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Yıl 2021, Cilt: 36 Sayı: 2, 1119 - 1132, 05.03.2021
https://doi.org/10.17341/gazimmfd.550770

Öz

Kaynakça

  • 1. Pamuk N., Enerji sistemlerinde yapay arı kolonisi (yak) algoritması kullanarak yük akışı optimizasyonu, Akdeniz Üniversitesi Akademik Bilişim Konferansı, Antalya, 2013.
  • 2. Dommel H.W., Tinney W.F., Optimal power flow solutions, IEEE Transactions on power apparatus and systems, 10, 1866–1876, 1968.
  • 3. Akdağ O., Okumuş F., Kocamaz A.F., Yeroğlu C., Fractional Order Darwinian PSO with Constraint Threshold for Load Flow Optimization of Energy Transmission System, Gazi University Journal of Science, 31(3), 831-844, 2018.
  • 4. Niknam T., Rasoul M., Jabbari M., Malekpour, A.R., A modified shuffle frog leaping algorithm for multi-objective optimal power flow, Energy, 36(11), 6420-6432, 2011.
  • 5. Kirchmayer L.K., Stagg G.W., Analysis of total and incremental losses in transmission systems, Transactions of the American Institute of Electrical Engineers, 70(2), 1197-1205, 1951.
  • 6. Squires R.B., Economic Dispatch of Generation Directly Rrom Power System Voltages and Admittances, Transactions of the American Institute of Electrical Engineers. Part III: Power Apparatus and Systems, 79(3), 1235-1244, 1960.
  • 7. Calvert J.F., Sze T.W., A new approach to loss minimization in electric power systems, Transactions of the American Institute of Electrical Engineers. Part III: Power Apparatus and Systems, 76(3), 1439-1446, 1957.
  • 8. Shipley R. B., Hochdorf, M., Exact Economic Dispatch-Digital Computer Solution [includes discussion], Transactions of the American Institute of Electrical Engineers. Part III: Power Apparatus and Systems, 75(3), 1147-1153, 1956.
  • 9. Dommel, H.W., Tinney, W.F., Optimal power flow solutions. IEEE Transactions on power apparatus and systems, (10), 1866-1876, 1968.
  • 10. Mota-Palomino R., Quintana, V.H., Sparse reactive power scheduling by a penalty function-linear programming technique. IEEE Transactions on Power Systems, 1(3), 31-39, 1986.
  • 11. Momoh J.A., El-Hawary M.E., Adapa R.A review of selected optimal power flow literature to 1993, II. Newton, linear programming and interior point methods, IEEE Transactions on Power Systems, 14(1), 105-111,1993.
  • 12. Wei H., Sasaki H., Kubokawa J., Yokoyama R. An interior point nonlinear programming for optimal power flow problems with a novel data structure, IEEE Transactions on Power Systems, 13(3), 870-877, 1998.
  • 13. Wu Y.C., Debs A.S., Marsten, R.E., A direct nonlinear predictor-corrector primal-dual interior point algorithm for optimal power flows, IEEE Transactions on power systems, 9(2), 876-883, 1994.
  • 14. Habibollahzadeh, H., Luo, G.X., Semlyen, A., Hydrothermal optimal power flow based on a combined linear and nonlinear programming methodology. IEEE Transactions on Power Systems, 4(2), 530-537, 1989.
  • 15. Burchett R.C., Happ H.H., Vierath D.R., Quadratically convergent optimal power flow, IEEE Transactions on Power Apparatus and Systems, (11), 3267-3275, 1984.
  • 16. Momoh J.A., Guo S.X., Ogbuobiri E.C., Adapa R., The quadratic interior point method solving power system optimization problems, IEEE Transactions on Power Systems, 9(3), 1327-1336, 1994.
  • 17. Fan J.Y., Zhang L. Real-time economic dispatch with line flow and emission constraints using quadratic programming, IEEE Transactions on Power Systems, 13(2), 320-325, 1998.
  • 18. Adams R.N., Laughton M.A., Optimal planning of power networks using mixed-integer programming. Part 1: Static and time-phased network synthesis, In Proceedings of the Institution of Electrical Engineers, Vol. 121, No. 2, 39-147, 2007.
  • 19. Yang G.Y., Hovland G., Majumder R., Dong Z.Y., TCSC allocation based on line flow based equations via mixed-integer programming, IEEE Transactions on power systems, 22(4), 2262-2269, 2007.
  • 20. Gou B., Optimal placement of PMUs by integer linear programming, IEEE Transactions on power systems, 23(3), 1525-1526, 2008.
  • 21. Abido M.A., Optimal power flow using particle swarm optimization, Electrical Power and, Energy Systems, 24, 563–571, 2002.
  • 22. Abido M.A., Optimal power flow using tabu search algorithm, Electric Power Components and Sys- tems (2002).
  • 23. Lenin K., Reddy B.R., Suryakalavathi M., Hybrid Tabu search-simulated annealing method to solve optimal reactive power problem, International Journal of Electrical Power & Energy Systems, 82, 87-91, 2016.
  • 24. Öztürk A., Duman S., Genetik algoritma kullanılarak güç sistemlerinde optimal çalışma şartlarının belirlenmesi, Journal of the Faculty of Engineering and Architecture of Gazi University, 24(3), 2009.
  • 25. Kahourzade S., Mahmoudi A., Mokhlis H.B., A comparative study of multi-objective optimal power flow based on particle swarm, evolutionary programming, and genetic algorithm, Electrical Engineering, 97(1), 1-12, 2015.
