| 考虑量测坏数据的发电机动态状态估计方法 |
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| 引用本文： | 马安安,江全元,熊鸿韬,陆海清.考虑量测坏数据的发电机动态状态估计方法[J].电力系统自动化,2017,41(14):140-146. |
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| 作者姓名： | 马安安 江全元 熊鸿韬 陆海清 |
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| 作者单位： | 浙江大学电气工程学院, 浙江省杭州市 310027,浙江大学电气工程学院, 浙江省杭州市 310027,国网浙江省电力公司电力科学研究院, 浙江省杭州市 310014,国网浙江省电力公司电力科学研究院, 浙江省杭州市 310014 |
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| 基金项目： | 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) |
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| 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。
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| 关 键 词： | 动态状态估计 机电暂态 无迹卡尔曼滤波 坏数据 |
| 收稿时间： | 2016/8/19 0:00:00 |
| 修稿时间： | 2017/4/28 0:00:00 |
Dynamic State Estimation Method for Generator Considering Measurement of Bad Data |
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| MA Anan,JIANG Quanyuan,XIONG Hongtao and LU Haiqing.Dynamic State Estimation Method for Generator Considering Measurement of Bad Data[J].Automation of Electric Power Systems,2017,41(14):140-146. |
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| Authors: | MA Anan JIANG Quanyuan XIONG Hongtao and LU Haiqing |
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| Affiliation: | College of Electrical Engineering, Zhejiang University, Hangzhou 310027, China,College of Electrical Engineering, Zhejiang University, Hangzhou 310027, China,Electric Power Research Institute of State Grid Zhejiang Electric Power Company, Hangzhou 310014, China and Electric Power Research Institute of State Grid Zhejiang Electric Power Company, Hangzhou 310014, China |
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| 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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| Keywords: | dynamic state estimation electromechanical transient unscented Kalman filter bad data |
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