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http://hdl.handle.net/2080/1098
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| 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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| bs1.pdf | | 230Kb | Adobe PDF | View/Open |
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