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Dynamic state estimation of a permanent magnet synchronous generator-based wind turbine

机译:基于永磁同步发电机的风力发电机的动态状态估计

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摘要

Precise modelling, control and monitoring of machines improve the overall stability and operation of power systems. State estimation reduces the effect of noises and presents all hidden variables, which can be beneficial especially in non-linear control. In this study, first, a complete 16th-order state space model is developed for a grid-connected permanent magnet synchronous generator-based wind turbine (PMSG-WT). Due to non-linearity of the model, extended Kalman filtering is utilised for state estimation. A phasor measurement unit connected to permanent magnet synchronous generator bus is utilised to provide required electrical values for state estimation in a synchronous manner. In order to evaluate the accuracy of the proposed algorithm, four different cases are studied corroborating the robustness of the proposed algorithm in the presence of high noises or in the case of large disturbances. Some comparisons are also provided with another non-linear model proposed recently for PMSG-WT, which verifies the advantages of the proposed model. Such results are expected to improve stability of wind farms especially in the case of large disturbances, which can lead to enhancing the whole network stability.
机译:机器的精确建模,控制和监视可改善电力系统的整体稳定性和运行。状态估计可减少噪声的影响并显示所有隐藏变量,这在非线性控制中尤其有用。在本研究中,首先,为基于电网的永磁同步发电机风力发电机组(PMSG-WT)开发了完整的16阶状态空间模型。由于模型的非线性,扩展卡尔曼滤波被用于状态估计。连接到永磁同步发电机总线的相量测量单元用于以同步方式提供状态估计所需的电气值。为了评估所提出算法的准确性,研究了四种不同情况,以证实所提出算法在高噪声存在或大干扰情况下的鲁棒性。最近对PMSG-WT提出的另一个非线性模型也提供了一些比较,这验证了所提出模型的优势。预期这种结果将改善风电场的稳定性,尤其是在存在较大干扰的情况下,这可能导致增强整个网络的稳定性。

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