Vector Control of a Grid-connected Rectifier/Inverter Using an Artificial Neural Network
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. 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.
Recommended Citation
S. Li et al., "Vector Control of a Grid-connected Rectifier/Inverter Using an Artificial Neural Network," Proceedings of the International Joint Conference on Neural Networks, Institute of Electrical and Electronics Engineers (IEEE), Jan 2012.
The definitive version is available at https://doi.org/10.1109/IJCNN.2012.6252614
Meeting Name
2012 Annual International Joint Conference on Neural Networks, IJCNN '12/ 2012 IEEE World Congress on Computational Intelligence, WCCI 2012 (2012: Jun. 10-15, Brisbane, Australia)
Department(s)
Electrical and Computer Engineering
International Standard Book Number (ISBN)
978-1467314909
Document Type
Article - Conference proceedings
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2012 Institute of Electrical and Electronics Engineers (IEEE), All rights reserved.
Publication Date
01 Jan 2012
