Solution of Reactive Power Planning Problem Including FACTS Devices by using PSOGSA Algorithm

Yıl 2018, Cilt: 6 Sayı: 4, 1234 - 1257, 01.08.2018

Serhat Duman

https://doi.org/10.29130/dubited.439984

Cited By: 1

Öz

Optimal reactive power planning problem is one of the most important problems of the modern power systems. The main goal of the reactive power planning in modern power systems is to improve voltage profile and to reduce active power loss of the transmissions line. In this study, solution of the reactive power planning problem including FACTS devices is proposed by using hybrid PSOGSA algorithm. The proposed algorithm was applied to IEEE 30 bus test system with FACTS devices, such as thyristor control series compensator and thyristor control phase shifter. The obtained results from the proposed PSOGSA approach are compared to the obtained results from the vortex algorithm (VS), firefly algorithm (FA) and gravitational search algorithm (GSA). The comparison results demonstrate the superiority of the proposed approach to the other algorithms. 

Anahtar Kelimeler

Optimal reactive power planning, power systems, Hybrid PSOGSA, Optimization

FACTS Cihazlarını İçeren Reaktif Güç Planlama Probleminin Hibrit PSOGSA Algoritması Kullanarak Çözülmesi

Öz

Optimal reaktif güç planlama problemi
modern güç sistemlerinin en önemli problemlerinden biridir. Modern güç
sistemlerinde reaktif güç planlamanın ana amacı, gerilim profilini iyileştirmek
ve iletim hattının aktif güç kayıplarını azaltmaktır. Bu çalışmada, hibrit
PSOGSA algoritması kullanılarak FACTS cihazlarını içeren reaktif güç planlama
probleminin çözülmesi amaçlanmıştır. Amaçlanan algoritma, tristör kontrollü
seri kapasitör ve tristör kontrollü faz kaydırıcı FACTS cihazlı IEEE 30 bara
test sistemine uygulanmıştır. Amaçlanan hibrit PSOGSA yaklaşımından elde edilen
sonuçlar girdap algoritması (VS), ateş böceği algoritması (FA) ve yerçekimsel
arama algoritmasından elde edilen sonuçlarla karşılaştırılmıştır. Karşılaştırma
sonuçları amaçlanan yaklaşımın kullanılan diğer algoritmalara üstünlüğünü
göstermektedir.

