Yalçın ALCAN, Ali ÖZTÜRK, Hasan DİRİK, Memnun DEMİR

Güç şebekelerinde minimum kayıpları sağlayan STATCOM konumunun ve değerinin belirlenmesinde farklı sezgisel algoritmaların karşılaştırılması

Güç sistemleri üzerine yapılan çalışmaların en önemli konularından biri toplam kayıpların azaltılmasıdır. Esnek A. A. iletim sistemleri (FACTS) bu amacın gerçekleştirilmesinde önemli bir araç olarak öne çıkmaktadır. Statik Senkron Kompanzatörler (STATCOM), söz konusu FACTS yapılarının çalışması bakımından arasında en esnek ve gelişmiş olanlarıdır. Bir güç sistemindeki toplam kayıplar STATCOM’un bağlı bulunduğu baranın konumu ve tepkin güç çıkış değerine göre değişir. Bu çalışmada dört farklı sezgisel algoritma kullanılarak güç sistemi kayıplarının minimumunu elde etmeyi sağlayacak en iyi STATCOM konumunun ve çıkış değerinin belirlenmesi ve böylece algoritmaların karşılaştırılması amaçlanmıştır. Kullanılan sezgisel yöntemler sırasıyla Yerçekimsel Arama Algoritması (YAA), Parçacık Sürü Optimizasyonu (PSO), Yapay Arı Kolonisi Algoritması (YAK) ve Karınca Kolonisi Algoritması (KKA) yöntemleridir. Yöntemler IEEE’nin 14 baralı test sistemine uygulanarak en az sistem kayıplarının elde edilebildiği STATCOM konumları ve bu konumlardaki STATCOM tepkin güç çıkış değerleri bulunmuştur. Elde edilen sonuçlar en uygun değeri bulma ve yakınsama hızı bakımından karşılaştırılmış ve tartışılmıştır.

Anahtar Kelimeler:

Optimal konum, STATCOM, Yerçekimsel arama algoritması, Parçacık sürü optimizasyonu, Yapay arı kolonisi, Karınca kolonisi algoritması

Comparison of different heuristic algorithms for location and value determination of STATCOM providing minimum losses in power systems

One of the most important issues of studies that have been done on the power systems is loss reduction. Flexible A. C. transmission systems (FACTS) give significant opportunities to realize this aim. Static Synchronous Compensators (STATCOMs) are the most flexible and sophisticated structure as compared to the other FACTS devices in terms of operation. Total losses of a power system change according to the location and reactive power output of STATCOM. In this work, it is aimed to find optimum location and output value of a STATCOM that provide minimum losses of power system by using four different heuristic algorithms and to compare these algorithms. Heuristic algorithms that are used in this paper are Gravitational Search Algorithm (GSA), Particle Swarm Optimization (PSO), Artificial Bee Colony (ABC) and Ant Colony Algorithm (ACA) respectively. These methods were applied to IEEE-14 bus test system, and optimum locations and output values of STATCOMs that provide minimum losses have been found. Also, results of all methods are compared and discussed in terms of finding proper value and convergence speed.

Keywords:

Optimal location, STATCOM, Gravitational search algorithm, Particle swarm optimization, Artificial bee colony, Ant colony algorithm,

