Optimal Power Flow Based on a Metaheuristics Optimization Approach for the Iraqi Super High Voltages Network
Yükleniyor...
Dosyalar
Tarih
2025
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Institute of Electrical and Electronics Engineers Inc.
Erişim Hakkı
info:eu-repo/semantics/openAccess
Özet
Optimal power flow is a tool It enables operators to run the system as efficiently as possible within specific limitations. Therefore, many tools have been developed to assist operators to make decisions for different objectives. The optimal power flow (OPF) problem is the most difficult and complex problem in power system analysis and design due to the nonlinearities and imposed constrains. OPF’s goal is to reduce generation costs and transmission losses. when the demand and generated power are balanced. In this work, the proposed approach uses genetic algorithm (GA) and camping the Hybrid Particle Swarm Optimization and Genetic algorithm (HPSO+GA) as an intelligent methods to perform optimal power flow. Cost functions that are defined and minimized in this work are the overall active losses and amount of required fuel. The viability of the suggested method is confirmed by comparing the results of the presented methodology With previous research results. Using the MATLAB platform, the best load flow technique was evaluated using data from the Iraqi 400 KV transmission network, which consists of 58 buses. Results document the viability of the proposed method in terms of less active losses and reduced fuel costs. Moreover, the proposed GA and HPSO+GA methods requires no iterations hence errors of solution divergence and initial conditions are omitted.
Açıklama
Anahtar Kelimeler
Fuel cost, genetic algorithm (GA), hybrid particle swarm optimization (HPSO), optimal power flow (OPF), system losses
Kaynak
IEEE Access
WoS Q Değeri
Q2
Scopus Q Değeri
Q1
Cilt
13
Sayı
Künye
Almosawi, A. A., Cevik, B. M., & Ersoy, C. A. (2025). Optimal Power Flow Based on a Metaheuristics Optimization Approach for the Iraqi Super High Voltages Network. IEEE Access, 13, 106724-106735. 10.1109/ACCESS.2025.3578579
