Early Prediction of Transient Instabilities Based on Pre-Fault Phasor Measurements using Decision Tree-based Methods

Abstract

In recent years, many blackouts occurred in power systems of different parts of the world, affecting millions of people and causing great economic losses. Power system stability, which has a critical role in the design and operation of electrical power systems, maintains its importance today. Monitoring the stability status of a power system in real time is regarded as a primary task in preventing system blackouts. This allows of a sufficient amount of time to take appropriate corrective control actions. In this study, the pre-fault voltage magnitudes and angles taken from the phasor measurement units (PMU), clearing time of the fault and topology information of the transmission line that has been tripped for clearing the fault are used to predict the transient instabilities by two different methods based on the decision trees. The success and the effectiveness of the proposed machine learning models are shown as they are applied to the 127-bus Western Systems Coordinating Council (WSCC) test system.    

Keywords

• Transient instabilities,Classification,Prediction,Power systems,Machine learning

Güç Sistemlerinde Geçici Hal Kararsızlığının Arıza Öncesi Fazör Ölçümleri Kullanarak Karar Ağacı Tabanlı Kestirimi

Abstract

Geçtiğimiz yıllarda, dünya çapında farklı güç sistemlerinde, çok sayıda geniş çaplı enerji kesintileri meydana gelmiş, bu kesintiler milyonlarca tüketicinin olumsuz etkilenmesine neden olmuş ve büyük miktarda mali zararlara neden olmuştur. Elektrik güç sistemi tasarımı ve işletmesinde kritik bir role sahip sistem kararlılığı, günümüzdeki önemini korumaktadır. Bir güç sisteminin kararlılık durumunu gerçek zamanlı olarak izlemek, sistem çökmelerini önlemede birincil öneme sahip bir görev olarak kabul edilmektedir. Şebekenin kararlılık durumunun gerçek zamanlı olarak izlenmesi, geniş alan izleme, koruma ve kontrol sistemlerinin verimliliği açısından önemli bir fonksiyondur. Bu fonksiyon ile düzeltici kontrol eylemlerinin zamanında gerçekleştirilebilmesi sağlanabilir. Bu çalışmada, güç sisteminde meydana gelebilecek arızalar öncesinde fazör ölçüm birimlerinden alınan gerilimlere ait genlik ve açıların yanı sıra, arızanın temizlenme süresi ve arızanın temizlenmesi için devreden çıkarılan iletim hattına ait topoloji bilgileri de kullanılarak, geçici hal kararsızlıklarının kestirimi, karar ağaçlarına dayalı iki farklı yöntem ile gerçekleştirilmiştir. Önerilen makine öğrenmesi modellerinin başarımları ve etkinlikleri 29 jeneratörlü 127 baralı WSCC (Batı Eyaletleri Koordinasyon Kurulu) test sisteminde uygulanarak gösterilmiştir.    

Keywords

• Geçici hal kararlılığı,Sınıflandırma,Kestirim,Güç sistemleri,Makine öğrenmesi

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