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Vector Control of a Grid-Connected Rectifier/Inverter Using an Artificial Neural Network

Li, S., Fairbank, M., Wunsch, D. C. & Alonso, E. (2012). Vector Control of a Grid-Connected Rectifier/Inverter Using an Artificial Neural Network. Paper presented at the IEEE International Joint Conference on Neural Networks (IEEE IJCNN 2012), 1783-1789, 10-15-2012, Brisbane, Australia. doi: 10.1109/IJCNN.2012.6252614


- 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. This paper investigates how to mitigate such problems using a neural network to control a grid-connected rectifier/inverter. The neural network implements a dynamic programming (DP) algorithm and is trained using backpropagation through time. The performance of the DP-based neural controller is studied for typical vector control conditions and compared with conventional vector control methods. The paper also investigates how varying grid and power converter system parameters may affect the performance and stability of the neural control system. Future research issues regarding the control of grid-connected converters using DP-based neural networks are analyzed.

Publication Type: Conference or Workshop Item (Paper)
Additional Information: © 2012 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Publisher Keywords: grid-connected rectifier/inverter, decoupled vector control, renewable energy conversion systems, neural controller, dynamic programming, backpropagation through time
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Departments: School of Science & Technology > Computer Science
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