Open AccessHybrid Signal Processing and Soft Computing approaches to Power System Frequency Estimation
Author(s) -
Pravat Kumar Ray,
Bidyadhar Subudhi,
A. M. Panda
Publication year - 2012
Publication title -
international journal of computer and communication technology
Language(s) - English
Resource type - Journals
eISSN - 2231-0371
pISSN - 0975-7449
DOI - 10.47893/ijcct.2012.1155
Subject(s) - electric power system , computer science , power (physics) , signal processing , artificial neural network , signal (programming language) , algorithm , soft computing , digital signal processing , artificial intelligence , physics , quantum mechanics , computer hardware , programming language
Dynamic variation in power system frequency is required to be estimated for implementing the correcting measures. This paper presents power system frequency estimation by using RLS-Adaline and KF-Adaline algorithms. In the proposed hybrid approaches the weights of the Adaline are updated using RLS/KF algorithms. Frequency of power system signal is estimated from final updated weights of the Adaline. The performances of the proposed algorithms are studied through simulations for several critical cases that often arise in a power system. These studies show that the KF-Adaline algorithm is superior over the RLS-Adaline in estimating power system frequency. Studies made on experimental data also support the superiority.