  • 26. Awasthi A., Venkitusamy K., Padmanaban S., Selvamuthukumaran R., Blaabjerg F., Singh A.K. Optimal planning of electric vehicle charging station at the distribution system using hybrid optimization algorithm. Energy, 133, 70-78, 2017.
  • 27. Naveen S., Kumar K.S., Rajalakshmi K., Distribution system reconfiguration for loss minimization using modified bacterial foraging optimization algorithm, International Journal of Electrical Power & Energy Systems, 69, 90-97, 2015.
  • 28. Biswas P.P., Suganthan P.N., Mallipeddi R., Amaratunga G.A.,. Optimal power flow solutions using differential evolution algorithm integrated with effective constraint handling techniques, Engineering Applications of Artificial Intelligence, 68, 81-100, 2018.
  • 29. Ayan K., Kılıç U., Baraklı B., Chaotic artificial bee colony algorithm based solution of security and transient stability constrained optimal power flow, International Journal of Electrical Power & Energy Systems, 64, 136-147, 2015.
  • 30. Peter A.K., Modified Shuffled Frog-Leaping Algorithm Based Determination of Optimal Size and Location of Distributed Generation in Radial Distribution System, IJEDR, 5(3), 2017.
  • 31. Pandiarajan K., Babulal C. K., Fuzzy harmony search algorithm based optimal power flow for power system security enhancement, International Journal of Electrical Power & Energy Systems, 78, 72-79, 2016.
  • 32. Bouchekara H.R., Chaib A.E., Abido M.A., El-Sehiemy R.A., Optimal power flow using an Improved Colliding Bodies Optimization algorithm, Applied Soft Computing, 42, 119-131, 2016.
  • 33. Mohammadi M., Ghadimi N., Optimal location and optimized parameters for robust power system stabilizer using honeybee mating optimization. Complexity, 21(1), 242-258, 2015.
  • 34. Ghasemi M., Ghavidel S., Gitizadeh M., Akbari E., An improved teaching–learning-based optimization algorithm using Lévy mutation strategy for non-smooth optimal power flow. International Journal of Electrical Power & Energy Systems, 65, 375-384, 2015.
  • 35. Chaib A.E., Bouchekara H.RE.H., Mehasni R., Abido M. A., Optimal power flow with emission and non-smooth cost functions using backtracking search optimization algorithm. International Journal of Electrical Power & Energy Systems, 81, 64-77, 2016.
  • 36. Duman S., Symbiotic organisms search algorithm for optimal power flow problem based on valve-point effect and prohibited zones. Neural Computing and Applications, 28(11), 3571-3585, 2017.
  • 37. Raviprabakaran V., Subramanian R.C., Enhanced ant colony optimization to solve the optimal power flow with ecological emission. International Journal of System Assurance Engineering and Management, 9(1), 58-65, 2018.
  • 38. El-Fergany A.A., Hasanien H.M., Tree-seed algorithm for solving optimal power flow problem in large-scale power systems incorporating validations and comparisons, Applied Soft Computing, 64, 307-316, 2018.
  • 39. IEEE 30 baralı test sistemi. https://tr.scribd.com/doc/282453109/IEEE-30-Bus-System-Data. Erişim tarihi Ocak 11, 2019.
  • 40. Eskandar H., Sadollah A., Bahreininejad A., Hamdi M., Water cycle algorithm–A novel metaheuristic optimization method for solving constrained engineering optimization problems, Computers & Structures, 110, 151-166, 2012.
  • 41. Korashy A., Kamel S., Youssef, A.R., & Jurado, F., Modified water cycle algorithm for optimal direction overcurrent relays coordination. Applied Soft Computing, 74, 10-25, 2019.
  • 42. Singh R.P., Mukherjee V., Ghoshal S.P., Particle swarm optimization with an aging leader and challengers algorithm for the solution of optimal power flow problem, Applied Soft Computing, 40, 161-177, 2016.
  • 43. Sayah S., Zehar K., Modified differential evolution algorithm for optimal power flow with non-smooth cost functions, Energy conversion and Management, 49(11), 3036-3042, 2008.
  • 44. Kumari M.S., Maheswarapu S., Enhanced genetic algorithm based computation technique for multi-objective optimal power flow solution, International Journal of Electrical Power & Energy Systems, 32(6), 736-742, 2010.
  • 45. Niknam T., Narimani M., Jabbari M., Malekpour A.R., A modified shuffle frog leaping algorithm for multi-objective optimal power flow, Energy, 36(11), 6420-6432, 2011.
  • 46. Daryani N., Hagh M.T., Teimourzadeh S., Adaptive group search optimization algorithm for multi-objective optimal power flow problem, Applied Soft Computing, 38, 1012-1024, 2016.
  • 47. Bouchekara H.R.E.H., Chaib A.E., Abido M.A., Optimal power flow using GA with a new multi-parent crossover considering: prohibited zones, valve-point effect, multi-fuels and emission, Electrical Engineering, 100(1), 151-165, 2018.
  • 48. Reddy S.S., Rathnam C.S., Optimal power flow using glowworm swarm optimization. International Journal of Electrical Power & Energy Systems, 80, 128-139, 2016.

