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Modifiye PSO ile değişken hızlı rüzgâr türbini kanat açısının ayarlanması

Yıl 2025, Cilt: 31 Sayı: 1, 73 - 78, 27.02.2025

Öz

Bu çalışmanın ana motivasyonu, rüzgar türbinlerinin (RT) mekanik çıkış gücüne etki eden kanat açısının (𝛽) parçacık sürü optimizasyonu (PSO) ile ayarlanmasıdır. Bu amaca yönelik olarak, standart yapıdaki PSO modifiye edilmiştir. Böylece modifiye edilmiş PSO algoritması rüzgar hızının değişimine bağlı olarak kendini güncelleyebilen bir duruma getirilmiştir. Yapılan çalışma da, değişken rüzgar hızlarında çalışan rüzgar türbinlerinin kanat açılarının kontrolü sürü zekasına dayalı bir optimizasyon algoritması kullanılarak ayarlanması sağlanmıştır. Çalışma kapsamında değişen hızlı rüzgar türbinini iki farklı şekilde benzetim ortamında çalıştırılmıştır. Birinci durumda kanat açısı sabit tutularak (𝛽 = 0° için) değerler elde edilmiştir. İkinci durumda ise önerilen modifiye PSO’un ürettiği açı ile kanat açısı ayarlanarak çalıştırılmıştır ve her iki durumun sonuçları tartışılmıştır.

