Kirinčić, Vedran and Čeperić, Ervin and Vlahinić, Saša and Lerga, Jonatan (2019) Support Vector Machine State Estimation. Applied Artificial Intelligence, 33 (6). pp. 517-530. ISSN 0883-9514
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Abstract
The power system state estimator based on the support vector machine (SVM) and the weighted least squares (WLS) method is presented in the paper. The WLS provides state estimations necessary for creating SVM model which is then used for state estimation. The developed algorithm was tested on the IEEE systems, and the performance indicators were calculated in order to compare the accuracy of estimation and the measurement error filtering. The results indicate that the proposed hybrid model outperforms the classical WLS-based state estimation in terms of accuracy and improves measurement error filtering in comparison to the classical estimator.
Item Type: | Article |
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Subjects: | STM Academic > Computer Science |
Depositing User: | Unnamed user with email support@stmacademic.com |
Date Deposited: | 19 Jun 2023 10:39 |
Last Modified: | 31 Oct 2023 06:35 |
URI: | http://article.researchpromo.com/id/eprint/1117 |