考虑量测坏数据的发电机动态状态估计方法

发布时间：2018-01-04 18:30

本文关键词：考虑量测坏数据的发电机动态状态估计方法　出处：《电力系统自动化》2017年14期 　论文类型：期刊论文

【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 phasor measurement unit (PMU) has been widely used, the research dynamic state estimation of the PMU generator has attracted more and more attention. If there is based on measurement of bad data, the filtering effect of dynamic state estimation is seriously affected. This paper introduces a kind of unscented filter based on Calman wave (UKF) generator dynamic state estimation methods. However, due to the quality of PMU data is not high, in order to solve the problem of bad data, derive residual equation for time-varying threshold, and then through the method of iterative detection to determine the location of measuring points of bad data. The bad data corresponding to the measurement algorithm, to be removed after once again to estimate correct estimation results. Numerical results show that this method can effectively suppress the effect of measurement of bad data on generator dynamic state estimation.

【作者单位】：浙江大学电气工程学院;国网浙江省电力公司电力科学研究院;
【基金】：国家自然科学基金资助项目(51137003) 国家电网公司科技项目(XT71-16-034)~~
【分类号】：TM31
【正文快照】： 上网日期:2017-04-10。0引言机电暂态模型广泛应用于电力系统暂态问题中,便于系统紧急状况下进行稳定控制[1]。目前数据采集与监控(SCADA)系统采样频率较慢,采集的数据并不能用于机电模型下的状态估计。随着广域测量系统(WAMS)的应用,提供描述发电机运行状态的变量值变为可能

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