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dc.contributor.authorSubudhi, B-
dc.contributor.authorRay, P K-
dc.identifier.citationIEEE TENCON 2009, 23-26 November 2009, Singaporeen
dc.descriptionCopyright belongs to TENCONen
dc.description.abstractThis paper presents combined RLS-Adaline (Recursive Least Square and adaptive linear neural network) and KF-Adaline (Kalman Filter Adaline) approach for the estimation of harmonic components of a power system. The neural estimator is based on the use of an adaptive perceptron comprising a linear adaptive neuron called Adaline. Kalman Filter and Recursive Least Square algorithms carry out the weight updating in Adaline. The estimators’ track the signal corrupted with noise and decaying DC components very accurately. Adaptive tracking of harmonic components of a power system can easily be done using these algorithms. The proposed approaches are tested both for static and dynamic signal. Out of these two, the KF-Adaline approach of tracking the fundamental and harmonic components is better.en
dc.format.extent236197 bytes-
dc.subjectHarmonics Estimationen
dc.subjectAdaptive Linear Neural Networks(Adaline)en
dc.subjectDiscrete Fourier Transform(DFT)en
dc.subjectFast Fourier Transform(FFT)en
dc.titleEstimation of Power System Harmonics Using Hybrid RLS-Adaline and KF-Adaline Algorithmsen
Appears in Collections:Conference Papers

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