Vector Control of a Grid-connected Rectifier/Inverter Using an Artificial Neural Network
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.
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
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)
Electrical and Computer Engineering
International Standard Book Number (ISBN)
Article - Conference proceedings
© 2012 Institute of Electrical and Electronics Engineers (IEEE), All rights reserved.