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Abstract
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Article Information:
Harmonics Estimation Investigation using a New Fuzzy Adeline Neural Network Method
S. Sajedi, A. Noruzi, H. Jafari Rezabeyglo, F. Khalifeh, T. Karimi and Z. Khalifeh
Corresponding Author: S. Sajedi
Submitted: 2011 September, 29
Accepted: 2011 November, 04
Published: 2012 April, 01 |
Abstract:
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Artificial Intelligence (AI) techniques, particularly the neural networks, are recently having
significant impact on power electronics and motor drives. Neural networks have created a new and advancing
frontier in power electronics, which is already a complex and multidisciplinary technology that is going through
dynamic evolution in the recent years. In this paper, a new method is proposed to approximate the harmonics
symmetric components exist in three-phase distribution system. In the proposed method, the amplitude and
phase components of the fundamental harmonic and the harmonics of each phase can be extracted. The positive,
negative, and the zero sequences are obtained from the harmonics existed in this system performing an
independent Fortescue Transform for each harmonic. The proposed estimator is simulated in
MATLAB/Simulink in order to assess the functionality of the method. The simulation results show higher
efficiency of the proposed method in symmetric components estimation of an artificial three-phase signal
harmonics and its higher performance in extracting such components in compare with that of the processing
unit structure in a sample three-phase system under unbalance and nonlinear loads existence. The proposed
system can be applied in power quality monitoring and be used as a control strategy in custom power devices,
according to its advantageous such as fast respond, high accuracy, and low calculation extent.
Key words: Fortescue transform, fuzzy ADALINE neural network, harmonics symmetric components, power quality, , ,
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Cite this Reference:
S. Sajedi, A. Noruzi, H. Jafari Rezabeyglo, F. Khalifeh, T. Karimi and Z. Khalifeh, . Harmonics Estimation Investigation using a New Fuzzy Adeline Neural Network Method. Research Journal of Applied Sciences, Engineering and Technology, (07): 735-748.
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ISSN (Online): 2040-7467
ISSN (Print): 2040-7459 |
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