A hybrid least squares-clonal selection based algorithm for harmonics estimation
SUMMARY This paper presents a new algorithm for harmonics estimation in power systems. Because of the nonlinearity of phases of sinusoids, the estimation of harmonic parameters is a nonlinear problem. However, nonlinear solving for amplitude estimation decreases speed of convergence. Thereby, hybrid...
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| Published in | International transactions on electrical energy systems Vol. 24; no. 1; pp. 1 - 15 |
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| Main Authors | , |
| Format | Journal Article |
| Language | English |
| Published |
Hoboken
Blackwell Publishing Ltd
01.01.2014
John Wiley & Sons, Inc |
| Subjects | |
| Online Access | Get full text |
| ISSN | 2050-7038 2050-7038 |
| DOI | 10.1002/etep.1676 |
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| Summary: | SUMMARY
This paper presents a new algorithm for harmonics estimation in power systems. Because of the nonlinearity of phases of sinusoids, the estimation of harmonic parameters is a nonlinear problem. However, nonlinear solving for amplitude estimation decreases speed of convergence. Thereby, hybrid methods decompose the harmonics estimation problem into two problems, linear for amplitude and nonlinear for phase. The objective of this paper is to introduce an accurate approach for harmonic parameters estimation. This approach is based on a stochastic search method, that is, clonal selection, to estimate the phases and a linear estimator, that is, least squares (LS), to estimate the amplitudes. This paper also indicates high accuracy of proposed algorithm in comparison with discrete Fourier transform and LS–Adaline (hybrid of LS and Adaline neural network) methods, especially in multiple frequency and highly noisy situations. Performance of the algorithm in noise rejecting even in interharmonics presence is shown by extracted simulation results of MATLAB (MathWorks Inc., Natick, MA, USA). Copyright © 2012 John Wiley & Sons, Ltd. |
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| Bibliography: | ArticleID:ETEP1676 ark:/67375/WNG-757H0R8T-X istex:F5E13A9DC36E6E59EC31121E8E76F301DC0D6765 ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 |
| ISSN: | 2050-7038 2050-7038 |
| DOI: | 10.1002/etep.1676 |