Kaynakça

  • [1] Ma Y, Zhang A, Yang L, Hu C, Bai Y. “Investigation on optimization design of offshore wind turbine blades based on particle swarm optimization”. Energies, 12(10), 1-18, 2019.
  • [2] Ata R. “Neural prediction of wind blowing durations based on average wind speeds for Akhisar location”. Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi, 20(5), 162-165, 2014.
  • [3] Soufi Y, Kahla S, Bechouat M. “Feedback linearization control based particle swarm optimization for maximum power point tracking of wind turbine equipped by PMSG connected to the grid”. International Journal of Hydrogen Energy, 41(45), 20950-20955, 2016.
  • [4] Hannan MA, Parvin K, Kit YK, Jern KP, Hoque MM. “Particle swarm optimization based fuzzy logic mppt inverter controller for grid connected wind turbine”. International Journal of Renewable Energy Research, 9(1), 164-174, 2019.
  • [5] Chen J, Yang R, Ma R, Li J. “Design optimization of wind turbine tower with lattice‐tubular hybrid structure using particle swarm algorithm”. The Structural Design of Tall and Special Buildings, 25(15), 743-758, 2016.
  • [6] Asaah P, Hao L, Ji J. “Optimal placement of wind turbines in wind farm layout using particle swarm optimization”. Journal of Modern Power Systems and Clean Energy, 9(2), 367-375, 2021.
  • [7] Moorthy CB, Deshmukh MK. “A new approach to optimise placement of wind turbines using particle swarm optimization”. International Journal of Sustainable Energy, 34(6), 396-405, 2015.
  • [8] Hou P, Hu W, Soltani M, Chen Z. “Optimized placement of wind turbines in large-scale offshore wind farm using particle swarm optimization algorithm”. IEEE Transactions on Sustainable Energy, 6(4), 1272-1282, 2015.
  • [9] Sun F, Xu Z, Zhang D. “Optimization Design of Wind Turbine Blade Based on an Improved Particle Swarm Optimization Algorithm Combined with Non‐Gaussian Distribution”. Advances in Civil Engineering, 2021(1), 1-9, 2021.
  • [10] Osadciw LA, Yan Y, Ye X, Benson G, White E. Wind Turbine Diagnostics Based on Power Curve Using Particle Swarm Optimization. Editors: Wang L, Singh C, Kusiak A. Wind power systems: applications of computational intelligence. Springer, 151-165, Berlin, Heidelberg, 2010.
  • [11] Iqbal A, Ying D, Saleem A, Hayat MA, Mateen M. “Proposed particle swarm optimization technique for the wind turbine control system”. Measurement and Control, 53(5-6), 1022-1030, 2020.
  • [12] Li Y, Wei K, Yang W, Wang Q. “Improving wind turbine blade based on multi-objective particle swarm optimization”. Renewable Energy, 161, 525-542, 2020.
  • [13] Luo GT, Xiao LQ, Huang YJ, Hsu YY. “Preventive frequency control of a microgrid with deloaded wind turbines using analytical frequency predictor and a PSO‐based pitch angle optimizer”. IET Renewable Power Generation, 17(7), 1654-1669, 2023.
  • [14] Shao Y, Liu J, Huang J, Hu L, Guo L, Fang Y. “The implementation of fuzzy PSO-PID adaptive controller in pitch regulation for wind turbines suppressing multi-factor disturbances”. Frontiers in Energy Research, 9, 1-10, 2022.
  • [15] Kamarzarrin M, Refan MH. “Intelligent sliding mode adaptive controller design for wind turbine pitch control system using PSO-SVM in presence of disturbance”. Journal of Control, Automation and Electrical Systems, 31(4), 912-925, 2020.
  • [16] Karami-Mollaee A, Barambones O. “pitch control of wind turbine blades using fractional particle swarm optimization”. Axioms, 12(1), 1-25, 2022.
  • [17] Chen J, Yang B, Duan W, Shu H, An N, Chen L, Yu T. “Adaptive pitch control of variable-pitch PMSG based wind turbine”. Applied Sciences, 9(19), 1-20, 2019.
  • [18] Poultangari I, Shahnazi R, Sheikhan M. “RBF neural network based PI pitch controller for a class of 5-MW wind turbines using particle swarm optimization algorithm”. ISA Transactions, 51(5), 641-648, 2012.
  • [19] Kahla S, Soufi Y, Sedraoui M, Bechouat M. “On-Off control based particle swarm optimization for maximum power point tracking of wind turbine equipped by DFIG connected to the grid with energy storage”. International Journal of Hydrogen Energy, 40(39), 13749-13758, 2015.
  • [20] Li Z, Chen H, Xu B, Ge H. “Hybrid wind turbine towers optimization with a parallel updated particle swarm algorithm”. Applied Sciences, 11(18), 1-21, 2021.
  • [21] İşcan S, Kaplan O, Lokman G. “Güç sisteminde meta-sezgisel algoritmalarla güç kaybı ve gerilim kararlılığı optimizasyonu”. Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi, 27(2), 199-209, 2021.
  • [22] Alcan Y, Öztürk A, Dirik H, Demir M. “Güç şebekelerinde minimum kayıpları sağlayan STATCOM konumunun ve değerinin belirlenmesinde farklı sezgisel algoritmaların karşılaştırılması”. Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi, 23(5), 550-558, 2017.
  • [23] Hodzic M, Tai LC. “Grey predictor reference model for assisting particle swarm optimization for wind turbine control”. Renewable Energy, 86, 251-256, 2016.
  • [24] Qureshi TA, Warudkar V. “Wind farm layout optimization through optimal wind turbine placement using a hybrid particle swarm optimization and genetic algorithm”. Environmental Science and Pollution Research, 30(31), 77436-77452, 2023.
  • [25] Sompracha C, Jayaweera D, Tricoli P. “Particle swarm optimisation technique to improve energy efficiency of doubly‐fed induction generators for wind turbines”. The Journal of Engineering, 2019(18), 4890-4895, 2019.
  • [26] Xiao Y, Zhang T, Ding Z, Li C. “The study of fuzzy proportional integral controllers based on improved particle swarm optimization for permanent magnet direct drive wind turbine converters”. Energies, 9(5), 1-17, 2016.

Adjustment of variable speed wind turbine blade angle with Modified PSO

Yıl 2025, Cilt: 31 Sayı: 1, 73 - 78, 27.02.2025

Öz

The main motivation of this study is to adjust the blade angle (β), which affects the mechanical output power of wind turbines (WTs), by particle swarm optimization (PSO). For this purpose, the standard PSO structure has been modified. Thus, the modified PSO algorithm has become capable of updating itself depending on the change of wind speed. In this study, the blade angles of wind turbines operating at variable wind speeds were controlled and adjusted using an optimization algorithm based on swarm intelligence. Within the scope of the study, a variable speed wind turbine was operated in two different simulation environments. In the first case, the values were obtained by keeping the wing angle constant (for β=0). In the second case, the proposed modification was operated by adjusting the blade angle with the angle produced by PSO, and the results of both cases were discussed.

