GÜÇ SİSTEMLERİNDE ARIZA TEŞHİSİ İÇİN PETRI NET TABANLI BİR YÖNTEM

Güç sistemlerindeki  merkezi kontrolcüler, arızanın hızlı bulunmasını ve tanımlanmasına ihtiyaç gösterirler. Hata analizi için bilgiler fiziksel olarak daima koruyucu cihazlardan alınır. Güç sistemi büyüdükçe bu işlemler giderek zorlaşır. Bu çalışmada, koruyucu cihazlardan gelen alarm işlemlerini teşhiste kullanmak için, Petri netler modelleme aracı olarak kullanılmıştır. Çalışmada önce, güç sistemlerinde arıza analizi için genel literatür çalışmaları verilmiştir. .Daha sonar, MATLAB  a dayalı arıza teşhisi modelleri geliştirilmiştir. 5 bara, 48 aşırı akım röleli, 48 devre kesicili, 23 iletim hatlı ve 20 yük barındıran bir güç ssitemi devresi, test devresi olarak kullanılmıştır. Teklif edilen metodun geçerliliği ve incelemesi simülasyon örnekleriyle sunulmuştur. Önerilen sistem, 9 arıza durumu için test edilmiştir. Test sonuçları sıralı olarak verilmiştir.  

A PETRI NETS BASED TECHNIQUE FOR FAULT DIAGNOSIS STUDIES IN POWER SYSTEMS

 A central controller requires quickly locating and identifying the failure in power systems. The failure analysis based on information taken from the physically distributed protective devices. The process becomes difficult owing to the complexity of the power system increasing. In this study, Petri nets are used as modeling tools to precisely diagnose faults when some uncertain and incomplete alarm information of protective devices is detected.  Firstly, a general literature survey of fault diagnosis in power systems is given. Then, models of fault diagnosis based on MATLAB SIMULINK have been developed. A power system network which has 5 buses, 48 over current relays, 48 circuit breakers, 23 transmission lines and 20 loads is used as test network. Finally, the validity and feasibility of the proposed method is demonstrated by simulation examples. The proposed system is tested with nine fault cases. Results of these tests are given in order. It is shown from nine cases that the faulted power system elements can be diagnosed accurately by using the Petri nets based fault diagnosis models.  

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