The results show that the IGA is more accurate than the GA and global optimal solutions can be found by the IGA.
📌 3 Maddede Bu Makale
🎯
Amaç & Hipotez:In this paper two conventional random search methods that are Genetic Algorithm and Immune Genetic Algorithm has been compared in optimal power flow problem in power system.
📊
Temel Bulgular & Veri:The IEEE 14-bus test system has been selected as case study.
💡
Sonuç & Katkı:The results show that the IGA is more accurate than the GA and global optimal solutions can be found by the IGA.
🔬Tür:Araştırma Makalesi
📍Kapsam:Genişletilmiş Akademik Örneklem
⚙️Değişkenler:Alan Parametreleri
Abstract
In this paper two conventional random search methods that are Genetic Algorithm and Immune Genetic Algorithm has been compared in optimal power flow problem in power system. The IEEE 14-bus test system has been selected as case study. This comparison has been done in equal conditions for two algorithms. Objective function in this problem is the minimization of cost of network losses and the cost of reactive power injection in the period of five years. Control variables are voltage magnitude of generator buses, active power of generators and reactive power injection of load buses. The results show that the IGA is more accurate than the GA and global optimal solutions can be found by the IGA.
🔖
Bu Makaleye Atıf Yap
GALVANI, S., HOSEINIAN, H., LAFZI, A. (2022). Immune genetic algorithm performance in optimization of power flow in power systems. Istanbul University Journal of Electrical and Electronics Engineering, Cilt: 10 - Sayı: 2, 1229–1234. https://ajindex.com/articles/543720
W. Huang, C. Xu, J. Zhang, S. Hu, "Study of Reactive Power Optimization Based on Immune Genetic Algorithm", Transmission and Distribution Conference and Exposition, 2003 IEEE PES, Vol. 1, pp. 186–190, Sept. 2003
W. Lei, J. Licheng, "The Immune Genetic Algorithm and Its Convergence", Fourth International Conference on Signal Proceedings, Vol. 2, pp. 1347–1350, Oct. 1998
Z. Haibo, Z, Lizi, M. Fanling, "Reactive Power Optimization Based on Genetic Algorithm", International Conference on Power System Technology, Vol. 2, pp. 1448-1453, Aug. 1998
H. Shyh-Jier, "An Immune Based Optimization Method to Capacitor Placement in a Radial Distribution System", IEEE Transaction on Power Delivery, Vol. 15, Issue 2, pp. 744–749
Z. Junmin, H. Tinghua, Z. Honggang, "The Reactive Power Optimization of Distribution Network Based on an Improved Genetic Algorithm", Asia and Pacific Transmission and Distribution Conference and Exhibition, pp. 1–4, Aug. 2005
J. Licheng, W. Lei, "A Novel Genetic Algorithm Based on Immunity", IEEE Transaction on Systems, Man and Cybernetics, Part A, Vol. 30, Issue 5, pp. 552–561, Sept. 2000
J. Timmis, C. Edmonds, J. Kelsey, "Assessing the Performance of Two Immune Inspired Algorithms and a Hybrid Genetic Algorithm for Function Optimization", Congress on Evolutionary Computation, Vol. 1, pp. 1044–1051, June 2004
Q. H. Wu, J.T. Ma, "Genetic Search for Optimal Reactive Power Dispatch ff Power Systems", International Conference on Control, vol. 1, pp. 717–722, April 2000
Y. Malachi, S. Singer, "A Genetic Algorithm for the Corrective Control of Voltage and Reactive Power", IEEE Transaction on Power Systems, Vol. 21, pp. 295– 300, Feb. 2006.