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İyileştirilmiş Öğretme-Öğrenme Tabanlı Optimizasyon Algoritmasıyla Güç Sistemlerinde Gerilim Kararlılığı Çalışması

Yıl 2023, Cilt: 11 Sayı: 3, 695 - 705, 27.09.2023
https://doi.org/10.29109/gujsc.1282188

Öz

Bu çalışmada, son yıllarda geliştirilen öğrenme-öğretme tabanlı optimizasyon algoritması (ÖÖTO) yeniden düzenlenerek, güç sistemlerinde gerilim kararlılığı için yeni bir optimizasyon yöntemi geliştirilmiştir. Düzenlenen öğrenme-öğretme tabanlı optimizasyon (D-ÖÖTO) algoritması, IEEE 14 baralı ve Türkiye, İstanbul Anadolu yakasında 17 baralı gerçek bir güç sistemi kullanılarak gerilim kararlılığı optimizasyonu olarak sunulmuştur. Bu güç sistemlerinde, beş farklı durum (temel durum, temel durumda ki talep edilen yükün %20, %40 ve %60 artışı ve 1-5 nolu hat kesintisi) oluşturulmuş ve analizler gerçekleştirilmiştir. Daha sonra yük baralarına şönt reaktif güç kompansatörleri (RGK) bağlanarak gerilim kararlılığı açısından etkisi incelenmiştir. Sunulan D-ÖÖTO algoritmasının etkinliğini kanıtlamak için orijinal ÖÖTO ve literatürde kullanılan Yerçekimi arama algoritması (YAA), parçacık sürü optimizasyonu (PSO) ve Newton-Raphson güç akış yönetimi sonuçlarıyla karşılaştırılmıştır. Tüm çalışma koşullarında sunulan D-ÖÖTO algoritması diğer yöntemlere göre üstünlüğü kanıtlanmıştır. Tüm analizler, Intel Core(TM) i7-2620 2.7GHz ve 8.00 (64 bit) Gb Ram PC kullanılarak, Matlab R2017b programında çözümlenmiştir.

Kaynakça

  • [1] Steinmetz, C.P. (1920). Power Control and Stability of Electric Generating Stations. American Institute of Electrical Engineers Transmission, 39(2), 1215-1287.
  • [2] AIEE Subcommittee on Interconnections and Stability Factors (1937). First Report of Power System Stability. American Institute of Electrical Engineers Transmission, 56(2), 261-282.
  • [3] Evans, R. and Bergvall, R. (1924). Experimental Analysis of Stability and Power Limitations. Transactions of the American Institute of Electrical Engineers, XLIII, 39-58.
  • [4] Farmer, R. (2001). Power System Dynamics and Stability. The Electric Power Engineering Handbook, Arizona State University, Ed.LL. Grigsby, 30-97.
  • [5] Kundur, P., Paserba, J., Ajjarapu, V., Andersson, G., Bose, A., Canizares, C., Hatziargyriou, N., Hill, D., Stankovic, A., Taylor, C., Cutsem, R. and Vittal, V. (2004). Definition and Classification of Power System Stability. IEEE Transaction on Power Systems, 19(2), 1387-1401.
  • [6] Vassel, G.S. (1991). Northcast Blacout of 1965. IEEE Power Engineering Review, 11(1), 4-8.
  • [7] Van Cutsem, T. and Vournas, C. (1998). Voltage Stability of Electric Power Systems. Norwell, MA: Kluwer, 213-264.
  • [8] Onksakul, W. and Jirapong, P. (2005). Optimal Allocation of FACTS Devices to Enhance Total Transfer Capability Using Evolutionary Programming. IEEE International Symposium on Circuits and Systems, Kobe, Japan.
  • [9] Ravi, V. and Duraiswamy, K. (2012). Effective Optimization Technique for Power System Stabilization using Artificial Bee Colony. International Conference on Computer Communication and Informatics, Coimbatore, India.
  • [10] Deepa, S.N. and Rizwana, J. (2013). Power System Stability by Reducing Power Losses using Optimization Techniques. IEEE International Conference on Computational Intelligence and Computing Research, Enathi, India.
  • [11] Akachukwu, C.M., Aibinu, A.M., Nwohu M.N. and Salau, H.B. (2014). A Decade Survey of Engineering Applications of Genetic Algorithm in Power System Optimization. International Conference on Intelligent Systems, Modelling and Simulation, Langkawi, Malaysia.
  • [12] Chen, G., Liu, L., Guo, Y. and Huang, S. (2015). Multi-Objective Enhanced PSO Algorithm for Optimizing Power Losses and Voltage Deviation in Power Systems. The International Journal for Computation and Mathematics in Electrical and Electronic Engineering, 35(1), 350-372.
  • [13] Singh, H. and Srivastava, L. (2016). Optimal VAR Control for Real Power Loss Minimization and Voltage Stability Improvement Using Hybrid Multi-Swarm PSO. IEEE International Conference on Circuit, Power and Computing Technologies, Nagercoil, India.
  • [14] Amrane, Y., Elmaouhab, A., Boudour, M. and Ladjici, A.A. (2017). Voltage Stability Analysis Based on Multi-Objective Optimal Reactive Power Dispatch Under Various Contingency. International Journal on Electrical Engineering and Informatics, 9(3), 521-541.
  • [15] Li, S., Gong, W., Hu, C., Yan, X., Wang, L. and Gu, Q. (2021). Adaptive Constraint Differential Evolution For Optimal Power Flow. Energy, 235, 121362.
  • [16] Ermiş, S. (2018). Güç Sistemlerinde Gerilim Kararliliğinin Optimizasyonunda Yeni Bir Akilli Yöntem Geliştirilmesi ve Uygulamasi”, Gazi Üniversitesi Fen Bilimleri Enstitüsü, Doktora Tezi.
  • [17] Abou El Ela, A.A., Abido, M.A. and Spea, S.R. (2010). Optimal Power Flow Using Differential Evolution Algorithm. Electric Power Systems Research, 80(7), 878-885.
  • [18] Abido, M.A. and Bakhashwain, J.M. (2005). Optimal VAR Dispatch Using a Multiobjective Evolutionary Algorithm. International Journal of Electrical Power & Energy Systems, 27(1), 13-20.
  • [19] Rao, V.R. and Patel, V. (2012). An Elitist Teaching-Learning-Based Optimization Algorithm For Solving Complex Constrained Optimization Problems. International Journal of Industrial Engineering Computations, 3(4), 535-560.
  • [20] Rao, R.V., Savsani, V.J. and Vakharia, D.P. (2011). Teaching-Learning-Based Optimization: A Novel Method for Constrained Mechanical Design Optimization Problems. Computer Aided Design, 43(3), 303-315.
  • [21] Yeşilbudak, M., Ermiş, S. and Bayındır, R. (2017). Farklı Baralara Sahip Güç Sistemlerinde Yük Akışı Analiz Metotlarının Karşılaştırılması. Gazi Üniversitesi Fen Bilimleri Dergisi Part C: Tasarım ve Teknoloji, 5(3), 237-246.
  • [22] İnternet: IEEE 14 Bus Power Flow Test Case (2008). URL:.http://www.webcitation.org/query?url =http%3A%2F%2Flabs.ece.uw.edu%2Fpstca%2Fpf14%2Fpg_tca14Bara.htm+&date=2018-11-20 adresinden 15.04.2017.