İyileştirilmiş su çevrim algoritmasıyla optimal yük akışı

Yıl 2021, Cilt: 36 Sayı: 2, 1119 - 1132, 05.03.2021
https://doi.org/10.17341/gazimmfd.550770

Öz

Güç sistemlerinde üretim ve tüketim dengesinin
sağlanabilmesi önemlidir. Optimum yük akışı (OYA) problemi generatörler, bara
gerilimlerini, bara şönt reaktörlerini/kondansatörlerini kendi güvenli
sınırlarında tutup, yakıt maliyeti ve aktif güç kayıplarını minimize etmeyi hedefler.
Bu nedenle güç sistemlerinde OYA problemi etkili bir yöntemle
çözülmelidir.  Bu yayında, Su Çevrim
Algoritması (SÇA) ve İyileştirilmiş Su Çevrim Algoritması (İSÇA) OYA problemine
uygulanmıştır. Çalışmada, amaç fonksiyonu ile toplam yakıt maliyeti ve aktif
güç kayıplarının minimizasyonu
amaçlanmıştır. Yük akış problemi için önerilen
optimizasyon algoritması IEEE 30 baralı test sistemine uygulanmıştır. İSÇA algoritması
ile bulunan sayısal sonuçlar literatürde güncel olan diğer sezgisel
algoritmalar ile karşılaştırarak, bu algoritmanın etkinliği, uygulanabilirliği
ve esnekliği tartışılmıştır. Ayrıca bu algoritma 154 kV Güney
Marmara iletim sisteminin bir kesitine uygulanmış ve iletim sistemine ait aktif
güç kayıpları azaltılarak, uygun yük akış sağlanmıştır. 

