Intelligent control of doubly‐fed induction generator systems using PIDNNs |
| |
| Authors: | Faa‐Jeng Lin Jonq‐Chin Hwang Kuang‐Hsiung Tan Zong‐Han Lu Yung‐Ruei Chang |
| |
| Affiliation: | 1. Department of Electrical Engineering, National Central University, Chungli 320, Taiwan;2. Department of Electrical Engineering, National Taiwan University of Science and Technology, Taipei 106, Taiwan;3. Department of Electrical and Electronic Engineering, Chung Cheng Institute of Technology, National Defense University, Taoyuan 335, Taiwan;4. Engineering Technology & Facility Operation Division, Institute of Nuclear Energy Research, Taoyuan 335, Taiwan |
| |
| Abstract: | An intelligent control for a stand‐alone doubly‐fed induction generator (DFIG) system using a 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 field‐oriented control to produce 3‐phase stator voltages with constant magnitude and frequency at different rotor speeds. Moreover, the grid 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 grid side converters to improve the transient and steady‐state responses of the DFIG system for different operating conditions. Both the network structure and online learning algorithm are introduced in detail. Finally, the feasibility of the proposed control scheme is verified through experimentation. Copyright © 2011 John Wiley and Sons Asia Pte Ltd and Chinese Automatic Control Society |
| |
| Keywords: | Doubly‐fed induction generator field‐oriented control proportional‐integral‐derivative neural network |
|
|
