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Realization of State-Estimation-Based DFIG Wind Turbine Control Design in Hybrid Power Systems Using Stochastic Filtering Approaches

机译:基于随机滤波的混合动力系统基于状态估计的双馈风力发电机控制设计的实现

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摘要

This paper uses three popular stochastic filtering techniques to acquire the unmeasurable internal states of the doubly fed induction generator (DFIG) in order to realize the widely adopted control scheme, which involves the inaccessible state variable—stator flux. Filtering methods to be discussed in this paper include particle filter, unscented Kalman filter, and extended Kalman filter, where their mathematical algorithms are presented, their implementations in the DFIG wind farm connected to complex power systems are studied, and their performances are compared. The whole power system network topology is taken into consideration for the state estimation, but only local phasor measurement unit measurement data are required. The purpose of using different stochastic filtering techniques to estimate dynamic states of DFIG in power systems is to resolve the long-lasting issue of the unavailability of DFIG internal states used in the DFIG controller design.
机译:本文使用三种流行的随机滤波技术来获取双馈感应发电机(DFIG)的无法测量的内部状态,以实现被广泛采用的控制方案,该方案涉及不可访问的状态变量-定子磁通。本文讨论的滤波方法包括粒子滤波,无味卡尔曼滤波器和扩展卡尔曼滤波器,在此介绍了它们的数学算法,研究了它们在连接复杂电力系统的DFIG风电场中的实现,并比较了它们的性能。状态估计要考虑整个电力系统网络拓扑,但仅需要局部相量测量单元测量数据。使用不同的随机滤波技术来估计电力系统中DFIG的动态状态的目的是解决DFIG控制器设计中使用的DFIG内部状态不可用的长期问题。

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