A Fast and Robust State Estimator Based on Exponential Function for Power Systems

In realistic power system state estimation, the distribution of measurement noise is usually assumed to be Gaussian while many researcher have verified that it can be non-Gaussian. In this paper, a new robust state estimator based on exponential absolute value function is proposed to address the non...

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Bibliographic Details
Published in:IEEE sensors journal 2022-03, Vol.22 (6), p.5755-5767
Main Authors: Chen, Tengpeng, Ren, He, Foo, Eddy Y. S., Sun, Lu, Amaratunga, Gehan A. J.
Format: Article
Language:English
Subjects:
Density measurement
Electric variables measurement
exponential absolute value function
Exponential functions
Influence functions
maximum likelihood estimation
Noise measurement
Outliers (statistics)
Phasor measurement unit
quadratic function
robust state estimator
Robustness
State estimation
System effectiveness
Systems design
Time measurement
Voltage measurement
Weight measurement
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Summary:In realistic power system state estimation, the distribution of measurement noise is usually assumed to be Gaussian while many researcher have verified that it can be non-Gaussian. In this paper, a new robust state estimator based on exponential absolute value function is proposed to address the non-Gaussian measurement noise and outliers. The influence function, a robust statistics tool, is used to obtain the state estimates to reduce its computational burden. A state estimation mean squared error formula of the proposed robust estimator is derived which can be used as a reference in the wide area monitoring system design or upgrade. Simulation results obtained from the IEEE 30-bus, 118-bus and 300-bus systems verify the effectiveness and robustness of the proposed robust estimator.
ISSN:1530-437X
1558-1748
DOI:10.1109/JSEN.2022.3143885