Anahtar Kelimeler

Optimal reaktif güç planlama, Güç Sistemleri, Hibrit PSOGSA

Kaynakça

• [1] K. Nuaekaew, P. Artrit, N. Pholdee, S. Bureerat, “Optimal reactive power dispatch problem using a two-archive multi-objective grey wolf optimizer,” Expert Systems With Applications, c. 87, ss. 79–89, 2017.
• [2] M. Basu, “Multi-objective optimal reactive power dispatch using multi-objective differential evolution,” International Journal of Electrical Power & Energy Systems, c. 82, ss. 213–224, 2016.
• [3] M. Mehdinejad, B. Mohammadi-Ivatloo, R. Dadashzadeh-Bonab, K. Zare, “Solution of optimal reactive power dispatch of power systems using hybrid particle swarm optimization and imperialist competitive algorithms,” International Journal of Electrical Power & Energy Systems, c. 83, ss. 104–116, 2016.
• [4] R. N. S. Mei, M. H. Sulaiman, Z. Mustaffa, H. Daniyal, “Optimal reactive power dispatch solution by loss minimization using moth-flame optimization technique,” Applied Soft Computing, c. 59, ss. 210–222, 2017.
• [5] A. A. Heidari, R. A. Abbaspour, A. R. Jordehi, “Gaussian bare-bones water cycle algorithm for optimal reactive power dispatch in electrical power systems,” Applied Soft Computing, c. 57, ss. 657–671, 2017.
• [6] M. H. Sulaiman, Z. Mustaffa, M. R. Mohamed, O. Aliman, “Using the gray wolf optimizer for solving optimal reactive power dispatch problem,” Applied Soft Computing, c. 32, ss. 286–292, 2015.
• [7] A. Rajan, T. Malakar, “Exchange market algorithm based optimum reactive power dispatch,” Applied Soft Computing, c. 43, ss. 320–336, 2016.
• [8] A. H. Khazali, M. Kalantar, “Optimal reactive power dispatch based on harmony search algorithm,” International Journal of Electrical Power & Energy Systems, c. 33, ss. 684–692, 2011.
• [9] K. Y. Lee, Y. M. Park, J. L. Ortiz, “A united approach to optimal real and reactive power dispatch,” IEEE Transactions on Power Apparatus and Systems, c. 104, s. 5, ss. 1147–1153, 1985.
• [10] N. Deeb, S. M. Shahidehpour, “Linear reactive power optimization in a large power network using the decomposition approach,” IEEE Transactions on Power Systems, c. 5, s. 2, ss. 428–438, 1990.
• [11] S. Granville, “Optimal reactive dispatch through interior point methods,” IEEE Transactions on Power Systems, c. 9, s. 1, ss. 136–146, 1994.
• [12] G. Chen, L. Liu, Z. Zhang, S. Huang, “Optimal reactive power dispatch by improved GSA based algorithm with the novel strategies to handle constraints,” Applied Soft Computing, c. 50, ss. 58–70, 2017.
• [13] B. Mandal, P. K. Roy, “Optimal reactive power dispatch using quasi-oppositional teachinglearning based optimization,” International Journal of Electrical Power & Energy Systems, c. 53, ss. 123–134, 2013.
• [14] R. P. Singh, V. Mukherjee, S. P. Ghoshal, “Optimal reactive power dispatch by particle swarm optimization with an aging leader and challengers,” Applied Soft Computing, c. 29, ss. 298–309, 2015.
• [15] E. Naderi, H. Narimani, M. Fathi, M. R. Narimani, “A novel fuzzy adaptive configuration of particle swarm optimization to solve large-scale optimal reactive power dispatch,” Applied Soft Computing, c. 53, ss. 441–456, 2017.
• [16] M. Ghasemi, S. Ghavidel, M. M. Ghanbarian, A. Habibi, “A new hybrid algorithm for optimal reactive power dispatch problem with discrete and continuous control variables,” Applied Soft Computing, c. 22, ss. 126–140, 2014.
• [17] P. Subbaraj, P. N. Rajnarayanan, “Optimal reactive power dispatch using self-adaptive real coded genetic algorithm,” Electric Power Systems Research, c. 79, ss. 374–381, 2009.
• [18] M. Varadarajan, K. S. Swarup, “Differential evolution approach for optimal reactive power dispatch,” Applied Soft Computing, c. 8, ss. 1549–1561, 2008.
• [19] M. Ghasemi, M. Taghizadeh, S. Ghavidel, J. Aghaei,A. Abbasian, “Solving optimal reactive power dispatch problem using a novel teaching–learning-based optimization algorithm,” Engineering Applications of Artificial Intelligence, c. 39, ss. 100–108, 2015.
• [20] M. Basu, “Quasi-oppositional differential evolution for optimal reactive power dispatch,” International Journal of Electrical Power & Energy Systems, c. 78, ss. 29–40, 2016.
• [21] Q. H. Wu, Y. J. Cao, J. Y. Wen, “Optimal reactive power dispatch using an adaptive genetic algorithm,” International Journal of Electrical Power & Energy Systems, c. 20, s.8, ss. 563–569, 1998.
• [22] P. K. Roy, S. P. Ghoshal, S. S. Thakur, “Optimal reactive power dispatch considering flexible AC transmission system devices using biogeography-based optimization,” Electric Power Components and Systems, c. 39, s.11, ss. 733–750, 2011.
• [23] M. Sedighizadeh, H. Faramarzi, M. M. Mahmoodi, M. Sarvi, “Hybrid approach to FACTS devices allocation using multi-objective function with NSPSO and NSGA-II algorithms in Fuzzy framework,” International Journal of Electrical Power & Energy Systems, c. 62, ss. 586–598, 2014.
• [24] D. Prasad, M. Mukherjee, “Solution of optimal reactive power dispatch by symbiotic organism search algorithm incorporating FACTS devices,” IETE Journal of Research, c. 64, s.1, ss. 149–160, 2018.
• [25] S. Dutta, P. K. Roy, D. Nandi, “Optimal location of STATCOM using chemical reaction optimization for reactive power dispatch problem,” Ain Shams Engineering Journal, c. 7, s. 1, ss. 233–247, 2016.
• [26] S. Dutta, S. Paul, P. K. Roy, “Optimal allocation of SVC and TCSC using quasi-oppositional chemical reaction optimization for solving multi-objective ORPD problem,” Journal of Electrical Systems and Information Technology, c. 5, s. 1, ss. 83–98, 2018.
• [27] S. Mirjalili, S. Z. M. Hashim, “A new hybrid PSOGSA algorithm for function optimization,” International Conference on Computer and Information Application (ICCIA 2010), Tianjin, China, 2010, ss. 374–377.
• [28] B. Doğan, T. Ölmez, “A new metaheuristic for numerical function optimization: Vortex Search algorithm,” Information Sciences, c. 293, ss. 125–145, 2015.
• [29] Yang X.S., “Firefly algorithms for multimodal optimization,” Stochastic Algorithms: Foundations and Appplications, SAGA 2009, Lecture Notes in Computer Science, c. 5792, ss.169–178, 2009.
• [30] E. Rashedi, H. Nezamabadi-pour, S. Saryazdi, “GSA: A gravitational search algortihm,” Information Sciences, c. 179, s. 13, ss. 2232–2248, 2009.
• [31] S. Duman, U. Güvenç, Y. Sönmez, N. Yörükeren, “Optimal power flow using gravitational search algorithm,” Energy Conversion and Management, c. 59, ss. 86–95, 2012.
• [32] Y. Kumar, G. Sahoo, “A review on gravitational search algorithm and its applications to data clustering & classification,” I.J. Intelligent Systems and Applications, c. 06, ss. 79–93, 2014.
• [33] N. M. Sabri, M. Puteh, M. R. Mahmood, “An overview of gravitational search algorithmutilization in optimization problems,” 2013 IEEE 3rd International Conference on System Engineering and Technology, Shah Alam, Malaysia, 2013, ss. 61–66.
• [34] J. Kennedy, R. C. Eberhart, “Particle swarm optimization,” International Conference on Neural Networks, Perth, WA, Australia, Australia, 1995, ss. 1942–1948.
• [35] IEEE 30-bus test system data http://www.ee.washington.edu/research/pstca/pf30/pg_tca30bus.htm