Güç Sistemlerinde Aşırı Akım Röle Ayarlarının Metasezgisel Optimizasyon Teknikleri ile İyileştirilmesi

Yıl 2026, Cilt: 28 Sayı: 82, 66 - 76, 27.01.2026

İbrahim Arslanoğlu , İsmail Altaş

https://doi.org/10.21205/deufmd.2026288209

Öz

Güç sistemlerinde artan yük talebi, dağıtık üretim kaynaklarının entegrasyonu ve ağ yapılarındaki karmaşıklık, koruma sistemlerinin daha hızlı, hassas ve esnek olmasını zorunlu kılmaktadır. Bu bağlamda, aşırı akım rölelerinin doğru şekilde koordine edilmesi, sistemin güvenli ve kararlı çalışabilmesi açısından kritik bir gerekliliktir. Ancak geleneksel yöntemler, özellikle geniş ölçekli şebekelerde optimal ayarların belirlenmesinde yetersiz kalmakta; bu durum, çözüm uzayını daha etkin tarayabilen metasezgisel optimizasyon algoritmalarını öne çıkarmaktadır. Bu çalışmada, IEEE 33 baralı bir radyal dağıtım sistemi üzerinde Genetik Algoritma (GA), Kara Delik Algoritması (BHA), Koku Ajanı Optimizasyonu (SAO), Lig Şampiyonu Algoritması (LCA) ve Geçiş Arama Optimizasyonu (TSO) algoritmaları kullanılarak aşırı akım röle koordinasyonu optimize edilmiştir. Her algoritma eşit başlangıç koşullarında değerlendirilmiş; röle zaman çarpanı ve eşik akım parametreleri optimize edilerek toplam işletme süresinin en aza indirilmesi hedeflenmiştir. Çalışma sonuçlarına göre, en düşük toplam işletme süresi olan 4.53 s TSO algoritması ile elde edilmiş ve bu algoritma en yüksek optimizasyon başarısını göstermiştir. Diğer algoritmalarla karşılaştırıldığında TSO; LCA’ya göre %41.95, SAO’ya göre %73.96, BHA’ya göre %84.99 ve GA’ya göre %86.32 oranında daha iyi performans sergilemiştir. Özellikle TSO ve SAO’nun bu probleme ilk kez uygulanmış olması ve başarılı sonuçlar elde etmesi çalışmanın özgün yönlerindendir. LCA ise, çözüm kalitesi ile işlem süresi arasındaki dengeli yapısıyla pratik uygulamalarda güçlü bir alternatif olarak değerlendirilmektedir.

Anahtar Kelimeler

Aşırı akım röle koordinasyonu , Genetik Algoritma (GA) , Kara Delik Algoritması (BHA) , Koku Ajanı Optimizasyonu (SAO) , Lig Şampiyonu Algoritması (LCA) , Geçiş Arama Optimizasyonu (TSO)

Improvement of Overcurrent Relay Settings in Power Systems Using Metaheuristic Optimization Techniques

Öz

The increasing load demand, the integration of distributed generation sources, and the growing complexity of network structures have necessitated protection systems in power grids to become faster, more accurate, and more flexible. In this context, the proper coordination of overcurrent relays is a critical requirement for ensuring the safe and stable operation of the system. However, conventional methods are often inadequate in determining optimal settings, especially in large-scale networks. This limitation highlights the importance of metaheuristic optimization algorithms that can explore the solution space more effectively. In this study, overcurrent relay coordination was optimized on an IEEE 33-bus radial distribution system using five metaheuristic algorithms: Genetic Algorithm (GA), Black Hole Algorithm (BHA), Smell Agent Optimization (SAO), League Championship Algorithm (LCA), and Transit Search Optimization (TSO). Each algorithm was evaluated under equal initial conditions, with the objective of minimizing the total operating time by optimizing the time dial setting (TDS) and pickup current (PS) parameters. According to the results, the lowest total operating time of 4.53 s was achieved using the TSO algorithm, which demonstrated the highest optimization performance. When compared with the other algorithms, TSO outperformed LCA by 41.95%, SAO by 73.96%, BHA by 84.99%, and GA by 86.32%. A notable contribution of this study is the first-time application of TSO and SAO to this problem, both of which yielded successful results. Furthermore, LCA is considered a strong alternative in practical applications due to its balanced performance between solution quality and computational efficiency.

Anahtar Kelimeler

Overcurrent Relay Coordination , Genetic Algorithm (GA) , Black Hole Algorithm (BHA) , Smell Agent Optimization (SAO) , League Championship Algorithm (LCA) , Transit Search Optimization (TSO)

Kaynakça

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