Voltage Stabılıty Study In Power Systems Wıth Improved Teachıng-Learnıng Based Optımızatıon Algorıthm

Yıl 2023, Cilt: 11 Sayı: 3, 695 - 705, 27.09.2023
https://doi.org/10.29109/gujsc.1282188

Öz

In this study, a new optimization method has been developed for voltage stability in power systems by rearranging the learning-teaching based optimization algorithm (ÖÖTO) developed in recent years. The designed learning-teaching based optimization (D-ÖÖTO) algorithm is presented as voltage stability optimization using a real power system with IEEE 14 bus and 17 bus on the Anatolian side of Istanbul, Turkey. In these power systems, five different situations (base state, 20%, 40% and 60% increase of the demanded load in the base state and line 1-5 interruption) were created and analyzes were carried out. Then, shunt reactive power compensators (RGK) were connected to the load busbars and their effect in terms of voltage stability was examined. In order to prove the effectiveness of the presented D-ÖÖTO algorithm, the original ÖÖTO and the gravity search algorithm (YAA) used in the literature were compared with the results of particle swarm optimization (PSO) and Newton-Raphson power flow management. The D-ÖÖTO algorithm presented in all operating conditions has been proven to be superior to other methods. All analyzes were analyzed in Matlab R2017b program using Intel Core(TM) i7-2620 2.7GHz and 8.00 (64 bit) Gb Ram PC.