Kaynakça

  • [1] Ma Y, Zhang A, Yang L, Hu C, Bai Y. “Investigation on optimization design of offshore wind turbine blades based on particle swarm optimization”. Energies, 12(10), 1-18, 2019.
  • [2] Ata R. “Neural prediction of wind blowing durations based on average wind speeds for Akhisar location”. Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi, 20(5), 162-165, 2014.
  • [3] Soufi Y, Kahla S, Bechouat M. “Feedback linearization control based particle swarm optimization for maximum power point tracking of wind turbine equipped by PMSG connected to the grid”. International Journal of Hydrogen Energy, 41(45), 20950-20955, 2016.
  • [4] Hannan MA, Parvin K, Kit YK, Jern KP, Hoque MM. “Particle swarm optimization based fuzzy logic mppt inverter controller for grid connected wind turbine”. International Journal of Renewable Energy Research, 9(1), 164-174, 2019.
  • [5] Chen J, Yang R, Ma R, Li J. “Design optimization of wind turbine tower with lattice‐tubular hybrid structure using particle swarm algorithm”. The Structural Design of Tall and Special Buildings, 25(15), 743-758, 2016.
  • [6] Asaah P, Hao L, Ji J. “Optimal placement of wind turbines in wind farm layout using particle swarm optimization”. Journal of Modern Power Systems and Clean Energy, 9(2), 367-375, 2021.
  • [7] Moorthy CB, Deshmukh MK. “A new approach to optimise placement of wind turbines using particle swarm optimization”. International Journal of Sustainable Energy, 34(6), 396-405, 2015.
  • [8] Hou P, Hu W, Soltani M, Chen Z. “Optimized placement of wind turbines in large-scale offshore wind farm using particle swarm optimization algorithm”. IEEE Transactions on Sustainable Energy, 6(4), 1272-1282, 2015.
  • [9] Sun F, Xu Z, Zhang D. “Optimization Design of Wind Turbine Blade Based on an Improved Particle Swarm Optimization Algorithm Combined with Non‐Gaussian Distribution”. Advances in Civil Engineering, 2021(1), 1-9, 2021.
  • [10] Osadciw LA, Yan Y, Ye X, Benson G, White E. Wind Turbine Diagnostics Based on Power Curve Using Particle Swarm Optimization. Editors: Wang L, Singh C, Kusiak A. Wind power systems: applications of computational intelligence. Springer, 151-165, Berlin, Heidelberg, 2010.
  • [11] Iqbal A, Ying D, Saleem A, Hayat MA, Mateen M. “Proposed particle swarm optimization technique for the wind turbine control system”. Measurement and Control, 53(5-6), 1022-1030, 2020.
  • [12] Li Y, Wei K, Yang W, Wang Q. “Improving wind turbine blade based on multi-objective particle swarm optimization”. Renewable Energy, 161, 525-542, 2020.
  • [13] Luo GT, Xiao LQ, Huang YJ, Hsu YY. “Preventive frequency control of a microgrid with deloaded wind turbines using analytical frequency predictor and a PSO‐based pitch angle optimizer”. IET Renewable Power Generation, 17(7), 1654-1669, 2023.
  • [14] Shao Y, Liu J, Huang J, Hu L, Guo L, Fang Y. “The implementation of fuzzy PSO-PID adaptive controller in pitch regulation for wind turbines suppressing multi-factor disturbances”. Frontiers in Energy Research, 9, 1-10, 2022.
  • [15] Kamarzarrin M, Refan MH. “Intelligent sliding mode adaptive controller design for wind turbine pitch control system using PSO-SVM in presence of disturbance”. Journal of Control, Automation and Electrical Systems, 31(4), 912-925, 2020.
  • [16] Karami-Mollaee A, Barambones O. “pitch control of wind turbine blades using fractional particle swarm optimization”. Axioms, 12(1), 1-25, 2022.
  • [17] Chen J, Yang B, Duan W, Shu H, An N, Chen L, Yu T. “Adaptive pitch control of variable-pitch PMSG based wind turbine”. Applied Sciences, 9(19), 1-20, 2019.
  • [18] Poultangari I, Shahnazi R, Sheikhan M. “RBF neural network based PI pitch controller for a class of 5-MW wind turbines using particle swarm optimization algorithm”. ISA Transactions, 51(5), 641-648, 2012.
  • [19] Kahla S, Soufi Y, Sedraoui M, Bechouat M. “On-Off control based particle swarm optimization for maximum power point tracking of wind turbine equipped by DFIG connected to the grid with energy storage”. International Journal of Hydrogen Energy, 40(39), 13749-13758, 2015.
  • [20] Li Z, Chen H, Xu B, Ge H. “Hybrid wind turbine towers optimization with a parallel updated particle swarm algorithm”. Applied Sciences, 11(18), 1-21, 2021.
  • [21] İşcan S, Kaplan O, Lokman G. “Güç sisteminde meta-sezgisel algoritmalarla güç kaybı ve gerilim kararlılığı optimizasyonu”. Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi, 27(2), 199-209, 2021.
  • [22] Alcan Y, Öztürk A, Dirik H, Demir M. “Güç şebekelerinde minimum kayıpları sağlayan STATCOM konumunun ve değerinin belirlenmesinde farklı sezgisel algoritmaların karşılaştırılması”. Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi, 23(5), 550-558, 2017.
  • [23] Hodzic M, Tai LC. “Grey predictor reference model for assisting particle swarm optimization for wind turbine control”. Renewable Energy, 86, 251-256, 2016.
  • [24] Qureshi TA, Warudkar V. “Wind farm layout optimization through optimal wind turbine placement using a hybrid particle swarm optimization and genetic algorithm”. Environmental Science and Pollution Research, 30(31), 77436-77452, 2023.
  • [25] Sompracha C, Jayaweera D, Tricoli P. “Particle swarm optimisation technique to improve energy efficiency of doubly‐fed induction generators for wind turbines”. The Journal of Engineering, 2019(18), 4890-4895, 2019.
  • [26] Xiao Y, Zhang T, Ding Z, Li C. “The study of fuzzy proportional integral controllers based on improved particle swarm optimization for permanent magnet direct drive wind turbine converters”. Energies, 9(5), 1-17, 2016.
Toplam 26 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Elektrik Mühendisliği (Diğer)
Bölüm Makale
Yazarlar

