TY  - JOUR
T1  - Güç Sistemlerinde Geçici Hal Kararsızlığının Arıza Öncesi Fazör Ölçümleri Kullanarak Karar Ağacı Tabanlı Kestirimi
TT  - Early Prediction of Transient Instabilities Based on Pre-Fault Phasor Measurements using Decision Tree-based Methods
AU  - Genç, V.  M.  İstemihan
AU  - Saner, Can Berk
AU  - Kesici, Mert
AU  - Mahdı, Mohammed
AU  - Yaslan, Yusuf
PY  - 2019
DA  - April
DO  - 10.19113/sdufenbed.474888
JF  - Süleyman Demirel Üniversitesi Fen Bilimleri Enstitüsü Dergisi
JO  - J. Nat. Appl. Sci.
PB  - Süleyman Demirel Üniversitesi
WT  - DergiPark
SN  - 1308-6529
SP  - 6
EP  - 14
VL  - 23
IS  - 1
LA  - tr
AB  - Geçtiğimiz yıllarda, dünya çapında farklı güç sistemlerinde,çok sayıda geniş çaplı enerji kesintileri meydana gelmiş, bu kesintilermilyonlarca tüketicinin olumsuz etkilenmesine neden olmuş ve büyük miktardamali zararlara neden olmuştur. Elektrik güç sistemi tasarımı ve işletmesinde kritikbir role sahip sistem kararlılığı, günümüzdeki önemini korumaktadır. Bir güçsisteminin kararlılık durumunu gerçek zamanlı olarak izlemek, sistemçökmelerini önlemede birincil öneme sahip bir görev olarak kabul edilmektedir. Şebekeninkararlılık durumunun gerçek zamanlı olarak izlenmesi, geniş alan izleme, korumave kontrol sistemlerinin verimliliği açısından önemli bir fonksiyondur. Bufonksiyon ile düzeltici kontrol eylemlerinin zamanında gerçekleştirilebilmesisağlanabilir. Bu çalışmada, güç sisteminde meydana gelebilecek arızalaröncesinde fazör ölçüm birimlerinden alınan gerilimlere ait genlik ve açılarınyanı sıra, arızanın temizlenme süresi ve arızanın temizlenmesi için devredençıkarılan iletim hattına ait topoloji bilgileri de kullanılarak, geçici halkararsızlıklarının kestirimi, karar ağaçlarına dayalı iki farklı yöntem ilegerçekleştirilmiştir. Önerilen makine öğrenmesi modellerinin başarımları veetkinlikleri 29 jeneratörlü 127 baralı WSCC (Batı Eyaletleri KoordinasyonKurulu) test sisteminde uygulanarak gösterilmiştir.
KW  - Geçici hal kararlılığı
KW  - Sınıflandırma
KW  - Kestirim
KW  - Güç sistemleri
KW  - Makine öğrenmesi
N2  - Inrecent years, many blackouts occurred in power systems of different parts ofthe world, affecting millions of people and causing great economic losses. Powersystem stability, which has a critical role in the design and operation ofelectrical power systems, maintains its importance today. Monitoring thestability status of a power system in real time is regarded as a primary taskin preventing system blackouts. This allows of a sufficient amount of time totake appropriate corrective control actions. In this study, the pre-faultvoltage magnitudes and angles taken from the phasor measurement units (PMU),clearing time of the fault and topology information of the transmission linethat has been tripped for clearing the fault are used to predict the transientinstabilities by two different methods based on the decision trees. The successand the effectiveness of the proposed machine learning models are shown as theyare applied to the 127-bus Western Systems Coordinating Council (WSCC) testsystem.
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UR  - https://doi.org/10.19113/sdufenbed.474888
L1  - http://dergipark.org.tr/tr/download/article-file/695665
ER  -