Kaynakça

  • [1] Steinmetz, C.P. (1920). Power Control and Stability of Electric Generating Stations. American Institute of Electrical Engineers Transmission, 39(2), 1215-1287.
  • [2] AIEE Subcommittee on Interconnections and Stability Factors (1937). First Report of Power System Stability. American Institute of Electrical Engineers Transmission, 56(2), 261-282.
  • [3] Evans, R. and Bergvall, R. (1924). Experimental Analysis of Stability and Power Limitations. Transactions of the American Institute of Electrical Engineers, XLIII, 39-58.
  • [4] Farmer, R. (2001). Power System Dynamics and Stability. The Electric Power Engineering Handbook, Arizona State University, Ed.LL. Grigsby, 30-97.
  • [5] Kundur, P., Paserba, J., Ajjarapu, V., Andersson, G., Bose, A., Canizares, C., Hatziargyriou, N., Hill, D., Stankovic, A., Taylor, C., Cutsem, R. and Vittal, V. (2004). Definition and Classification of Power System Stability. IEEE Transaction on Power Systems, 19(2), 1387-1401.
  • [6] Vassel, G.S. (1991). Northcast Blacout of 1965. IEEE Power Engineering Review, 11(1), 4-8.
  • [7] Van Cutsem, T. and Vournas, C. (1998). Voltage Stability of Electric Power Systems. Norwell, MA: Kluwer, 213-264.
  • [8] Onksakul, W. and Jirapong, P. (2005). Optimal Allocation of FACTS Devices to Enhance Total Transfer Capability Using Evolutionary Programming. IEEE International Symposium on Circuits and Systems, Kobe, Japan.
  • [9] Ravi, V. and Duraiswamy, K. (2012). Effective Optimization Technique for Power System Stabilization using Artificial Bee Colony. International Conference on Computer Communication and Informatics, Coimbatore, India.
  • [10] Deepa, S.N. and Rizwana, J. (2013). Power System Stability by Reducing Power Losses using Optimization Techniques. IEEE International Conference on Computational Intelligence and Computing Research, Enathi, India.
  • [11] Akachukwu, C.M., Aibinu, A.M., Nwohu M.N. and Salau, H.B. (2014). A Decade Survey of Engineering Applications of Genetic Algorithm in Power System Optimization. International Conference on Intelligent Systems, Modelling and Simulation, Langkawi, Malaysia.
  • [12] Chen, G., Liu, L., Guo, Y. and Huang, S. (2015). Multi-Objective Enhanced PSO Algorithm for Optimizing Power Losses and Voltage Deviation in Power Systems. The International Journal for Computation and Mathematics in Electrical and Electronic Engineering, 35(1), 350-372.
  • [13] Singh, H. and Srivastava, L. (2016). Optimal VAR Control for Real Power Loss Minimization and Voltage Stability Improvement Using Hybrid Multi-Swarm PSO. IEEE International Conference on Circuit, Power and Computing Technologies, Nagercoil, India.
  • [14] Amrane, Y., Elmaouhab, A., Boudour, M. and Ladjici, A.A. (2017). Voltage Stability Analysis Based on Multi-Objective Optimal Reactive Power Dispatch Under Various Contingency. International Journal on Electrical Engineering and Informatics, 9(3), 521-541.
  • [15] Li, S., Gong, W., Hu, C., Yan, X., Wang, L. and Gu, Q. (2021). Adaptive Constraint Differential Evolution For Optimal Power Flow. Energy, 235, 121362.
  • [16] Ermiş, S. (2018). Güç Sistemlerinde Gerilim Kararliliğinin Optimizasyonunda Yeni Bir Akilli Yöntem Geliştirilmesi ve Uygulamasi”, Gazi Üniversitesi Fen Bilimleri Enstitüsü, Doktora Tezi.
  • [17] Abou El Ela, A.A., Abido, M.A. and Spea, S.R. (2010). Optimal Power Flow Using Differential Evolution Algorithm. Electric Power Systems Research, 80(7), 878-885.
  • [18] Abido, M.A. and Bakhashwain, J.M. (2005). Optimal VAR Dispatch Using a Multiobjective Evolutionary Algorithm. International Journal of Electrical Power & Energy Systems, 27(1), 13-20.
  • [19] Rao, V.R. and Patel, V. (2012). An Elitist Teaching-Learning-Based Optimization Algorithm For Solving Complex Constrained Optimization Problems. International Journal of Industrial Engineering Computations, 3(4), 535-560.
  • [20] Rao, R.V., Savsani, V.J. and Vakharia, D.P. (2011). Teaching-Learning-Based Optimization: A Novel Method for Constrained Mechanical Design Optimization Problems. Computer Aided Design, 43(3), 303-315.
  • [21] Yeşilbudak, M., Ermiş, S. and Bayındır, R. (2017). Farklı Baralara Sahip Güç Sistemlerinde Yük Akışı Analiz Metotlarının Karşılaştırılması. Gazi Üniversitesi Fen Bilimleri Dergisi Part C: Tasarım ve Teknoloji, 5(3), 237-246.
  • [22] İnternet: IEEE 14 Bus Power Flow Test Case (2008). URL:.http://www.webcitation.org/query?url =http%3A%2F%2Flabs.ece.uw.edu%2Fpstca%2Fpf14%2Fpg_tca14Bara.htm+&date=2018-11-20 adresinden 15.04.2017.
Toplam 22 adet kaynakça vardır.

Ayrıntılar

Birincil Dil Türkçe
Konular Mühendislik
Bölüm Tasarım ve Teknoloji
Yazarlar

Salih Ermiş 0000-0002-1053-9160

Ramazan Bayındır 0000-0001-6424-0343

Mehmet Yeşilbudak 0000-0002-9739-5883

Erken Görünüm Tarihi 5 Ağustos 2023
Yayımlanma Tarihi 27 Eylül 2023
Gönderilme Tarihi 12 Nisan 2023
Yayımlandığı Sayı Yıl 2023 Cilt: 11 Sayı: 3

Kaynak Göster

APA Ermiş, S., Bayındır, R., & Yeşilbudak, M. (2023). İyileştirilmiş Öğretme-Öğrenme Tabanlı Optimizasyon Algoritmasıyla Güç Sistemlerinde Gerilim Kararlılığı Çalışması. Gazi University Journal of Science Part C: Design and Technology, 11(3), 695-705. https://doi.org/10.29109/gujsc.1282188

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