Kaynakça

  • 1. Pamuk N., Enerji sistemlerinde yapay arı kolonisi (yak) algoritması kullanarak yük akışı optimizasyonu, Akdeniz Üniversitesi Akademik Bilişim Konferansı, Antalya, 2013.
  • 2. Dommel H.W., Tinney W.F., Optimal power flow solutions, IEEE Transactions on power apparatus and systems, 10, 1866–1876, 1968.
  • 3. Akdağ O., Okumuş F., Kocamaz A.F., Yeroğlu C., Fractional Order Darwinian PSO with Constraint Threshold for Load Flow Optimization of Energy Transmission System, Gazi University Journal of Science, 31(3), 831-844, 2018.
  • 4. Niknam T., Rasoul M., Jabbari M., Malekpour, A.R., A modified shuffle frog leaping algorithm for multi-objective optimal power flow, Energy, 36(11), 6420-6432, 2011.
  • 5. Kirchmayer L.K., Stagg G.W., Analysis of total and incremental losses in transmission systems, Transactions of the American Institute of Electrical Engineers, 70(2), 1197-1205, 1951.
  • 6. Squires R.B., Economic Dispatch of Generation Directly Rrom Power System Voltages and Admittances, Transactions of the American Institute of Electrical Engineers. Part III: Power Apparatus and Systems, 79(3), 1235-1244, 1960.
  • 7. Calvert J.F., Sze T.W., A new approach to loss minimization in electric power systems, Transactions of the American Institute of Electrical Engineers. Part III: Power Apparatus and Systems, 76(3), 1439-1446, 1957.
  • 8. Shipley R. B., Hochdorf, M., Exact Economic Dispatch-Digital Computer Solution [includes discussion], Transactions of the American Institute of Electrical Engineers. Part III: Power Apparatus and Systems, 75(3), 1147-1153, 1956.
  • 9. Dommel, H.W., Tinney, W.F., Optimal power flow solutions. IEEE Transactions on power apparatus and systems, (10), 1866-1876, 1968.
  • 10. Mota-Palomino R., Quintana, V.H., Sparse reactive power scheduling by a penalty function-linear programming technique. IEEE Transactions on Power Systems, 1(3), 31-39, 1986.
  • 11. Momoh J.A., El-Hawary M.E., Adapa R.A review of selected optimal power flow literature to 1993, II. Newton, linear programming and interior point methods, IEEE Transactions on Power Systems, 14(1), 105-111,1993.
  • 12. Wei H., Sasaki H., Kubokawa J., Yokoyama R. An interior point nonlinear programming for optimal power flow problems with a novel data structure, IEEE Transactions on Power Systems, 13(3), 870-877, 1998.
  • 13. Wu Y.C., Debs A.S., Marsten, R.E., A direct nonlinear predictor-corrector primal-dual interior point algorithm for optimal power flows, IEEE Transactions on power systems, 9(2), 876-883, 1994.
  • 14. Habibollahzadeh, H., Luo, G.X., Semlyen, A., Hydrothermal optimal power flow based on a combined linear and nonlinear programming methodology. IEEE Transactions on Power Systems, 4(2), 530-537, 1989.
  • 15. Burchett R.C., Happ H.H., Vierath D.R., Quadratically convergent optimal power flow, IEEE Transactions on Power Apparatus and Systems, (11), 3267-3275, 1984.
  • 16. Momoh J.A., Guo S.X., Ogbuobiri E.C., Adapa R., The quadratic interior point method solving power system optimization problems, IEEE Transactions on Power Systems, 9(3), 1327-1336, 1994.
  • 17. Fan J.Y., Zhang L. Real-time economic dispatch with line flow and emission constraints using quadratic programming, IEEE Transactions on Power Systems, 13(2), 320-325, 1998.
  • 18. Adams R.N., Laughton M.A., Optimal planning of power networks using mixed-integer programming. Part 1: Static and time-phased network synthesis, In Proceedings of the Institution of Electrical Engineers, Vol. 121, No. 2, 39-147, 2007.
  • 19. Yang G.Y., Hovland G., Majumder R., Dong Z.Y., TCSC allocation based on line flow based equations via mixed-integer programming, IEEE Transactions on power systems, 22(4), 2262-2269, 2007.
  • 20. Gou B., Optimal placement of PMUs by integer linear programming, IEEE Transactions on power systems, 23(3), 1525-1526, 2008.
  • 21. Abido M.A., Optimal power flow using particle swarm optimization, Electrical Power and, Energy Systems, 24, 563–571, 2002.
  • 22. Abido M.A., Optimal power flow using tabu search algorithm, Electric Power Components and Sys- tems (2002).
  • 23. Lenin K., Reddy B.R., Suryakalavathi M., Hybrid Tabu search-simulated annealing method to solve optimal reactive power problem, International Journal of Electrical Power & Energy Systems, 82, 87-91, 2016.
  • 24. Öztürk A., Duman S., Genetik algoritma kullanılarak güç sistemlerinde optimal çalışma şartlarının belirlenmesi, Journal of the Faculty of Engineering and Architecture of Gazi University, 24(3), 2009.
  • 25. Kahourzade S., Mahmoudi A., Mokhlis H.B., A comparative study of multi-objective optimal power flow based on particle swarm, evolutionary programming, and genetic algorithm, Electrical Engineering, 97(1), 1-12, 2015.
