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

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


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FEATURED ARTICLE

Improved Wind Speed Prediction Using Empirical Mode Decomposition, ZHANG, Y., ZHANG, C., SUN, J., GUO, J.
Issue 2/2018

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  3/2017 - 14
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 HIGH-IMPACT PAPER 

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
 
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Download PDF pdficon (1,200 KB) | Citation | Downloads: 495 | Views: 2,707

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.


References | Cited By

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SCOPUS® Times Cited: 25
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Cited-By CrossRef

[1] Multi-sensors based condition monitoring of rotary machines: An approach of multidimensional time-series analysis, Wang, Teng, Lu, Guoliang, Yan, Peng, Measurement, ISSN 0263-2241, Issue , 2019.
Digital Object Identifier: 10.1016/j.measurement.2018.10.089
[CrossRef]

[2] A Validity Index for Fuzzy Clustering Based on Bipartite Modularity, Liu, Yongli, Zhang, Xiaoyang, Chen, Jingli, Chao, Hao, Journal of Electrical and Computer Engineering, ISSN 2090-0147, Issue , 2019.
Digital Object Identifier: 10.1155/2019/2719617
[CrossRef]

[3] A Method Based on Lorenz Disturbance and Variational Mode Decomposition for Wind Speed Prediction, ZHANG, Y., GAO, S., BAN, M., SUN, Y., Advances in Electrical and Computer Engineering, ISSN 1582-7445, Issue 2, Volume 19, 2019.
Digital Object Identifier: 10.4316/AECE.2019.02001
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[4] A domain association hierarchical decomposition optimization method for cab vibration control of commercial vehicles, He, Shuilong, Tang, Tao, Ye, Mingsong, Xu, Enyong, Deng, Jucai, Tang, Rongjiang, Measurement, ISSN 0263-2241, Issue , 2019.
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[5] A Novel Hybrid Model for Wind Speed Prediction Based on VMD and Neural Network Considering Atmospheric Uncertainties, Zhang, Yagang, Zhao, Yuan, Gao, Shuang, IEEE Access, ISSN 2169-3536, Issue , 2019.
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[6] Automatic detection of a wheelset bearing fault using a multi-level empirical wavelet transform, Ding, Jianming, Ding, Chengcheng, Measurement, ISSN 0263-2241, Issue , 2019.
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[7] A sparse stacked denoising autoencoder with optimized transfer learning applied to the fault diagnosis of rolling bearings, Sun, Meidi, Wang, Hui, Liu, Ping, Huang, Shoudao, Fan, Peng, Measurement, ISSN 0263-2241, Issue , 2019.
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[9] Bearing fault diagnosis based on Cluster-contraction Stage-wise Orthogonal-Matching-Pursuit, Song, Liu, Yan, Ruqiang, Measurement, ISSN 0263-2241, Issue , 2019.
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[10] Short-term wind speed prediction model based on GA-ANN improved by VMD, Zhang, Yagang, Pan, Guifang, Chen, Bing, Han, Jingyi, Zhao, Yuan, Zhang, Chenhong, Renewable Energy, ISSN 0960-1481, Issue , 2020.
Digital Object Identifier: 10.1016/j.renene.2019.12.047
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[11] Wind Speed Prediction Using Wavelet Decomposition Based on Lorenz Disturbance Model, Zhang, Yagang, Zhang, Chenhong, Gao, Shuang, Wang, Penghui, Xie, Fenglin, Cheng, Penglai, Lei, Shuang, IETE Journal of Research, ISSN 0377-2063, Issue 5, Volume 66, 2020.
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[CrossRef]

[12] Evaluation of neural network-based methodologies for wind speed forecasting, Samet, Haidar, Reisi, Mohammad, Marzbani, Fatemeh, Computers & Electrical Engineering, ISSN 0045-7906, Issue , 2019.
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[13] Detection of Cracks and damage in wind turbine blades using artificial intelligence-based image analytics, Reddy, Abhishek, Indragandhi, V., Ravi, Logesh, Subramaniyaswamy, V., Measurement, ISSN 0263-2241, Issue , 2019.
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[14] A new laboratory test method for tire-pavement noise, Ren, Wanyan, Han, Sen, Fwa, Tien Fang, Zhang, Jiahao, He, Zhihao, Measurement, ISSN 0263-2241, Issue , 2019.
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[15] A Rolling Bearing Fault Diagnosis-Optimized Scale-Space Representation for the Empirical Wavelet Transform, Liu, Zechao, Ding, Jianming, Lin, Jianhui, Huang, Yan, Shock and Vibration, ISSN 1070-9622, Issue , 2018.
Digital Object Identifier: 10.1155/2018/2749689
[CrossRef]

[16] A three-dimensional geometric features-based SCA algorithm for compound faults diagnosis, Hao, Yansong, Song, Liuyang, Cui, Lingli, Wang, Huaqing, Measurement, ISSN 0263-2241, Issue , 2019.
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[CrossRef]

[17] A novel hybrid model based on VMD-WT and PCA-BP-RBF neural network for short-term wind speed forecasting, Zhang, Yagang, Chen, Bing, Pan, Guifang, Zhao, Yuan, Energy Conversion and Management, ISSN 0196-8904, Issue , 2019.
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[CrossRef]

[18] States prediction for solar power and wind speed using BBA‐SVM, Li, Zhen‐Long, Xia, Jing, Liu, An, Li, Peng, IET Renewable Power Generation, ISSN 1752-1416, Issue 7, Volume 13, 2019.
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[CrossRef]

[19] Wind Speed Interval Prediction Based on Lorenz Disturbance Distribution, Zhang, Yagang, Zhao, Yuan, Pan, Guifang, Zhang, Jinfang, IEEE Transactions on Sustainable Energy, ISSN 1949-3029, Issue 2, Volume 11, 2020.
Digital Object Identifier: 10.1109/TSTE.2019.2907699
[CrossRef]

[20] Short-term wind speed prediction model of VMD-FOAGRNN based on Lorenz disturbance, Pan, Guifang, Han, Jingyi, Zhang, Yagang, 2019 IEEE Sustainable Power and Energy Conference (iSPEC), ISBN 978-1-7281-4930-1, 2019.
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