Open AccessPower System State Estimation Based on PLS-ELM
Author(s) -
Yang Guo,
Yongmei Zhang,
Rui Fang,
Kui Zhao,
Yuanyuan Sha
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1871/1/012022
Subject(s) - extreme learning machine , robustness (evolution) , computer science , electric power system , artificial intelligence , algorithm , machine learning , data mining , artificial neural network , power (physics) , biochemistry , chemistry , physics , quantum mechanics , gene
In order to effectively improve the computational accuracy and robustness of state estimation, a power system state estimation method based on partial least square (PLS) and extreme learning machine (ELM) is proposed in this paper, which combines artificial intelligence technology with grid data. In order to solve the problem of strong correlation between measurements, PLS is used to extract important information and select variables for each measurement, and the optimal variables are input into the ELM model, thus the PLS-ELM model of state quantities is established. Finally, this method is compared with other methods. Experimental results show that the proposed method reduces the complexity of the model, and has high estimation accuracy and strong robustness.