@inproceedings{6b7a41f841964f4886dd49d6df4f5345,
title = "Control of doubly-fed induction generator system using PIDNNs",
abstract = "An intelligent control stand-alone doubly-fed induction generator (DFIG) system using proportional-integral-derivative neural network (PIDNN) is proposed in this study. This system can be applied as a stand-alone power supply system or as the emergency power system when the electricity grid fails for all sub-synchronous, synchronous and super-synchronous conditions. The rotor side converter is controlled using the field-oriented control to produce three-phase stator voltages with constant magnitude and frequency at different rotor speeds. Moreover, the stator side converter, which is also controlled using field-oriented control, is primarily implemented to maintain the magnitude of the DC-link voltage. Furthermore, the intelligent PIDNN controller is proposed for both the rotor and stator side converters to improve the transient and steady-state responses of the DFIG system for different operating conditions. Both the network structure and on-line learning algorithm are introduced in detail. Finally, the feasibility of the proposed control scheme is verified through experimentation.",
keywords = "Doubly-fed induction generator, Field-oriented control, Proportional- integral-derivative neural network",
author = "Lin, {Faa Jeng} and Hwang, {Jonq Chin} and Tan, {Kuang Hsiung} and Lu, {Zong Han} and Chang, {Yung Ruei}",
year = "2010",
doi = "10.1109/ICMLA.2010.104",
language = "???core.languages.en_GB???",
isbn = "9780769543000",
series = "Proceedings - 9th International Conference on Machine Learning and Applications, ICMLA 2010",
pages = "675--680",
booktitle = "Proceedings - 9th International Conference on Machine Learning and Applications, ICMLA 2010",
note = "9th International Conference on Machine Learning and Applications, ICMLA 2010 ; Conference date: 12-12-2010 Through 14-12-2010",
}