Voltage Stabılıty Study In Power Systems Wıth Improved Teachıng-Learnıng Based Optımızatıon Algorıthm

Salih Ermiş; Ramazan Bayındır; Mehmet Yeşilbudak

Makale detayları

Voltage Stabılıty Study In Power Systems Wıth Improved Teachıng-Learnıng Based Optımızatıon Algorıthm

Yazarlar:

Salih Ermiş | Ramazan Bayındır | Mehmet Yeşilbudak

Yayıncı:

Gazi University

Ana Başlıklar:

Mühendislik

Dergi:

Gazi Üniversitesi Fen Bilimleri Dergisi Part C: Tasarım ve Teknoloji Vol.11 Issue 3, pp.695-705

Belge Tipi:

Makale

Yayın Tarihi:

9/2023

Belge Dili:

Türkçe

Document ID (Org No.):

2001299406

pISSN:

2147-9526

Anahtar kelimeler:

Özet:

 In this study, a new optimization method has been developed for voltage stability in power systems by rearranging the learning-teaching based optimization algorithm ÖÖTO developed in recent years. The designed learning-teaching based optimization D-ÖÖTO algorithm is presented as voltage stability optimization using a real power system with IEEE 14 bus and 17 bus on the Anatolian side of Istanbul, Turkey. In these power systems, five different situations base state, 20, 40 and 60 increase of the demanded load in the base state and line 1-5 interruption were created and analyzes were carried out. Then, shunt reactive power compensators RGK were connected to the load busbars and their effect in terms of voltage stability was examined. In order to prove the effectiveness of the presented D-ÖÖTO algorithm, the original ÖÖTO and the gravity search algorithm YAA used in the literature were compared with the results of particle swarm optimization PSO and Newton-Raphson power flow management. The D-ÖÖTO algorithm presented in all operating conditions has been proven to be superior to other methods. All analyzes were analyzed in Matlab R2017b program using Intel CoreTM i7-2620 2.7GHz and 8.00 64 bit Gb Ram PC.

 In this study, a new optimization method has been developed for voltage stability in power systems by rearranging the learning-teaching based optimization algorithm ÖÖTO developed in recent years. The designed learning-teaching based optimization D-ÖÖTO algorithm is presented as voltage stability optimization using a real power system with IEEE 14 bus and 17 bus on the Anatolian side of Istanbul, Turkey. In these power systems, five different situations base state, 20, 40 and 60 increase of the demanded load in the base state and line 1-5 interruption were created and analyzes were carried out. Then, shunt reactive power compensators RGK were connected to the load busbars and their effect in terms of voltage stability was examined. In order to prove the effectiveness of the presented D-ÖÖTO algorithm, the original ÖÖTO and the gravity search algorithm YAA used in the literature were compared with the results of particle swarm optimization PSO and Newton-Raphson power flow management. The D-ÖÖTO algorithm presented in all operating conditions has been proven to be superior to other methods. All analyzes were analyzed in Matlab R2017b program using Intel CoreTM i7-2620 2.7GHz and 8.00 64 bit Gb Ram PC.

 In this study, a new optimization method has been developed for voltage stability in power systems by rearranging the learning-teaching based optimization algorithm ÖÖTO developed in recent years. The designed learning-teaching based optimization D-ÖÖTO algorithm is presented as voltage stability optimization using a real power system with IEEE 14 bus and 17 bus on the Anatolian side of Istanbul, Turkey. In these power systems, five different situations base state, 20, 40 and 60 increase of the demanded load in the base state and line 1-5 interruption were created and analyzes were carried out. Then, shunt reactive power compensators RGK were connected to the load busbars and their effect in terms of voltage stability was examined. In order to prove the effectiveness of the presented D-ÖÖTO algorithm, the original ÖÖTO and the gravity search algorithm YAA used in the literature were compared with the results of particle swarm optimization PSO and Newton-Raphson power flow management. The D-ÖÖTO algorithm presented in all operating conditions has been proven to be superior to other methods. All analyzes were analyzed in Matlab R2017b program using Intel CoreTM i7-2620 2.7GHz and 8.00 64 bit Gb Ram PC.

 Bu çalışmada, son yıllarda geliştirilen öğrenme-öğretme tabanlı optimizasyon algoritması ÖÖTO yeniden düzenlenerek, güç sistemlerinde gerilim kararlılığı için yeni bir optimizasyon yöntemi geliştirilmiştir. Düzenlenen öğrenme-öğretme tabanlı optimizasyon D-ÖÖTO algoritması, IEEE 14 baralı ve Türkiye, İstanbul Anadolu yakasında 17 baralı gerçek bir güç sistemi kullanılarak gerilim kararlılığı optimizasyonu olarak sunulmuştur. Bu güç sistemlerinde, beş farklı durum temel durum, temel durumda ki talep edilen yükün 20, 40 ve 60 artışı ve 1-5 nolu hat kesintisi oluşturulmuş ve analizler gerçekleştirilmiştir. Daha sonra yük baralarına şönt reaktif güç kompansatörleri RGK bağlanarak gerilim kararlılığı açısından etkisi incelenmiştir. Sunulan D-ÖÖTO algoritmasının etkinliğini kanıtlamak için orijinal ÖÖTO ve literatürde kullanılan Yerçekimi arama algoritması YAA, parçacık sürü optimizasyonu PSO ve Newton-Raphson güç akış yönetimi sonuçlarıyla karşılaştırılmıştır. Tüm çalışma koşullarında sunulan D-ÖÖTO algoritması diğer yöntemlere göre üstünlüğü kanıtlanmıştır. Tüm analizler, Intel CoreTM i7-2620 2.7GHz ve 8.00 64 bit Gb Ram PC kullanılarak, Matlab R2017b programında çözümlenmiştir.

Makale detayları

Voltage Stabılıty Study In Power Systems Wıth Improved Teachıng-Learnıng Based Optımızatıon Algorıthm

Yazarlar:

Salih Ermiş | Ramazan Bayındır | Mehmet Yeşilbudak

Yayıncı:

Gazi University

Ana Başlıklar:

Mühendislik

Dergi:

Gazi Üniversitesi Fen Bilimleri Dergisi Part C: Tasarım ve Teknoloji Vol.11 Issue 3, pp.695-705

Belge Tipi:

Makale

Yayın Tarihi:

9/2023

Belge Dili:

Türkçe

Document ID (Org No.):

2001299406

pISSN:

2147-9526

Power systems

Voltage stability

Power flow

OptimizationGüç sistemleri

Gerilim karalılığı

Güç akışı

Optimizasyon

Sayı

Makale detayları

Yazarlar: Salih Ermiş | Ramazan Bayındır | Mehmet Yeşilbudak

Yayıncı: Gazi University

Ana Başlıklar: Mühendislik

Dergi: Gazi Üniversitesi Fen Bilimleri Dergisi Part C: Tasarım ve Teknoloji Vol.11 Issue 3, pp.695-705

Belge Tipi: Makale

Yayın Tarihi: 9/2023

Belge Dili: Türkçe

Document ID (Org No.): 2001299406

pISSN: 2147-9526

Sayı

Yazarlar:

Salih Ermiş | Ramazan Bayındır | Mehmet Yeşilbudak

Yayıncı:

Gazi University

Ana Başlıklar:

Mühendislik

Dergi:

Gazi Üniversitesi Fen Bilimleri Dergisi Part C: Tasarım ve Teknoloji Vol.11 Issue 3, pp.695-705

Belge Tipi:

Makale

Yayın Tarihi:

9/2023

Belge Dili:

Türkçe

Document ID (Org No.):

2001299406

pISSN:

2147-9526