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Squirrel-cage induction generator system using intelligent control for wind power applications

机译:Squirrel-Cage诱导发电机系统使用智能控制风电应用

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An intelligent controlled three-phase squirrel-cage induction generator (SCIG) system for grid-connected power application using wavelet fuzzy neural network (WFNN) is proposed in this study. First, the indirect field-oriented mechanism is implemented for the control of the SCIG system. Then, an AC/DC power converter and a DC/AC power inverter are developed to convert the electric power generated by a three-phase SCIG from variable-voltage and variable-frequency to constant-voltage and constant-frequency. Moreover, the intelligent WFNN controller is proposed for both the AC/DC power converter and DC/AC power inverter to improve the transient and steady-state responses of the SCIG system at different operating conditions. Three online trained WFNNs using backpropagation learning algorithm are implemented as the tracking controllers for the DC-link voltage of the AC/DC power converter and the active power and reactive power outputs of the DC/AC power inverter. Furthermore, the network structure and the online learning algorithm of the WFNN are introduced in detail. Finally, some experimental results are provided to demonstrate the effectiveness of the proposed SCIG system.
机译:本研究提出了一种用于使用小波模糊神经网络(WFNN)的电网连接电力应用的智能控制的三相鼠笼式感应发电机(SCIG)系统。首先,为突发系统的控制实现了面向间面向的机制。然后,开发了AC / DC功率转换器和DC / AC功率逆变器以将由三相突出的电力从可变电压和可变频率转换为恒压和恒定频率。此外,提出了智能WFNN控制器,适用于AC / DC功率转换器和DC / AC功率逆变器,以改善突发系统在不同的操作条件下的瞬态和稳态响应。使用Backpropagation Least算法的三个在线训练的WFNNS作为AC / DC功率转换器的直流电电压的跟踪控制器和DC / AC电源逆变器的有功功率和无功功率输出。此外,详细介绍了WFNN的网络结构和在线学习算法。最后,提供了一些实验结果来证明所提出的突发系统的有效性。

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