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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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  4/2021 - 1
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 HIGHLY CITED PAPER 

A Wind Energy Prediction Scheme Combining Cauchy Variation and Reverse Learning Strategy

WU, X. See more information about WU, X. on SCOPUS See more information about WU, X. on IEEExplore See more information about WU, X. on Web of Science, SHEN, X. See more information about  SHEN, X. on SCOPUS See more information about  SHEN, X. on SCOPUS See more information about SHEN, X. on Web of Science, ZHANG, J. See more information about  ZHANG, J. on SCOPUS See more information about  ZHANG, J. on SCOPUS See more information about ZHANG, J. on Web of Science, ZHANG, Y. See more information about ZHANG, Y. on SCOPUS See more information about ZHANG, Y. on SCOPUS See more information about ZHANG, Y. on Web of Science
 
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Download PDF pdficon (1,966 KB) | Citation | Downloads: 1,108 | Views: 1,231

Author keywords
carbon emissions, cauchy mutation, long short-term memory, reverse learning, synchrosqueezed wavelet transforms

References keywords
wind(18), energy(18), speed(13), forecasting(11), prediction(9), term(8), short(8), model(7), zhao(6), novel(6)
Blue keywords are present in both the references section and the paper title.

About this article
Date of Publication: 2021-11-30
Volume 21, Issue 4, Year 2021, On page(s): 3 - 10
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2021.04001
Web of Science Accession Number: 000725107100001
SCOPUS ID: 85122245524

Abstract
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Full text preview
Modular multilevel converters (MMCs) can be a reliable solution since they have modular structure and high quality output waveform for permanent magnet synchronous generator (PMSG) based wind energy conversion system (WECS). Capacitor voltage balancing in nearest level modulation (NLM) is required to keep the capacitor voltage of each submodule of MMC constant. In this paper, an efficient capacitor voltage balancing scheme under NLM is proposed for PMSG based WECS with MMC topology. Through proposed control scheme, arm voltages are separately controlled and voltage ripple of around 1.5% is obtained. This result provides high quality output waveform at the point of common coupling (PCC). Furthermore, DC-link voltage control is achieved via hysteresis current control based proportional-integral controller. The ripple of DC-link voltage is obtained quite well as nearly 0.25%. In addition, load voltage control is accomplished using dq reference frame-based voltage control scheme for voltage and frequency stabilization at the PCC by regulating the voltage at its reference value. Simulation studies show that all proposed control schemes give satisfactory results for MMC based WECS under variable dynamic operation modes. Finally, experimental verification is performed using laboratory prototype to show the applicability of the proposed capacitor voltage balancing scheme.


References | Cited By  «-- Click to see who has cited this paper

[1] Y. Zhang, Y. Li, G. Zhang, "Short-term wind power forecasting approach based on Seq2Seq model using NWP data," Energy, 2020: 118371.
[CrossRef] [Web of Science Times Cited 76] [SCOPUS Times Cited 91]


[2] H. Shuai, X. Yue, H. Zhang, S. Xie, J. Li, C. Gu, W. Sun, J. Liu,. "Hybrid forecasting method for wind power integrating spatial correlation and corrected numerical weather prediction," Applied Energy, 2021, 293: 116951.
[CrossRef] [Web of Science Times Cited 32] [SCOPUS Times Cited 44]


[3] E. Erdem, J. Shi. "ARMA based approaches for forecasting the tuple of wind speed and direction," Applied Energy, 2011, 88(4): 1405-1414.
[CrossRef] [Web of Science Times Cited 572] [SCOPUS Times Cited 681]


[4] W. Li, X. Jia, X. Li,, Y. Wang, J. Lee, "A Markov model for short term wind speed prediction by integrating the wind acceleration information," Renewable Energy, 2021, 164: 242-253.
[CrossRef] [Web of Science Times Cited 19] [SCOPUS Times Cited 23]


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[CrossRef] [Web of Science Times Cited 288] [SCOPUS Times Cited 324]


[6] D. Putz, M. Gumhalter, H. Auer, "A novel approach to multi-horizon wind power forecasting based on deep neural architecture," Renewable Energy, 2021, 178: 494-505.
[CrossRef] [Web of Science Times Cited 28] [SCOPUS Times Cited 37]


[7] Y. Zhang, Y. Zhao, X. Shen, J. Zhang, "A comprehensive wind speed prediction system based on Monte Carlo and artificial intelligence algorithms," Applied Energy, 2022, 305: 117815.
[CrossRef] [Web of Science Times Cited 34] [SCOPUS Times Cited 43]


[8] S. Wang, N. Zhang, L. Wu, Y. Wang, "Wind speed forecasting based on the hybrid ensemble empirical mode decomposition and GA-BP neural network method" Renewable Energy, 2016, 94: 629-636.
[CrossRef] [Web of Science Times Cited 441] [SCOPUS Times Cited 513]


[9] X. J. Chen, J. Zhao, X. Z. Jia, Z. L. Li, "Multi-step wind speed forecast based on sample clustering and an optimized hybrid system," Renewable Energy, 2021, 165: 595-611.
[CrossRef] [Web of Science Times Cited 25] [SCOPUS Times Cited 28]


