Enhanced Maximum Exponential Square State Estimator based on Simulation-Hyperparameter Optimization

The maximum exponential square (MES) provides an effective method for robust state estimation. However, its estimation effect is greatly influenced by the window width. This letter first shows that it is generally difficult to determine a proper window width for an MES estimator a priori. Then, an e...

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Bibliographic Details
Published in:IEEE transactions on power systems 2023-11, Vol.38 (6), p.1-4
Main Authors: Wang, Xuzhuo, Liu, Yitong, Li, Zhengshuo, Sun, Shumin
Format: Article
Language:English
Subjects:
Algorithms
Atmospheric measurements
Computational modeling
hyperparameter optimization
maximum exponential square
Optimization models
Particle measurements
Particle swarm optimization
Robustness (mathematics)
State estimation
Training
Weight measurement
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Summary:The maximum exponential square (MES) provides an effective method for robust state estimation. However, its estimation effect is greatly influenced by the window width. This letter first shows that it is generally difficult to determine a proper window width for an MES estimator a priori. Then, an enhanced MES state estimator is proposed for which the window width is determined by a bilevel simulation-hyperparameter optimization model. An efficient algorithm is proposed to solve this model. Numerical tests demonstrate that the proposed enhanced MES state estimator achieves superior performance compared to that of the MES estimator and other robust state estimators both with and without bad data.
ISSN:0885-8950
1558-0679
DOI:10.1109/TPWRS.2023.3304133