Research Repository

Artificial neural networks for control of a grid-Connected rectifier/inverter under disturbance, dynamic and power converter switching conditions

Li, S and Fairbank, M and Johnson, C and Wunsch, DC and Alonso, E and Proao, JL (2014) 'Artificial neural networks for control of a grid-Connected rectifier/inverter under disturbance, dynamic and power converter switching conditions.' IEEE Transactions on Neural Networks and Learning Systems, 25 (4). 738 - 750. ISSN 2162-237X

[img]
Preview
Text
LFJWAP-ROC.pdf - Accepted Version

Download (2MB) | Preview

Abstract

Three-phase grid-connected converters are widely used in renewable and electric power system applications. Traditionally, grid-connected converters are controlled with standard decoupled d-q vector control mechanisms. However, recent studies indicate that such mechanisms show limitations in their applicability to dynamic systems. This paper investigates how to mitigate such restrictions using a neural network to control a grid-connected rectifier/inverter. The neural network implements a dynamic programming algorithm and is trained by using backpropagation through time. To enhance performance and stability under disturbance, additional strategies are adopted, including the use of integrals of error signals to the network inputs and the introduction of grid disturbance voltage to the outputs of a well-trained network. The performance of the neural-network controller is studied under typical vector control conditions and compared against conventional vector control methods, which demonstrates that the neural vector control strategy proposed in this paper is effective. Even in dynamic and power converter switching environments, the neural vector controller shows strong ability to trace rapidly changing reference commands, tolerate system disturbances, and satisfy control requirements for a faulted power system. © 2013 IEEE.

Item Type: Article
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: 19 Nov 2015 10:45
Last Modified: 30 Jan 2019 16:23
URI: http://repository.essex.ac.uk/id/eprint/15485

Actions (login required)

View Item View Item