Control of Three-Phase Grid-Connected Microgrids Using Artificial Neural Networks

Shuhui, L., Fu, X., Jaithwa, I., Alonso, E., Fairbank, M. & Wunsch, D. C. (2015). Control of Three-Phase Grid-Connected Microgrids Using Artificial Neural Networks. Paper presented at the 7th International Joint Conference on Computational Intelligence (IJCCI 2015), 12-11-2015 - 14-11-2015, Lisbon, Portugal.

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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.

Item Type: Conference or Workshop Item (Paper)
Additional Information: © 2015, Elsevier. Licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions: School of Informatics > Department of Computing

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