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University of Suceava
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Print ISSN: 1582-7445
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  4/2021 - 1
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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: 832 | Views: 571

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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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

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[CrossRef] [Web of Science Times Cited 22] [SCOPUS Times Cited 32]


[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 13] [SCOPUS Times Cited 19]


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[CrossRef] [Web of Science Times Cited 492] [SCOPUS Times Cited 575]


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[CrossRef] [Web of Science Times Cited 262] [SCOPUS Times Cited 294]


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[CrossRef] [Web of Science Times Cited 10] [SCOPUS Times Cited 14]


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[CrossRef] [Web of Science Times Cited 14] [SCOPUS Times Cited 18]


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[CrossRef] [Web of Science Times Cited 365] [SCOPUS Times Cited 431]


[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 16] [SCOPUS Times Cited 20]


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[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 44] [SCOPUS Times Cited 50]


[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 11] [SCOPUS Times Cited 13]


[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 82] [SCOPUS Times Cited 98]


[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 30] [SCOPUS Times Cited 36]


[17] A. Glowacz, "Ventilation diagnosis of angle grinder using thermal imaging" Sensors 2021; 21:2853.
[CrossRef] [Web of Science Times Cited 72] [SCOPUS Times Cited 75]


[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 9] [SCOPUS Times Cited 10]


[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 12] [SCOPUS Times Cited 14]


[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 20] [SCOPUS Times Cited 24]


[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 9] [SCOPUS Times Cited 11]


[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 21] [SCOPUS Times Cited 29]


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[CrossRef] [Web of Science Times Cited 5] [SCOPUS Times Cited 5]




References Weight

Web of Science® Citations for all references: 1,634 TCR
SCOPUS® Citations for all references: 1,906 TCR

Web of Science® Average Citations per reference: 63 ACR
SCOPUS® Average Citations per reference: 73 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 2022-08-06 19:37 in 142 seconds.




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