Araştırma Makalesi
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RETRACTION: DSTATCOM Based on Artificial Neural Networks and Particle Swarm Optimization for Voltage Profile Improvement

Yıl 2021, , 32 - 45, 21.06.2021
https://doi.org/10.5281/zenodo.4742866

Öz

Recently the demand for electric power is increased due to increasing the residential and industrial facilities which may contain sensitive nonlinear loads that needed high power quality (PQ) on the distribution system to avoid malfunction operation. One main PQ issue is voltage profile improvement with acceptable voltage harmonic distortion. It should be regulated to be within acceptable standard levels. In order to improve the voltage profile, the distribution static synchronous compensator (DSTATCOM) is used with a developed control strategy. In this research, DSTATCOM control is developed based on artificial intelligent (AI) using the artificial neural network (ANN), which depends on optimum values obtained by using particle swarm optimization (PSO). The results of the simulation proved the superiority and robustness of the proposed control strategy of DSTATCOM for improving the voltage profile on the distribution system. The validation of results has been done by MATLAB/Simulink software package.


This article was retracted on August 17, 2021.

Kaynakça

  • [1] Saafin Z, Zaro F, Jawadeh M. Voltage Profile Improvement Using DSTATCOM Based on Artificial Intelligent Techniques. IEEE Jordan International Joint Conference on Electrical Engineering and Information Technology (JEEIT), 2019,773-778, doi: 10.1109/JEEIT.2019.8717452.
  • [2] Sharaf A, Gandoman F. A switched hybrid filter-DVS/green plug for smart grid nonlinear loads. Smart Energy Grid Engineering (SEGE), 2015, 1-6, doi: 10.1109/SEGE.2015.7324588
  • [3] Short, T. Distribution reliability and power quality. United States: Crc Press, 2005.
  • [4] Zaro F, Abido M. Real-Time Detection and Classification of Power Quality Problems Based on Wavelet Transform. Jordan Journal of Electrical Engineering, 2019, 5(4), 222-242.
  • [5] Ipinnimo, Chowdhury, O, Chowdhury, S, Mitra, J. A review of voltage dip mitigation techniques with distributed generation in electricity networks. Electric Power Systems Research, 2013, 103, 28-36, https://doi.org/10.1016/j.epsr.2013.05.004.
  • [6] Rana A, Trivedi I, Vasoya C, Pandya M, Gohil S, Saradva P. Application of D-STATCOM for power quality improvement in distribution line. International Conference on Computation of Power, Energy Information and Commuincation, Chennai, 2016, 719-725, doi: 10.1109/ICCPEIC.2016.7557315.
  • [7] Bhim S, Sabha R, Pankajkumar V. An implementation of double‐frequency oscillation cancellation technique in control of DSTATCOM. International Transactions on Electrical Energy Systems, 2013, 24(6): 796-807, doi.org/10.1002/etep.1735
  • [8] Liu C, Hsu Y. Design of a self-tuning PI controller for a STATCOM using particle swarm optimization. IEEE Transactions on industrial Electronics, 2010, 57(2): 702-715, doi: 10.1109/TIE.2009.2028350.
  • [9] Kamaraj P, Thamizharasu T, M V. Voltage regulation of soft switched interleaved boost converter using fuzzy proportional integral controller. Journal of Energy Systems. 2020; 4(4): 145-160. https://doi.org/10.30521/jes.762506
  • [10] Alqam S, Zaro F. Power Quality Detection and Classification Using S-Transform and Rule-Based Decision Tree. International Journal of Electrical and Electronic Engineering & Telecommunications, 2019, 8(1): 45-50, doi: 10.18178/ijeetc.8.1.45-50
  • [11] Labeeb M, Lathika B. Design and analysis of DSTATCOM using SRFT and ANN-fuzzy based control for power quality improvement. IEEE Recent Advances in Intelligent Computational Systems, Trivandrum, Kerala, 2011, 274-279, doi: 10.1109/RAICS.2011.6069317.
  • [12] Nabisha A, Joseph X. Power Quality Enriched Wind Energy System using DSTATCOM based on PID-ANN Controller. American-Eurasian Journal of Scientific Research, 2017, 12(5): 271-284, doi: 10.5829/idosi.aejsr.2017.271.284.
  • [13] Babaei M, Jafari-Marandi M, Abdelwahed S, Smith B. A simulated Annealing-based optimal design of STATCOM under unbalanced conditions and faults. Power and Energy Conference at Illinois (PECI), 2017, 1-5, doi: 10.1109/PECI.2017.7935768.
  • [14] Zaro F. True Multi-Objective Optimal Power Flow in a Deregulated Environment Using Intelligent Technique. Journal of Engineering Research and Technology, 2017, 3(4): 110-118.
  • [15] Panda S, Padhy N. Comparison of particle swarm optimization and genetic algorithm for FACTS-based controller design. Applied soft computing, 2008, 8(4): 1418-1427.
  • [16] Beşikçi E, Arslan O, Turan O, Ölçer A. An artificial neural network-based decision support system for energy efficient ship operations. Computers & Operations Research, 2016, 66: 393-401, https://doi.org/10.1016/j.cor.2015.04.004.
  • [17] Samadianfard S, Jarhan S, Sadri Nahand H. Application of support vector regression integrated with firefly optimization algorithm for predicting global solar radiation. Journal of Energy Systems. 2018; 2(4): 180-189. https://doi.org/10.30521/jes.458328

