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Please use this identifier to cite or link to this item: http://hdl.handle.net/2080/1098

Title: Estimation of Power System Harmonics Using Hybrid RLS-Adaline and KF-Adaline Algorithms
Authors: Subudhi, B
Ray, P K
Keywords: Harmonics Estimation
Adaptive Linear Neural Networks(Adaline)
Discrete Fourier Transform(DFT)
Fast Fourier Transform(FFT)
Issue Date: 2009
Publisher: IEEE
Citation: IEEE TENCON 2009, 23-26 November 2009, Singapore
Abstract: This 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.
Description: Copyright belongs to TENCON
URI: http://hdl.handle.net/2080/1098
Appears in Collections:Conference Papers

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