Journals / Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi / 2017 / Cilt: 23 - Sayı: 5

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

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

Pages
550–558
Publication Date
2017-10-20
DOI
—
🏛️Bibliographic metadata and full text access for this article are provided via DergiPark.
DergiPark Record ↗
✨

AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

Also, results of all methods are compared and discussed in terms of finding proper value and convergence speed.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:One of the most important issues of studies that have been done on the power systems is loss reduction.
📊
Key Findings & Data:C. transmission systems (FACTS) give significant opportunities to realize this aim.
💡
Conclusion & Impact:Also, results of all methods are compared and discussed in terms of finding proper value and convergence speed.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Optimal konum · STATCOM · Yerçekimsel arama algoritması

Abstract

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.

Özet

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.

🔖

Cite This Article

ALCAN, Y., ÖZTÜRK, A., DİRİK, H., DEMİR, M. (2017). Comparison of different heuristic algorithms for location and value determination of STATCOM providing minimum losses in power systems. Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi, Cilt: 23 - Sayı: 5, 550–558. https://ajindex.com/articles/42492

📄 Full Text PDF Viewer

📄

PDF görüntüleyici hazırlanıyor...

References (12)

  1. Oliveira EJ, Marang JW, Pereira JLR. “Flexible AC transmission system devices: allocation and transmission”. Electrical Power and Energy Systems, 21(2), 111-118, 1999.
  2. 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.
  3. 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.
  4. 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.
  5. 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.
  6. Bhattacharyya B, Gupta VK, Das S. “Evolutionary programming for reactive power planning using FACTS devices”. Wseas Transactıons on Power Systems, 9, 1-6, 2014.
  7. 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.
  8. 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.
  9. 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.
  10. 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.
  11. 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.
  12. 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,