Geri Çekildi: Yapay Sinir Ağlarına ve Gerilim Profili İyileştirme için Parçacık Sürüsü Optimizasyonuna Dayalı DSTATCOM

Yıl 2021, , 32 - 45, 21.06.2021
https://doi.org/10.5281/zenodo.4742866

Öz

Son zamanlarda, arıza çalışmasını önlemek için dağıtım sisteminde yüksek güç kalitesi (PQ) gerektiren hassas doğrusal olmayan yükler içerebilen konut ve endüstriyel tesislerin artması nedeniyle elektrik gücüne olan talep artmıştır. Bir ana PQ sorunu, kabul edilebilir gerilim harmonik distorsiyonu ile gerilim profili iyileştirmesidir. Kabul edilebilir standart seviyelerde olacak şekilde düzenlenmelidir. Gerilim profilini iyileştirmek için, geliştirilmiş bir kontrol stratejisi ile dağıtım statik senkron kompansatör (DSTATCOM) kullanılır. Bu araştırmada, DSTATCOM kontrolü, parçacık sürüsü optimizasyonu (PSO) kullanılarak elde edilen optimum değerlere bağlı olan yapay sinir ağı (YSA) kullanılarak yapay zekaya (AI) dayalı olarak geliştirilmiştir. Simülasyonun sonuçları, dağıtım sistemindeki voltaj profilini iyileştirmek için DSTATCOM'un önerilen kontrol stratejisinin üstünlüğünü ve sağlamlığını kanıtladı. Sonuçların doğrulanması MATLAB / Simulink yazılım paketi ile yapılmıştır.


Bu makale 17-08-2021 tarihinde geri çekildi. 

Kaynakça

  • [1] Saafin Z, Zaro F, Jawadeh M. Voltage Profile Improvement Using DSTATCOM Based on Artificial Intelligent Techniques. IEEE Jordan International Joint Conference on Electrical Engineering and Information Technology (JEEIT), 2019,773-778, doi: 10.1109/JEEIT.2019.8717452.
  • [2] Sharaf A, Gandoman F. A switched hybrid filter-DVS/green plug for smart grid nonlinear loads. Smart Energy Grid Engineering (SEGE), 2015, 1-6, doi: 10.1109/SEGE.2015.7324588
  • [3] Short, T. Distribution reliability and power quality. United States: Crc Press, 2005.
  • [4] Zaro F, Abido M. Real-Time Detection and Classification of Power Quality Problems Based on Wavelet Transform. Jordan Journal of Electrical Engineering, 2019, 5(4), 222-242.
  • [5] Ipinnimo, Chowdhury, O, Chowdhury, S, Mitra, J. A review of voltage dip mitigation techniques with distributed generation in electricity networks. Electric Power Systems Research, 2013, 103, 28-36, https://doi.org/10.1016/j.epsr.2013.05.004.
  • [6] Rana A, Trivedi I, Vasoya C, Pandya M, Gohil S, Saradva P. Application of D-STATCOM for power quality improvement in distribution line. International Conference on Computation of Power, Energy Information and Commuincation, Chennai, 2016, 719-725, doi: 10.1109/ICCPEIC.2016.7557315.
  • [7] Bhim S, Sabha R, Pankajkumar V. An implementation of double‐frequency oscillation cancellation technique in control of DSTATCOM. International Transactions on Electrical Energy Systems, 2013, 24(6): 796-807, doi.org/10.1002/etep.1735
  • [8] Liu C, Hsu Y. Design of a self-tuning PI controller for a STATCOM using particle swarm optimization. IEEE Transactions on industrial Electronics, 2010, 57(2): 702-715, doi: 10.1109/TIE.2009.2028350.
  • [9] Kamaraj P, Thamizharasu T, M V. Voltage regulation of soft switched interleaved boost converter using fuzzy proportional integral controller. Journal of Energy Systems. 2020; 4(4): 145-160. https://doi.org/10.30521/jes.762506
  • [10] Alqam S, Zaro F. Power Quality Detection and Classification Using S-Transform and Rule-Based Decision Tree. International Journal of Electrical and Electronic Engineering & Telecommunications, 2019, 8(1): 45-50, doi: 10.18178/ijeetc.8.1.45-50
  • [11] Labeeb M, Lathika B. Design and analysis of DSTATCOM using SRFT and ANN-fuzzy based control for power quality improvement. IEEE Recent Advances in Intelligent Computational Systems, Trivandrum, Kerala, 2011, 274-279, doi: 10.1109/RAICS.2011.6069317.
  • [12] Nabisha A, Joseph X. Power Quality Enriched Wind Energy System using DSTATCOM based on PID-ANN Controller. American-Eurasian Journal of Scientific Research, 2017, 12(5): 271-284, doi: 10.5829/idosi.aejsr.2017.271.284.
  • [13] Babaei M, Jafari-Marandi M, Abdelwahed S, Smith B. A simulated Annealing-based optimal design of STATCOM under unbalanced conditions and faults. Power and Energy Conference at Illinois (PECI), 2017, 1-5, doi: 10.1109/PECI.2017.7935768.
  • [14] Zaro F. True Multi-Objective Optimal Power Flow in a Deregulated Environment Using Intelligent Technique. Journal of Engineering Research and Technology, 2017, 3(4): 110-118.
  • [15] Panda S, Padhy N. Comparison of particle swarm optimization and genetic algorithm for FACTS-based controller design. Applied soft computing, 2008, 8(4): 1418-1427.
  • [16] Beşikçi E, Arslan O, Turan O, Ölçer A. An artificial neural network-based decision support system for energy efficient ship operations. Computers & Operations Research, 2016, 66: 393-401, https://doi.org/10.1016/j.cor.2015.04.004.
  • [17] Samadianfard S, Jarhan S, Sadri Nahand H. Application of support vector regression integrated with firefly optimization algorithm for predicting global solar radiation. Journal of Energy Systems. 2018; 2(4): 180-189. https://doi.org/10.30521/jes.458328
Toplam 17 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Elektrik Mühendisliği
Bölüm Araştırma Makaleleri
Yazarlar

