考虑量测坏数据的发电机动态状态估计方法

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考虑量测坏数据的发电机动态状态估计方法

更新时间：2025-08-13

- 考虑量测坏数据的发电机动态状态估计方法
- Automation of Electric Power Systems Vol. 41, Issue 14, Pages: 140-146(2017)
- 作者机构：
  
  浙江大学电气工程学院,浙江省,杭州市,310027
- 作者简介：
- 基金信息：
- DOI：
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- Published：2017
- 稿件说明：
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MA Anan. [J]. Automation of electric power systems, 2017, 41(14): 140-146.
DOI：

MA Anan. [J]. Automation of electric power systems, 2017, 41(14): 140-146. DOI：

摘要

随着相量测量单元(PMU)的广泛应用，基于PMU的发电机动态状态估计的研究越来越受到重视。如果存在量测坏数据，动态状态估计的滤波效果会受到严重的影响。首先介绍了一种基于无迹卡尔曼滤波(UKF)的发电机动态状态估计方法。然而，由于PMU数据的质量不高，为解决坏数据的问题，推导残差方程得出时变的阈值，再通过一种迭代检测的方法确定坏数据的测点位置。对于坏数据对应的量测，算法将其剔除后重新进行一次估计，以修正估计结果。算例结果表明，该方法能有效抑制量测坏数据对发电机动态状态估计的影响。

Abstract

With the wide adoption of phasor measurement unit(PMU)in energy management systems

research on dynamic state estimator for synchronous machine based on PMU is attracting more and more attention. Should there exist bad data

the effect of filtering would be seriously affected. First

an algorithm for dynamic state estimation based on unscented Kalman filter is described. But as the PMU data is of poor quality

to solve problem

the time-varying residual threshold is found by deriving the residual equation. Then

the position of bad data measuring point is determined by an iterative detection method. The corresponding measurement of the bad data is ruled out and repeated for correction of the estimation. Simulation results show that the proposed algorithm can effectively restrain the influence of bad data on state estimation.

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