Abstract This paper presents a design for the novel intelligent control (NIC) of the stabilization of a doubly fed induction generator (DFIG)-based wind turbine. The intelligent control scheme was developed to control the rotor side voltage source converter that allowed independent control of the generated active and reactive power as well as the rotor speed to track the maximum power point (MPP). Moreover, the NIC was used to reduce the interaction between the real and reactive power flow and improve the performance. The proposed NIC consisted of an improved recurrent fuzzy neural network (IRFNN) and ant colony optimization with genetic algorithms (GACO). GACO was adopted to adjust the learning rates to improve the online learning capability of the IRFNN. Furthermore, an NIC controller was proposed for both the rotor and stator side converters to improve the steady-state and transient voltage stability of the DFIG system for different operating conditions. In addition, the method is highly simple to implement. Finally, the proposed approach stabilized the internal dynamics through rotor voltage control and improved the dynamic behavior of the DFIG after clearing a fault.

中文翻译：

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提高瞬态控制性能的双馈感应发电机驱动风力发电机新型智能控制器设计

摘要 本文提出了一种基于双馈感应发电机 (DFIG) 的风力涡轮机稳定的新型智能控制 (NIC) 设计。开发了智能控制方案来控制转子侧电压源换流器，允许独立控制产生的有功和无功功率以及转子速度以跟踪最大功率点 (MPP)。此外，NIC 用于减少有功功率流和无功功率流之间的相互作用并提高性能。所提出的 NIC 由改进的递归模糊神经网络 (IRFNN) 和使用遗传算法的蚁群优化 (GACO) 组成。采用 GACO 调整学习率以提高 IRFNN 的在线学习能力。此外，为转子和定子侧转换器提出了一个 NIC 控制器，以改善不同运行条件下双馈发电机系统的稳态和瞬态电压稳定性。此外，该方法实施起来非常简单。最后，所提出的方法通过转子电压控制稳定了内部动态，并在清除故障后改善了双馈电机的动态行为。