Göksel Gökkuş

Yayımlanma Tarihi 27 Şubat 2025
Gönderilme Tarihi 11 Aralık 2023
Kabul Tarihi 22 Mart 2024
Yayımlandığı Sayı Yıl 2025 Cilt: 31 Sayı: 1

Kaynak Göster

APA Gökkuş, G. (2025). Adjustment of variable speed wind turbine blade angle with Modified PSO. Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi, 31(1), 73-78.
AMA Gökkuş G. Adjustment of variable speed wind turbine blade angle with Modified PSO. Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi. Şubat 2025;31(1):73-78.
Chicago Gökkuş, Göksel. “Adjustment of Variable Speed Wind Turbine Blade Angle With Modified PSO”. Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi 31, sy. 1 (Şubat 2025): 73-78.
EndNote Gökkuş G (01 Şubat 2025) Adjustment of variable speed wind turbine blade angle with Modified PSO. Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi 31 1 73–78.
IEEE G. Gökkuş, “Adjustment of variable speed wind turbine blade angle with Modified PSO”, Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi, c. 31, sy. 1, ss. 73–78, 2025.
ISNAD Gökkuş, Göksel. “Adjustment of Variable Speed Wind Turbine Blade Angle With Modified PSO”. Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi 31/1 (Şubat 2025), 73-78.
JAMA Gökkuş G. Adjustment of variable speed wind turbine blade angle with Modified PSO. Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi. 2025;31:73–78.
MLA Gökkuş, Göksel. “Adjustment of Variable Speed Wind Turbine Blade Angle With Modified PSO”. Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi, c. 31, sy. 1, 2025, ss. 73-78.
Vancouver Gökkuş G. Adjustment of variable speed wind turbine blade angle with Modified PSO. Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi. 2025;31(1):73-8.





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