风力发电拓扑中双馈感应发电机的参数估计

J. Bekker, H. Vermeulen
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引用次数: 4

摘要

近年来,风力发电系统的装机容量急剧增加,因此增加了对运行应用的有效模型实现的需求,例如模拟系统行为和电网相互作用,识别和量化老化效应以及执行状态监测。虽然传统系统的模型拓扑已经很好地建立起来,但从在线测量中确定参数值的方法还需要进一步研究,特别是在状态监测等应用中。本文介绍了一种用于经典风力发电系统的双馈感应发电机模型参数估算的探索性研究结果,该系统包括风力发电机叶片、齿轮箱和简化的电网模型。本研究使用专用的Matlab实现了各种系统组件的c代码s函数模型,并将其编译为Simulink库。这种方法确保了高效的模型实现和快速的仿真时间,这是参数估计过程通常需要的。给出了对ABC和DQ发电机模型实施的案例研究结果,其中发电机要么孤立运行,要么作为风力发电系统的一部分运行。该研究考虑两种摄动信号,即分别施加于转子角速度和定子电压的阶跃摄动。结果表明,利用这些模型实现、运行拓扑和扰动信号可以较好地估计发电机的电气参数。
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Parameter estimation of a doubly-fed induction generator in a wind generation topology
The installed capacity of wind generation systems has increased dramatically in recent years, thereby increasing the need for efficient model implementations for operational applications such as simulating system behaviour and grid interactions, identifying and quantifying ageing effects and performing condition monitoring. While the model topologies for conventional systems are well established, the methodologies for determining parameter values from online measurements require further research, especially for applications such as condition monitoring. This paper presents the results of an exploratory investigation to estimate the model parameters of a double-fed induction generator used in a classical wind generation system that includes a wind turbine blade, gearbox and simplified grid model. The investigation is conducted using dedicated Matlab implementations of C-code S-function models of the various system components, compiled as a Simulink library. This approach ensures highly efficient model implementations with fast simulation times as is typically required by parameter estimation processes. Results are presented for case studies performed on both ABC and DQ generator model implementations, with the generator either operated in isolation or as part of a wind generation system. The investigation considers two perturbation signals, namely step perturbations applied to the rotor angular velocity and stator voltages respectively. The results show that the electrical parameters of the generator can be estimated with good accuracy using these model implementations, operating topologies and perturbation signals.
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