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Stefan cel Mare
University of Suceava
Faculty of Electrical Engineering and
Computer Science
13, Universitatii Street
Suceava - 720229
ROMANIA

Print ISSN: 1582-7445
Online ISSN: 1844-7600
WorldCat: 643243560
doi: 10.4316/AECE


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LATEST NEWS

2017-Jun-14
Thomson Reuters published the Journal Citations Report for 2016. The JCR Impact Factor of Advances in Electrical and Computer Engineering is 0.595, and the JCR 5-Year Impact Factor is 0.661.

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  3/2017 - 14
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Wind Speed Prediction with Wavelet Time Series Based on Lorenz Disturbance

ZHANG, Y. See more information about ZHANG, Y. on SCOPUS See more information about ZHANG, Y. on IEEExplore See more information about ZHANG, Y. on Web of Science, WANG, P. See more information about  WANG, P. on SCOPUS See more information about  WANG, P. on SCOPUS See more information about WANG, P. on Web of Science, CHENG, P. See more information about  CHENG, P. on SCOPUS See more information about  CHENG, P. on SCOPUS See more information about CHENG, P. on Web of Science, LEI, S. See more information about LEI, S. on SCOPUS See more information about LEI, S. on SCOPUS See more information about LEI, S. on Web of Science
 
Click to see author's profile on See more information about the author on SCOPUS SCOPUS, See more information about the author on IEEE Xplore IEEE Xplore, See more information about the author on Web of Science Web of Science

Download PDF pdficon (1,200 KB) | Citation | Downloads: 286 | Views: 1,088

Author keywords
ARMA model, Lorenz system, renewable energy, wavelet decomposition, wind speed prediction

References keywords
wind(14), energy(10), speed(8), prediction(8), time(6), power(6), systems(5), series(5), forecasting(5), models(4)
Blue keywords are present in both the references section and the paper title.

About this article
Date of Publication: 2017-08-31
Volume 17, Issue 3, Year 2017, On page(s): 107 - 114
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2017.03014
Web of Science Accession Number: 000410369500014
SCOPUS ID: 85030118150

Abstract
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Due to the sustainable and pollution-free characteristics, wind energy has been one of the fastest growing renewable energy sources. However, the intermittent and random fluctuation of wind speed presents many challenges for reliable wind power integration and normal operation of wind farm. Accurate wind speed prediction is the key to ensure the safe operation of power system and to develop wind energy resources. Therefore, this paper has presented a wavelet time series wind speed prediction model based on Lorenz disturbance. Therefore, in this paper, combined with the atmospheric dynamical system, a wavelet-time series improved wind speed prediction model based on Lorenz disturbance is proposed and the wind turbines of different climate types in Spain and China are used to simulate the disturbances of Lorenz equations with different initial values. The prediction results show that the improved model can effectively correct the preliminary prediction of wind speed, improving the prediction. In a word, the research work in this paper will be helpful to arrange the electric power dispatching plan and ensure the normal operation of the wind farm.


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Faculty of Electrical Engineering and Computer Science
Stefan cel Mare University of Suceava, Romania


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