Evrimsel algoritma performanslarının güç akışı analizinde karşılaştırılması

Enerji sistemlerinde güç akışı, önemli problemlerden biridir. Bu problemin çözümü için farklı klasik çözümleme yöntemlerinden faydalanılmaktadır. Ancak sistemdeki jeneratörlerin güç üretme limitleri, valf yükleme etkileri gibi parametreler güç akışı probleminin ilgili yollarla çözümünü zorlaştırmaktadır. Bu durumda evrimsel algoritmalarla en uygun çözümleri gerçekleştirmek mümkün olabilmektedir. Gerçekleştirilen çalışmada optimal güç akışı problemlerinin çözümü, iki farklı durumda 30 baralı IEEE test sisteminde güncel evrimsel algoritmalar ile eşit başlangıç şartlarıyla karşılaştırmalı olarak gerçekleştirilmiş ve ilgili algoritmaların performans değerlendirmeleri yapılmıştır. Algoritmaların enerji kazanımları elde edilmiş; optimizasyon sonucunda elde edilen en iyi, en kötü ve ortalama değerleri hesaplanmış; yakınsama analizleri karşılaştırmalı olarak gerçekleştirilmiştir. Böylece optimal güç akışı probleminin çözümünde evrimsel algoritmaların etkinlik ve verimlilikleri açıkça ortaya konulmuştur.

Comparison of the evolutionary algorithm's performances on power flow analysis

Power flow in energy systems is one of the major problems. Several classical analysis methods are utilized for solving this problem. However, power generation limits, valve loading effects of units also makes the power flow problem become much harder to solve in the system. In this case, it is possible to achieve the most appropriate solutions with evolutionary algorithms. In this study, optimal power flow problems are solved under same beginning conditions, comprehensively performed with evolutionary algorithms which are recently used and associated algorithm performance is analyzed in IEEE 30-bus test system for two cases. Energy gains of algorithms are obtained; the best, worst and mean values found from optimization are evaluated; convergence analyses are performed comparatively. Thus the effectiveness and efficiency of evolutionary algorithms are clearly demonstrated on solution of optimal power flow problems.

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  • Carpentier J. "Contribution to the economic dispatch problem". Bulletin de la Societe Francoise des Electriciens, 3(8), 431-447, 1962.
  • Sun DI, Ashley B, Brewer B, Hughes, A, Tinney W. "Optimal power flow by newton approach". IEEE Transaction on Power Apparatus and Systems, 103(10), 2864-2880, 1984.
  • Habiabollahzadeh H, Luo GX, Semlyen A. “Hydrothermal optimal power flow based on combined linear and nonlinear programming methodology”. IEEE Transaction on Power Apparatus and Systems, 4(2), 530-537, 1989.
  • Momoh JA, El-Hawary ME, Adapa R. “A review of selected optimal power flow literature to 1993. II. Newton, linear programming and interior point methods”. IEEE Transactions Power Systems, 14, 105-111, 1999.
  • Bakistzis A, Biskas P, Zoumas C, Petridis V. "Optimal power flow by enhanced genetic algorithm". IEEE Transaction on Power Systems, 17, 229-236, 2002.
  • Abou EEA, Abido M, Spea S. "Optimal power flow using differential evolution algorithm". Electrical Engineering, 91(2), 69-78, 2009.
  • Abido M. "Optimal power flow using particle swarm optimization". International Journal of Electrical Power & Energy Systems, 24(7), 563-571, 2002.
  • Roa-Sepulveda C, Pavez-Lazo B. "A solution to the optimal power flow using simulated annealing". International Journal of Electrical Power & Energy Systems, 25(1), 47-57, 2003.
  • Sinsuphan N, Leeton U, Kulworawanichpong T. "Optimal power flow solution using ımproved harmony search method". Applied Soft Computing, 13(5), 2364-2374, 2013.
  • Aruldoss T, Victoire A, Jeyakumar AE. "Hybrid PSO-SQP for economic dispatch with valve-point effect". Electric Power Systems Research, 71(1), 51-59, 2004.
  • Nikham T, Narimani M, Azizipanah-Abarghooee R. "A new hybrid algorithm for optimal power flow considering prohibited zones and valve point effect". Energy Conversion and Management, 58, 79-95, 2012.
  • Karaboga D. "An Idea based on honey bee swarm for numerical optimization". Computer Engineering Department, Erciyes University, Erciyes, Turkey, Technical Report, TR06, 2005.
  • Yang X, Deb S. "Cuckoo Search via lévy flights". World Congress on Nature & Biologically Inspired Computing, Coimbatore, India, 9-11 December 2009.
  • Yang X, He X. "Firefly algorithm: recent advances and applications". International Journal Swarm Intelligence, 1(1), 36-50, 2013.
  • Goldberg D. "Real-Coded genetic algorithms, virtual alphabets, and blocking". Complex Systems, 2, 139-168, 1991.
  • Yang X. Harmony Search as a Metaheuristic Algorithm. Editors: Geem ZW. Music-Inspired Harmony Search Algorithm, 1-14, Berlin, Heidelberg, Germany, Springer, 2009.
  • Kennedy, J, Eberhart RC. “Particle swarm optimization”. Proceedings of IEEE International Conference on Neural Networks, Piscataway, New Jersey, USA, 27 November-1 December, 1995.
  • Kirkpatrick S, Vecchi M, Gelatt C. "Optimization by simulated annealing". Science, 220(4598), 671-680, 1983.
  • Storn R, Price K. "Differential Evolution-a Simple and Efficient Adaptive Scheme for Global Optimization over Continuous Spaces". International Computer Science Institute, Berkley, USA, Technical Report, TR95, 1995.
  • Power Flow Test Cases, IEEE 30-Bus Test System Data. https://www.ee.washington.edu/research/pstca7pf30/ pg_tca30bus.htm (10.09.2015).
  • The MathWorks Inc. “MATLAB (2007)”.
  • Niknam T, Narimani MR, Azizipanah-Abarghooee R. “A new hybrid algorithm for optimal power flow considering prohibited zones and valve point effect”. Energy Conversion and Management, 58, 197-206, 2012.