卡尔曼滤波器
估计员
稳健性(进化)
控制理论(社会学)
离群值
相量测量单元
指数函数
高斯噪声
电力系统
数学
相量
数学优化
计算机科学
算法
功率(物理)
统计
人工智能
数学分析
生物化学
化学
控制(管理)
物理
量子力学
基因
作者
Tengpeng Chen,He Ren,Po Li,G.A.J. Amaratunga
出处
期刊:IEEE Transactions on Instrumentation and Measurement
[Institute of Electrical and Electronics Engineers]
日期:2022-01-01
卷期号:71: 1-10
被引量:1
标识
DOI:10.1109/tim.2022.3189743
摘要
Even though the noise model applied in power system dynamic state estimation is usually assumed to be Gaussian, this is not the case due to the unknown system inputs, influence from the communication channel noise and the outliers generated by phasor measurement units (PMUs). In this paper a robust power system dynamic state estimation (DSE) method combining a robust exponential absolute value based estimator and the unscented Kalman filter together is proposed under non-Gaussian noise. Based on the quadratic function and the exponential absolute value function, robust exponential absolute value based estimator is derived, further mitigating the effects of bad data or outliers. The influence function is utilized to calculate the state estimation error covariance of the proposed robust DSE method. The simulation results on the IEEE 39-bus system verify the robustness and effectiveness of the proposed dynamic state estimation method.
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