استفاده از روش تركيبي PSO – GA جهت جايابي بهينة خازن در سیستم‌های توزيع
محورهای موضوعی :محمدهادی ورهرام 1 , امیر محمدی 2
1 - وزارت علوم تحقیقات و فناوری
2 - دانشگاه تهران
کلید واژه: جایابی خازن, الگوریتم ژنتیک, بهینه‌سازی تجمّع ذرّات,
چکیده مقاله :
در اين مقاله ، ما يك الگوريتم جديد پيشنهاد كرده‌ايم كه PSO و ژنتيك را به طريقي با هم تركيب می‌کند بگونه‌اي كه الگوريتم جديد مؤثرتر و كارآمدتر می‌شود. اين بدان معناست که سرعت رسيدن به پاسخ به طور قابل ملاحظه‌اي افزايش مي‌يابد و در عين حال دقّت پاسخ نيز به مراتب بالاتر است. خاصيت الگوريتم بهينه‌سازي تجمّع اين است که به سرعت همگرا مي‌شود ، اما در نزديكي‌هاي نقطة بهينه فرآيند جستجو به شدّت كند مي‌شود . از طرفی می‌دانیم که الگوريتم ژنتيك نيز به شرايط اوليه به شدت حساس است. در حقيقت طبيعت تصادفي عملگرهاي ژنتيك ، الگوريتم را به جمعیّت اوليّه حساس مي‌کند. اين وابستگي به شرايط اوليه به گونه‌اي است كه اگر جمعیّت اوليه خوب انتخاب نشود ، الگوريتم ممكن است همگرا نشود. در اين مقاله با استفاده از اين الگوريتم تركيبي GA- PSO، مكان و اندازة بهينة خازن در يك سيستم توزيع نمونه بدست آمده است . همچنين جايابي بهينة خازن با الگوريتم هاي PSO و GA بطور جداگانه بدست و نتايج با هم مقايسه شده‌اند .نتايج نشان می‌دهند که الگوريتم جديد مي‌تواند سريع‌تر به پاسخ برسد و به جمعیّت اوليه وابسته نيست و پاسخ‌هاي دقيق‌تري را پيدا می‌کند.
In this paper, we have proposed a new algorithm which combines PSO and GA in such a way that the new algorithm is more effective and efficient.The particle swarm optimization (PSO) algorithm has shown rapid convergence during the initial stages of a global search but around global optimum, the search process will become very slow. On the other hand, genetic algorithm is very sensitive to the initial population. In fact, the random nature of the GA operators makes the algorithm sensitive to the initial population. This dependence to the initial population is in such a manner that the algorithm may not converge if the initial population is not well selected. This new algorithm can perform faster and does not depend on initial population and can find optimal solutions with acceptable accuracy. Optimal capacitor placement and sizing have been found using this hybrid PSO-GA algorithm. We have also found the optimal place and size of capacitors using GA and PSO separately and compared the results.
1. T. H. Fawzi, S. M. El-Sobki, and M. A. Abdel-Halim, "A New Approach for the application of Shunt Capacitors to the Primary Distribution Feeders," IEEE Transactions on Power Apparatus and Systems, vol. 102, pp. 10-13, January 1983.
2. H. N. Ng, M. M. A. Salama, and A. Y. Chikhani, "Capacitor Allocation by Approximate Reasoning: Fuzzy Capacitor Placement," IEEE Transactions of Power Delivery, vol. 15, pp. 393-398, January 2000.
3. S. J. Huang, "Immune-Based Optimization Method to Capacitor Placement in a Radial Distribution System," IEEE Transactions ofPower Delivery, vol. 15, pp. 744- 749, April 2000.
4. R. A. Gallego, A. J. Monticelli, and R. Romero, "Optimal Capacitor Placement in Radial Distribution Networks," IEEE Transactions of Power Delivery, vol. 16, pp. 630-637, November 2001.
5. M. A. S. Masoum, M. Ladjevardi, A. Jafarian, and E. F. Fuchs, "Optimal Placement, Replacement and Sizing of Capacitor Banks in Distorted Distribution networks by Genetic Algorithms," IEEE Transactions of Power Deliverv, vol. 19, pp. 1794-1801, October 2004.
6. distortion consideration", Electric Power Systems Research, vol. 7, pp. 27-33. January 2004
7. Jing-Ru Zhang, Jun Zhang, Tat-Ming Lok, Michael R. Lyu, " A hybrid particle swarm optimization–back-propagation algorithm for feedforward neural network training" Applied Mathematics and Computation Elsevier, 2006
8. Jose Miva, Jose Ramon, Alvarez "Artificial Intelligence and Knowledge Engineering Applications" Ebook
9. Ching-Tzong Su, Guor-Rurng Lii and Ching Cheng Tsai,"Optimal Capacitor Allocation Using Fuzzy Reasoning and Genetic Algorithms for Distribution Systems", Mathematical and Computer Modeling, Vol. 33, 2001
10. K.F.Man, K.S.Tang and S.Kwong, "Genetic Algorithm concepts and application", IEEE Trans. On Industrial Electronics, vol 43 no.5, pp.519-534,Oct 1996.
11. J.H. Holland, Adaptation in Natural and Artificial Systems. Ann Arbor, MI: The University of Michigan Press, 1975.
12. T.S. Chung, Y.Z. Li," A Hybrid GA Approach for OPF with Consideration of FACTS Devices", IEEE Power Engineering Review, February 2001
13. Betram Koh Lin Hon "Accelerated Genetic Algorithm in Power System Planning" Electrical Engineering thesis 2003
Xin-mei Yu, Xin-yin Xiong, Yao-wu Wu "A PSO-based approach to optimal capacitor placement with harmonic
14. Stephane Gerbex, Richard Cherkaoui and Alain.J.Germond " Optimal location of multi-type FACTS devices by means of Genetic algorithm" IEEE Trans. Power system Vol.16 pp 537-544 August 2001
15. James Kennedy and Russel Eberhart, "Particle Swarm Optimization" Proc. of IEEE International conference on neural networks, Vol 14, pp 1942 – 1948 December 1995
16. Yuhui Shi, Russel.C.Eberhart,
"Empirical study of particle swarm optimization", Proc. of the congress on Evolutionary computation, Vol.13, pp 1945-1950, July 1999
17.Murthy, K.V.S.R; RamalingaRaju, M.; Rao, G.G, "Comparison between conventional, GA and PSO with respect to optimal capacitor placement in agricultural distribution system ", Proc. of the 2010 Annual IEEE india conference, INDICON 2010, 17-19, pp 1-4
