Control of Doubly-Fed Induction Generator System Using PIDNNs

2010 Ninth International Conference on Machine Learning and Applications Pub Date : 2010-12-12 DOI:10.1109/ICMLA.2010.104

F. Lin, Jonq-Chin Hwang, K. Tan, Zong-Han Lu, Yung-Ruei Chang

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引用次数: 7

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.

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用pidnn控制双馈感应发电机系统

提出了一种基于比例-积分-导数神经网络(PIDNN)的单机双馈感应发电机(DFIG)智能控制系统。该系统既可以作为独立供电系统，也可以作为电网故障时的应急供电系统，适用于所有分同步、同步和超同步工况。转子侧变换器采用磁场定向控制，在不同转子转速下产生定幅恒频三相定子电压。此外，定子侧转换器也采用磁场定向控制，主要用于维持直流链路电压的大小。在此基础上，针对转子侧变换器和定子侧变换器提出了智能PIDNN控制器，以改善DFIG系统在不同工况下的暂态和稳态响应。详细介绍了网络结构和在线学习算法。最后，通过实验验证了所提控制方案的可行性。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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2010 Ninth International Conference on Machine Learning and Applications

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