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

Li, S and Alonso, E and Fu, X and Fairbank, M and Jaithwa, I and Wunsch, DC (2015) Hardware Validation for Control of Three-Phase Grid-Connected Microgrids Using Artificial Neural Networks. In: 12th International Conference on Applied Computing 2015, ? - ?, Maynooth, Ireland.

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Abstract

This paper presents a strategy for controlling inverter-interfaced DERs within a microgrid using an artificial neural network. The neural network implements a dynamic programming algorithm and is trained with a new Levenberg-Marquardt backpropagation algorithm. Hardware experiments were conducted to evaluate the performance of the neural network vector control method. They showed that the neural network control technique performs well for DER converter control if the controller output voltage is below the converter?s PWM saturation limit. If the controller?s output voltage exceeds the PWM saturation limit, the neural network controller automatically turns into a state by maintaining a constant dc-link voltage as its first priority, while meeting the reactive power control demand as soon as possible. Under variable, unbalanced, and distorted system conditions, the neural network controller is stable and reliable.

Item Type: Conference or Workshop Item (Paper)
Additional Information: Published proceedings: _not provided_
Uncontrolled Keywords: microgrid, distributed energy sources, neural network control, dynamic programming, Levenberg-Marquardt backpropagation
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions: Faculty of Science and Health > Computer Science and Electronic Engineering, School of
Depositing User: Jim Jamieson
Date Deposited: 05 Aug 2016 14:42
Last Modified: 17 Aug 2017 17:24
URI: http://repository.essex.ac.uk/id/eprint/17374

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