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Simulation Studies for Doubly-fed Wind Generator Based on Neural Network Internal Model Control

机译：基于神经网络内模型控制的双馈风力发电机仿真研究

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Wind power is a clean and renewable energy, and its exploitation is developing rapidly across the world. The Variable Speed Constant Frequency (VSCF) wind power generation system requires the doubly-fed wind generator (DFIG) to have high response as well as good robustness. DFIG will be affected both by internal disturbance of parametic variations and external disturbance of the load torque oscillation. The dynamics of the current response of the present PI controlled rotor may be in bad condition. In addition, the above disturbances reduce efficiency and stability of the wind turbine. Internal model control (IMC) is a kind of control strategy based on mathematical model to design the controller, it has advantages such as simply to design, robustness, convenient to research. Compared with PI control, IMC has a faster response, and is insensitive to the parameter variation and disturbances, but the IMC highly depends on accurate model of the controlled object. To solve this problem, combine the neural network and IMC to design the neural network internal model controller. The simulation indicates that the dynamic performance and the anti-interference ability of DFIG are improved.
机译：风力是一种干净，可再生能源，其开发正在全球迅速发展。变速恒定频率（VSCF）风力发电系统需要双馈风发电机（DFIG）具有高响应以及良好的鲁棒性。 DFIG将受到载荷变化的内部干扰和负载扭矩振荡的外部干扰的影响。当前PI控制转子的电流响应的动态可能处于不良状态。此外，上述干扰降低了风力涡轮机的效率和稳定性。内部模型控制（IMC）是一种基于数学模型设计控制器的控制策略，它具有简单的设计，坚固，方便的优点。与PI控制相比，IMC具有更快的响应，对参数变化和干扰不敏感，但IMC高度取决于受控对象的准确模型。为了解决这个问题，将神经网络和IMC组合设计神经网络内部模型控制器。模拟表明，改进了DFIG的动态性能和抗干扰能力。

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