  • 26. Awasthi A., Venkitusamy K., Padmanaban S., Selvamuthukumaran R., Blaabjerg F., Singh A.K. Optimal planning of electric vehicle charging station at the distribution system using hybrid optimization algorithm. Energy, 133, 70-78, 2017.
  • 27. Naveen S., Kumar K.S., Rajalakshmi K., Distribution system reconfiguration for loss minimization using modified bacterial foraging optimization algorithm, International Journal of Electrical Power & Energy Systems, 69, 90-97, 2015.
  • 28. Biswas P.P., Suganthan P.N., Mallipeddi R., Amaratunga G.A.,. Optimal power flow solutions using differential evolution algorithm integrated with effective constraint handling techniques, Engineering Applications of Artificial Intelligence, 68, 81-100, 2018.
  • 29. Ayan K., Kılıç U., Baraklı B., Chaotic artificial bee colony algorithm based solution of security and transient stability constrained optimal power flow, International Journal of Electrical Power & Energy Systems, 64, 136-147, 2015.
  • 30. Peter A.K., Modified Shuffled Frog-Leaping Algorithm Based Determination of Optimal Size and Location of Distributed Generation in Radial Distribution System, IJEDR, 5(3), 2017.
  • 31. Pandiarajan K., Babulal C. K., Fuzzy harmony search algorithm based optimal power flow for power system security enhancement, International Journal of Electrical Power & Energy Systems, 78, 72-79, 2016.
  • 32. Bouchekara H.R., Chaib A.E., Abido M.A., El-Sehiemy R.A., Optimal power flow using an Improved Colliding Bodies Optimization algorithm, Applied Soft Computing, 42, 119-131, 2016.
  • 33. Mohammadi M., Ghadimi N., Optimal location and optimized parameters for robust power system stabilizer using honeybee mating optimization. Complexity, 21(1), 242-258, 2015.
  • 34. Ghasemi M., Ghavidel S., Gitizadeh M., Akbari E., An improved teaching–learning-based optimization algorithm using Lévy mutation strategy for non-smooth optimal power flow. International Journal of Electrical Power & Energy Systems, 65, 375-384, 2015.
  • 35. Chaib A.E., Bouchekara H.RE.H., Mehasni R., Abido M. A., Optimal power flow with emission and non-smooth cost functions using backtracking search optimization algorithm. International Journal of Electrical Power & Energy Systems, 81, 64-77, 2016.
  • 36. Duman S., Symbiotic organisms search algorithm for optimal power flow problem based on valve-point effect and prohibited zones. Neural Computing and Applications, 28(11), 3571-3585, 2017.
  • 37. Raviprabakaran V., Subramanian R.C., Enhanced ant colony optimization to solve the optimal power flow with ecological emission. International Journal of System Assurance Engineering and Management, 9(1), 58-65, 2018.
  • 38. El-Fergany A.A., Hasanien H.M., Tree-seed algorithm for solving optimal power flow problem in large-scale power systems incorporating validations and comparisons, Applied Soft Computing, 64, 307-316, 2018.
  • 39. IEEE 30 baralı test sistemi. https://tr.scribd.com/doc/282453109/IEEE-30-Bus-System-Data. Erişim tarihi Ocak 11, 2019.
  • 40. Eskandar H., Sadollah A., Bahreininejad A., Hamdi M., Water cycle algorithm–A novel metaheuristic optimization method for solving constrained engineering optimization problems, Computers & Structures, 110, 151-166, 2012.
  • 41. Korashy A., Kamel S., Youssef, A.R., & Jurado, F., Modified water cycle algorithm for optimal direction overcurrent relays coordination. Applied Soft Computing, 74, 10-25, 2019.
  • 42. Singh R.P., Mukherjee V., Ghoshal S.P., Particle swarm optimization with an aging leader and challengers algorithm for the solution of optimal power flow problem, Applied Soft Computing, 40, 161-177, 2016.
  • 43. Sayah S., Zehar K., Modified differential evolution algorithm for optimal power flow with non-smooth cost functions, Energy conversion and Management, 49(11), 3036-3042, 2008.
  • 44. Kumari M.S., Maheswarapu S., Enhanced genetic algorithm based computation technique for multi-objective optimal power flow solution, International Journal of Electrical Power & Energy Systems, 32(6), 736-742, 2010.
  • 45. Niknam T., Narimani M., Jabbari M., Malekpour A.R., A modified shuffle frog leaping algorithm for multi-objective optimal power flow, Energy, 36(11), 6420-6432, 2011.
  • 46. Daryani N., Hagh M.T., Teimourzadeh S., Adaptive group search optimization algorithm for multi-objective optimal power flow problem, Applied Soft Computing, 38, 1012-1024, 2016.
  • 47. Bouchekara H.R.E.H., Chaib A.E., Abido M.A., Optimal power flow using GA with a new multi-parent crossover considering: prohibited zones, valve-point effect, multi-fuels and emission, Electrical Engineering, 100(1), 151-165, 2018.
  • 48. Reddy S.S., Rathnam C.S., Optimal power flow using glowworm swarm optimization. International Journal of Electrical Power & Energy Systems, 80, 128-139, 2016.
Toplam 48 adet kaynakça vardır.

