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

Year 2019, Volume: 23 Issue: 1, 6 - 14, 01.04.2019

Can Berk Saner Mert Kesici Mohammed Mahdı Yusuf Yaslan , V. M. İstemihan Genç

https://doi.org/10.19113/sdufenbed.474888

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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