Fouad Zaro 0000-0003-3107-0661

Yayımlanma Tarihi 21 Haziran 2021
Gönderilme Tarihi 28 Mart 2021
Kabul Tarihi 7 Mayıs 2021
Yayımlandığı Sayı Yıl 2021

Kaynak Göster

APA Zaro, F. (2021). RETRACTION: DSTATCOM Based on Artificial Neural Networks and Particle Swarm Optimization for Voltage Profile Improvement. Journal of Science, Technology and Engineering Research, 2(1), 32-45. https://doi.org/10.5281/zenodo.4742866
AMA Zaro F. RETRACTION: DSTATCOM Based on Artificial Neural Networks and Particle Swarm Optimization for Voltage Profile Improvement. JSTER. Haziran 2021;2(1):32-45. doi:10.5281/zenodo.4742866
Chicago Zaro, Fouad. “RETRACTION: DSTATCOM Based on Artificial Neural Networks and Particle Swarm Optimization for Voltage Profile Improvement”. Journal of Science, Technology and Engineering Research 2, sy. 1 (Haziran 2021): 32-45. https://doi.org/10.5281/zenodo.4742866.
EndNote Zaro F (01 Haziran 2021) RETRACTION: DSTATCOM Based on Artificial Neural Networks and Particle Swarm Optimization for Voltage Profile Improvement. Journal of Science, Technology and Engineering Research 2 1 32–45.
IEEE F. Zaro, “RETRACTION: DSTATCOM Based on Artificial Neural Networks and Particle Swarm Optimization for Voltage Profile Improvement”, JSTER, c. 2, sy. 1, ss. 32–45, 2021, doi: 10.5281/zenodo.4742866.
ISNAD Zaro, Fouad. “RETRACTION: DSTATCOM Based on Artificial Neural Networks and Particle Swarm Optimization for Voltage Profile Improvement”. Journal of Science, Technology and Engineering Research 2/1 (Haziran 2021), 32-45. https://doi.org/10.5281/zenodo.4742866.
JAMA Zaro F. RETRACTION: DSTATCOM Based on Artificial Neural Networks and Particle Swarm Optimization for Voltage Profile Improvement. JSTER. 2021;2:32–45.
MLA Zaro, Fouad. “RETRACTION: DSTATCOM Based on Artificial Neural Networks and Particle Swarm Optimization for Voltage Profile Improvement”. Journal of Science, Technology and Engineering Research, c. 2, sy. 1, 2021, ss. 32-45, doi:10.5281/zenodo.4742866.
Vancouver Zaro F. RETRACTION: DSTATCOM Based on Artificial Neural Networks and Particle Swarm Optimization for Voltage Profile Improvement. JSTER. 2021;2(1):32-45.
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