognition
;
Physiological Computing Systems
;
Sensor Networks
;
Signal Processing
;
Soft Computing
;
Theory and Methods
Abstract:
A microgrid consists of a variety of inverter-interfaced distributed energy resources (DERs). A key issue is
how to control DERs within the microgrid and how to connect them to or disconnect them from the microgrid
quickly. This paper presents a strategy for controlling inverter-interfaced DERs within a microgrid using an
artificial neural network, which implements a dynamic programming algorithm and is trained with a new
Levenberg-Marquardt backpropagation algorithm. Compared to conventional control methods, our neural
network controller exhibits fast response time, low overshoot, and, in general, the best performance. In
particular, the neural network controller can quickly connect or disconnect inverter-interfaced DERs without
the need for a synchronization controller, efficiently track fast-changing reference commands, tolerate system
disturbances, and satisfy control requirements at grid-connected mode, islanding mode, and their transition.