Reactive power optimization with chaotic firefly algorithm and particle swarm optimization in a distribution subsystem network

In this paper the minimization of power losses in a real distribution network have been described by solving reactive power optimization problem. The optimization has been performed and tested on Konya Eregli Distribution Network in Turkey, a section of Turkish electric distribution network managed...

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Bibliographic Details
Published in:Iraqi journal for electrical and electronic engineering 2016-06, Vol.12 (1), p.71-78
Main Authors: Yapici, Hamzah, Cetinkaya, Nurettin
Format: Article
Language:English
Subjects:
Chaotic behavior in systems
Chaotic firefly algorithm
Computer algorithms
Computer networks
distribution subsystem network
Electric losses
Electric power systems
Evolution equations
Internet Protocol multimedia subsystem
Mathematical models
Mathematical optimization
Particle swarm optimization
power loss
reactive power optimization
Swarm intelligence
أساليب المحاكاة
أنظمة الطاقة الكهربائية
الاتصالات الرقمية
الاتصالات السلكية و اللاسلكية
التحليل الرياضي
الخوارزميات
النماذج الرياضية
بحوث العمليات
شبكات الحاسوب
شبكات المعلومات
معالجة البيانات
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Summary:In this paper the minimization of power losses in a real distribution network have been described by solving reactive power optimization problem. The optimization has been performed and tested on Konya Eregli Distribution Network in Turkey, a section of Turkish electric distribution network managed by MEDA (Meram Electricity Distribution Corporation). The network contains about 9 feeders, 1323 buses (including 0.4 kV, 15.8 kV and 31.5 kV buses) and 1311 transformers. This paper prefers a new Chaotic Firefly Algorithm (CFA) and Particle Swarm Optimization (PSO) for the power loss minimization in a real distribution network. The reactive power optimization problem is concluded with minimum active power losses by the optimal value of reactive power. The formulation contains detailed constraints including voltage limits and capacitor boundary. The simulation has been carried out with real data and results have been compared with Simulated Annealing (SA), standard Genetic Algorithm (SGA) and standard Firefly Algorithm (FA). The proposed method has been found the better results than the other algorithms.
ISSN:1814-5892
2078-6069
2078-6069
DOI:10.33762/eeej.2016.110676