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Robust Direct Power Control Schemes of DFIGs Driven by Variable-speed Wind Turbines

机译：变速风力发电机驱动的双馈发电机的鲁棒直接功率控制方案

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摘要

Despite its several advantages, a classic direct power control (DPC) technique of doubly fed induction generators (DFIGs) driven by variable-speed wind turbines has some drawbacks such as high power ripples and variable switching frequency. In this paper, two robust controllers are designed to improve the classical DPC performance without complicating the overall scheme. First, an integral sliding mode controller (ISMC) is designed to regulate the stator active and reactive powers. Two integral switching functions are selected for controlling stator active and reactive powers. The idea of total sliding mode controller is selected to avoid reaching phase stability problem. Second, a diagonal recurrent neural network (DRNN) controller is designed and trained based on DPC. The DRNN has several advantages compared to the classical static neural networks such as recurrence and simple construction. Simple off-line back-propagation algorithm is proposed to train the proposed DRNN. The stability of the proposed ISMC and DRNN controller is proved using the Lyapunov stability theorem. The grid side converter is controlled based on the DPC principle to ensure both constant DC-link voltage and grid side reactive power. The feasibility of the proposed DPC schemes is validated by simulation studies on a 1.5-MW wind power generation system. The performance of the proposed schemes is compared with a conventional DPC scheme under different operating conditions.
机译：尽管具有多个优点，但由变速风力涡轮机驱动的双馈感应发电机（DFIG）的经典直接功率控制（DPC）技术具有一些缺点，例如高功率纹波和可变开关频率。在本文中，设计了两个鲁棒的控制器来提高经典DPC的性能，而不会使整个方案复杂化。首先，设计了集成滑模控制器（ISMC）来调节定子有功和无功功率。选择了两个集成的开关功能来控制定子有功功率和无功功率。选择总滑模控制器的想法是为了避免达到相位稳定性问题。其次，基于DPC设计并训练了对角递归神经网络（DRNN）控制器。与经典的静态神经网络相比，DRNN具有多个优点，例如递归和简单构造。提出了一种简单的离线反向传播算法来训练提出的DRNN。利用Lyapunov稳定性定理证明了所提出的ISMC和DRNN控制器的稳定性。电网侧转换器基于DPC原理进行控制，以确保恒定的直流母线电压和电网侧无功功率。通过对1.5兆瓦风力发电系统的仿真研究，验证了提出的DPC方案的可行性。在不同的操作条件下，将所提方案的性能与常规DPC方案进行了比较。

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