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Oliveira EJ, Marang JW, Pereira JLR. “Flexible AC transmission system devices: allocation and transmission”. Electrical Power and Energy Systems, 21(2), 111-118, 1999.
Jordehia AR, Jasni J, Wahab NA, Kadir MZ, Javadi MS. “Enhanced leader PSO (ELPSO): A new algorithm for allocating distributed TCSC’s in power systems“. International Journal of Electrical Power & Energy Systems, 64, 771-784, 2015.
Sarker J, Goswami SK. “Solution of multiple UPFC placement problems using Gravitational Search Algorithm”. International Journal of Electrical Power & Energy Systems, 55, 531-541, 2014.
Eslamia M, Shareefa H, Khajehzadeh M. “Optimal design of damping controllers using a new hybrid artificial bee colony algorithm”. International Journal of Electrical Power & Energy Systems, 52, 42-54, 2013.
Padmavathi V, Sahu SK, Jayalaxmi A. “Comparison of hybrid differential evolution algorithm with genetic algorithm based power system security analysis using FACTS”. Journal Electrical Systems, 11(2), 189-202, 2015.
Bhattacharyya B, Gupta VK, Das S. “Evolutionary programming for reactive power planning using FACTS devices”. Wseas Transactıons on Power Systems, 9, 1-6, 2014.
Burade B, Helonde JB. “Congestion management Incorporation of FACTS devices using ant colony optimization”. Bulletin of Electrical Engineering and Informatics, 1(2), 139-150, 2012.
Sreejith S, Chandrasekaran K, Simon P. “Application of touring ant colony optimization technique for optimal power flow incorporating thyristor controlled series compensator”. TENCON 2009 - 2009 IEEE Region 10 Conference, Singapore, 23-26 November 2009.
Vijay Kumar B, Srikanth NV. “Optimal location and sizing of unified power flow controller (UPFC) to improve dynamic stability: A hybrid technique”. International Journal of Electrical Power & Energy System, 64, 429-438, 2015.
Sree Renga Raja T, Mangaiyarkarasi SP. “Optimal location and sizing of multiple static var compensators for voltage risk assessment using hybrid PSO-GSA algorithm”. Arabian Journal for Sciences and Engineering, 39(11), 7967-7980, 2014.
Kaveh A, Talatahari S. “A particle swarm ant colony optimization for truss structures with discrete variables”. Journal of Constructional Steel Research, 65(8-9),1558-1568, 2009.
Safari A, Ahmadian A, Golkar MA. “Comparıson of honey bee mating optimization and genetic algorithm for coordinated design of pss and STATCOM based on damping of power system oscillation”. Journal of Electrical Engineering, 64(3), 133-142, 2013.
Safari A, Ahmadian A, Golkar MAA. “Controller design of STATCOM for power system stability ımprovement using honey bee mating optimization”. Journal of Applied Research and Technology, 11(1), 144-155, 2013.
Idris RM, Khairuddin A, Mustafa MW. “Optimal allocation of facts devices for atc enhancement using bees algorithm”. International Journal of Electrical, Computer, Energetic, Electronic and Communication Engineering, 3(6), 1295-1302, 2009.
Sreejith S, Simon SP, Selvan MP. “Analysis of FACTS devices on security constrained unit commitment problem”. Electrical Power and Energy Systems, 66, 280–293, 2015.
Satheesh A, Manigandan T. “Maintaining power system stability with FACTS controller using bees algorithm and NN”. Journal of Theoretical and Applied Information Technology, 49(1), 38-47, 2013.
Abacı K, Yamaclı V, Akdağlı A. “Optimal power ﬂow with SVC devices by using the artiﬁcial bee colony algorithm”. Turkish Journal of Electrical Engineering & Computer Sciences, 24, 341-353, 2016.
Padmavathi V, Sahu SK, Jayalaxmi A. “Application of gravitational search algorithm to ımprove power system security by optimal placement of FACTS”. Journal Electrical Systems, 11(3), 326-342, 2015.
Jordehi RA. “Brainstorm optimisation algorithm (BSOA): An efficient algorithm for finding optimal location and setting of FACTS devices in electric power systems”. Electrical Power and Energy Systems, 69, 48-57, 2015.
Dutta S, Roy KP, Nandi D. “Optimal location of STATCOM using chemical reaction optimization for reactive power dispatch problem”. Ain Shams Engineering Journal, 1, 1-15, 2015.
Alcan Y, Öztürk A, Dirik H, Demir M. “Minimum kayıplar için güç şebekelerinde STATCOM yerinin ve değerinin yerçekimsel arama algoritması kullanılarak belirlenmesi”. 3rd International Symposium on Innovative Technologies in Engineering and Science, Valencia, Spain, 3-5 June 2015.
Sirjani R, Mohamed A, Shareef H. “Optimal placement and sizing of shunt FACTS devices in power systems using heuristic optimization techniques: A comprehensive survey”. Przegląd Elektrotechnıczny, 88(10), 335-341, 2012.
Baghaee RH, Vahidi B, Jazebi S, Gharehpetian BG, Kashefi A. “Power system security ımprovement by using differential evolution algorithm based FACTS allocation”. Power System Technology and IEEE Power India Conference, New Delhi, India, 12-15 October 2008.
Rashedi E, Nezamabadi-pour H, Saryazdi S. “GSA: A gravitational search algorithm”. Information Sciences, 179, 2232-2248, 2009.
Rashedi E, Nezamabadi-pour H, Saryazdi S. ”Binary gravitational search algorithm”. Natural Computing, 9, 727-745, 2010.
Karaboğa D. “An İdea Based on Honey Bee Swarm for Numerical Optimization”. Engineering Faculty Computer Engineering Department, Erciyes University, Kayseri, Turkey, Technical Report, TR06, 2005.
Karaboğa D, Baştürk B. “Artificial bee colony (ABC) optimization algorithm for solving constrained optimization problems”. Foundations of Fuzzy Logic and Soft Computing, 4529, 789-798, 2007.
Karaboğa D, Baştürk B. “On the performance of artificial bee colony (ABC) algorithm”. Applied Soft Computing, 8(1), 687–697, 2008.
Kennedy J, Eberhart R. “Particle swarm optimization”. IEEE International Conference on Neural Networks, Piscataway, USA, 27 November-1 December 1995.
Dorigo M., Maniezzo, V., Colorni, A., “The Ant System: An Autocatalytic Optimizing Process”. Tech. Rep. No. 91-016, Dipartimento di Elettronica, Politecnico di Milano, Italy, 1991.
Dorigo M, Stützle T. “The ant colony optimization metaheuristic: Algorithms, Applications and Advances”. International Series in Operations Research & Management Science, 57, 250-285, 2003.
Dirik H. STATCOM ve SSSC Denetleyicilerinin Güç Sistemi Gerilim Kararlılığı Üzerine Etkisinin İncelenmesi. Yüksek Lisans Tezi, Ondokuz Mayıs Üniversitesi, Samsun, Türkiye, 2006.
Li D, Gao L, Zhang J, Yang L. “Power system reactive power optimization based on adaptive particle swarm optimization algorithm”. 6th World Congress on Intelligent Control and Automation, Dalian, China, 21-23 June 2006.
Losi A, Rossi F, Russo M, Verde P. "New tool for reactive power planning". IEE Proceedings Generation, Transmission and Distribution, 140(4), 256-262, 1993.