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

机译:鼠笼式感应发电机系统,采用智能控制,适用于风力发电应用

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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:提出了一种基于小波模糊神经网络(WFNN)的智能化三相鼠笼式感应发电机(SCIG)系统,用于并网发电。首先,实现了间接的面向字段的机制来控制SCIG系统。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SCIG产生的电力从可变电压和可变频率转换成恒定电压和恒定频率。此外,针对交流/直流电源转换器和直流/交流电源逆变器,提出了智能WFNN控制器,以改善SCIG系统在不同工作条件下的瞬态和稳态响应。三个使用反向传播学习算法的经过在线训练的WFNN被用作跟踪控制器,用于跟踪AC / DC电源转换器的DC链路电压以及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SCIG系统的有效性。

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