Comparison of different heuristic algorithms for location and value determination of STATCOM providing minimum losses in power systems
Yazarlar (4)
Yalcin Alcan
Ali Ozturk
Dr. Öğr. Üyesi Hasan DİRİK Sinop Üniversitesi, Türkiye
Öğr. Gör. Memnun DEMİR Sinop Üniversitesi, Türkiye
| Makale Türü |
Özgün Makale
(ESCI dergilerinde yayınlanan tam makale)
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| Dergi Adı | PAMUKKALE UNIVERSITY JOURNAL OF ENGINEERING SCIENCES-PAMUKKALE UNIVERSITESI MUHENDISLIK BILIMLERI DERGISI | ||
| Dergi ISSN | 1300-7009 | ||
| Makale Dili | İngilizce | Basım Tarihi | 01-2017 |
| Kabul Tarihi | – | Yayınlanma Tarihi | 01-01-2017 |
| Cilt / Sayı / Sayfa | 23 / 5 / 550–558 | DOI | 10.5505/pajes.2017.60566 |
| Makale Linki | https://jag.journalagent.com/z4/vi.asp?pdir=pajes&plng=eng&un=PAJES-60566 | ||
| UAK Araştırma Alanları |
Gömülü Sistemler
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| Özet |
| One of the most important issues of studies that have been done on the power systems is loss reduction. Flexible AC 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. |
| Anahtar Kelimeler |
| Optimal location | STATCOM | Gravitationalsearch algorithm | Particle swarm optirnizatioo | Artificial bee colony | Ant colony algorithm |
BM Sürdürülebilir Kalkınma Amaçları
| Atıf Sayıları |