Support Vector Machine State Estimation. Issue 6 (12th May 2019)

• Record Type: Journal Article
• Authors: Kirinčić, Vedran; Čeperić, Ervin; Vlahinić, Saša; Lerga, Jonatan
• Is Part Of: Applied artificial intelligence. Volume 33:Issue 6(2019)
• Page Start: 517
• Page End: 530
• Publication Date: 2019-05-12
• Subjects: Artificial intelligence -- Periodicals; 006.3
• DOI: 10.1080/08839514.2019.1583452
• Languages: English
• ISSNs: 0883-9514

Abstract

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.