[10] Z. Preitl, R. E. Precup, J. K. Tar, M. Takács, "Use of multi-parametric quadratic programming in fuzzy control systems," Acta Polytechnica Hungarica, 2006, 3(3): 29-43

[11] R. E. Precup, R. C. David, R. C. Roman, A. I. Szedlak-Stinean, E. M. Petriu, "Optimal tuning of interval type-2 fuzzy controllers for nonlinear servo systems using Slime Mould Algorithm," International Journal of Systems Science, 2021: 1-16.
[CrossRef] [Web of Science Times Cited 81] [SCOPUS Times Cited 83]


[12] M. Moattari, M. H. Moradi, "Conflict monitoring optimization heuristic inspired by brain fear and conflict systems," Int J Artif Intell, 2020, 18(1): 45-62

[13] C. Song, L. Yao, C. Hua, Q. Ni, "A novel hybrid model for water quality prediction based on synchrosqueezed wavelet transform technique and improved long short-term memory," Journal of Hydrology, 2021, 603(Part A): 126879.
[CrossRef] [Web of Science Times Cited 30] [SCOPUS Times Cited 35]


[14] Q. Mao, Q. Zhang, "Improved Sparrow algorithm integrating cauchy mutation and reverse learning," Journal of Frontiers of Computer Science and Technology, 2020, 15(6): 1155-1164.
[CrossRef]


[15] G. Memarzadeh, F. Keynia, "A new short-term wind speed forecasting method based on fine-tuned LSTM neural network and optimal input sets," Energy Conversion and Management, 2020, 213: 112824.
[CrossRef] [Web of Science Times Cited 143] [SCOPUS Times Cited 162]


[16] Y. Zhang, G. Pan, Y. Zhao, Q. Li, F. Wang, "Short-term wind speed interval prediction based on artificial intelligence methods and error probability distribution," Energy Conversion and Management, 2020, 224: 1-14.
[CrossRef] [Web of Science Times Cited 44] [SCOPUS Times Cited 52]


[17] A. Glowacz, "Ventilation diagnosis of angle grinder using thermal imaging" Sensors 2021; 21:2853.
[CrossRef] [Web of Science Times Cited 121] [SCOPUS Times Cited 121]


[18] Y. Zhao, W. Zhang, X. Gong, C. Wang, "A novel method for online real-time forecasting of crude oil price," Applied Energy, 2021; 303: 117588.
[CrossRef] [Web of Science Times Cited 19] [SCOPUS Times Cited 22]


[19] Y. Nie, N. Liang, J. Wang, "Ultra-short-term wind-speed bi-forecasting system via artificial intelligence and a double-forecasting scheme," Applied Energy, 2021, 301: 117452.
[CrossRef] [Web of Science Times Cited 34] [SCOPUS Times Cited 36]


[20] T. Liang, Q., Zhao, Q. Lv, H. Sun, "A novel wind speed prediction strategy based on Bi-LSTM, MOOFADA and transfer learning for centralized control centers," Energy, 2021, 230: 120904.
[CrossRef] [Web of Science Times Cited 44] [SCOPUS Times Cited 52]


[21] B. Lin, C. Zhang, "A novel hybrid machine learning model for short-term wind speed prediction in inner Mongolia, China," Renewable Energy, 2021.
[CrossRef] [Web of Science Times Cited 15] [SCOPUS Times Cited 17]


[22] W. Shuai, W. Jianzhou, H. Lu, W. Zhao, "A novel combined model for wind speed prediction-Combination of linear model, shallow neural networks, and deep learning approaches," Energy, 2021, 234: 121275.
[CrossRef] [Web of Science Times Cited 52] [SCOPUS Times Cited 57]


[23] Y. Zhang, G. Pan, "A hybrid prediction model for forecasting wind energy resources," Environmental Science and Pollution Research, 2020, 27(16): 19428-19446.
[CrossRef] [Web of Science Times Cited 16] [SCOPUS Times Cited 21]


[24] A. Glowacz, "Fault diagnosis of electric impact drills using thermal imaging," Measurement 2021; 171:108815.
[CrossRef] [Web of Science Times Cited 154] [SCOPUS Times Cited 170]


[25] T. B. M. J. Ouarda, C. Charron, "Non-stationary statistical modelling of wind speed: A case study in eastern Canada," Energy Conversion and Management, 2021, 236: 114028.
[CrossRef] [Web of Science Times Cited 10] [SCOPUS Times Cited 10]




References Weight

Web of Science® Citations for all references: 2,278 TCR
SCOPUS® Citations for all references: 2,622 TCR

Web of Science® Average Citations per reference: 88 ACR
SCOPUS® Average Citations per reference: 101 ACR

TCR = Total Citations for References / ACR = Average Citations per Reference

We introduced in 2010 - for the first time in scientific publishing, the term "References Weight", as a quantitative indication of the quality ... Read more

Citations for references updated on 2023-09-21 07:02 in 139 seconds.




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