Ayrıntılar

Birincil Dil Türkçe
Bölüm Makaleler
Yazarlar

Ozan Akdağ 0000-0001-8163-8898

Celaleddin Yeroğlu 0000-0002-6106-2374

Yayımlanma Tarihi 5 Mart 2021
Gönderilme Tarihi 8 Nisan 2019
Kabul Tarihi 1 Ocak 2021
Yayımlandığı Sayı Yıl 2021 Cilt: 36 Sayı: 2

Kaynak Göster

APA Akdağ, O., & Yeroğlu, C. (2021). İyileştirilmiş su çevrim algoritmasıyla optimal yük akışı. Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi, 36(2), 1119-1132. https://doi.org/10.17341/gazimmfd.550770
AMA Akdağ O, Yeroğlu C. İyileştirilmiş su çevrim algoritmasıyla optimal yük akışı. GUMMFD. Mart 2021;36(2):1119-1132. doi:10.17341/gazimmfd.550770
Chicago Akdağ, Ozan, ve Celaleddin Yeroğlu. “İyileştirilmiş Su çevrim algoritmasıyla Optimal yük akışı”. Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi 36, sy. 2 (Mart 2021): 1119-32. https://doi.org/10.17341/gazimmfd.550770.
EndNote Akdağ O, Yeroğlu C (01 Mart 2021) İyileştirilmiş su çevrim algoritmasıyla optimal yük akışı. Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi 36 2 1119–1132.
IEEE O. Akdağ ve C. Yeroğlu, “İyileştirilmiş su çevrim algoritmasıyla optimal yük akışı”, GUMMFD, c. 36, sy. 2, ss. 1119–1132, 2021, doi: 10.17341/gazimmfd.550770.
ISNAD Akdağ, Ozan - Yeroğlu, Celaleddin. “İyileştirilmiş Su çevrim algoritmasıyla Optimal yük akışı”. Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi 36/2 (Mart 2021), 1119-1132. https://doi.org/10.17341/gazimmfd.550770.
JAMA Akdağ O, Yeroğlu C. İyileştirilmiş su çevrim algoritmasıyla optimal yük akışı. GUMMFD. 2021;36:1119–1132.
MLA Akdağ, Ozan ve Celaleddin Yeroğlu. “İyileştirilmiş Su çevrim algoritmasıyla Optimal yük akışı”. Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi, c. 36, sy. 2, 2021, ss. 1119-32, doi:10.17341/gazimmfd.550770.
Vancouver Akdağ O, Yeroğlu C. İyileştirilmiş su çevrim algoritmasıyla optimal yük akışı. GUMMFD. 2021;36(2):1119-32.