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Dynamic State Estimation of a Synchronous Machine Using PMU Data: A Comparative Study

机译:使用PMU数据的同步电机动态状态估计:一个比较研究

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Accurate information about dynamic states is important for efficient control and operation of a power system. This paper compares the performance of four Bayesian-based filtering approaches in estimating dynamic states of a synchronous machine using phasor measurement unit data. The four methods are extended Kalman filter, unscented Kalman filter, ensemble Kalman filter, and particle filter. The statistical performance of each algorithm is compared using Monte Carlo methods and a two-area-four-machine test system. Under the statistical framework, robustness against measurement noise and process noise, sensitivity to sampling interval, and computation time are evaluated and compared for each approach. Based on the comparison, this paper makes some recommendations for the proper use of the methods.
机译:有关动态状态的准确信息对于电力系统的有效控制和操作非常重要。本文比较了四种基于贝叶斯滤波方法在使用相量测量单位数据估算同步电机动态状态时的性能。这四种方法是扩展卡尔曼滤波器,无味卡尔曼滤波器,集成卡尔曼滤波器和粒子滤波器。使用蒙特卡洛方法和两区四机测试系统比较了每种算法的统计性能。在统计框架下,评估并比较了每种方法对测量噪声和过程噪声的鲁棒性,对采样间隔的敏感性以及计算时间。在比较的基础上,本文对正确使用这些方法提出了一些建议。

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