A CNN-Based Post-Contingency Transient Stability Prediction Using Transfer Learning

Sevda Jafarzadeh, Nazanin Moarref, Yusuf Yaslan, V. M. Istemihan Genc

Araştırma sonucu: ???type-name???Konferans katkısıbilirkişi

9 Atıf (Scopus)

Özet

One of the main problems in the utilization of machine learning-based classifiers for transient stability prediction is their long training times with the comprehensive large-sized datasets. Using a small-sized dataset to decrease the training time is not reasonable since the dataset should be representative of all types of severe faults. In this paper, a novel methodology based on transfer learning is proposed for real-time post-contingency transient stability prediction to overcome the difficulties about the long training times of these classifiers. In the proposed method, first, a small dataset which contains only the three-phase fault contingencies for various operating points is selected to train a convolutional neural network (CNN) classifier, and then, an additional dataset which involves two-phase-to-ground fault scenarios is used to update the trained CNN using the transfer learning approach instead of retraining the model from ground up. To demonstrate the efficiency of the proposed method, it is applied to the 127-bus test system.

Orijinal dilİngilizce
Ana bilgisayar yayını başlığıELECO 2019 - 11th International Conference on Electrical and Electronics Engineering
YayınlayanInstitute of Electrical and Electronics Engineers Inc.
Sayfalar156-160
Sayfa sayısı5
ISBN (Elektronik)9786050112757
DOI'lar
Yayın durumuYayınlandı - Kas 2019
Etkinlik11th International Conference on Electrical and Electronics Engineering, ELECO 2019 - Bursa, Turkey
Süre: 28 Kas 201930 Kas 2019

Yayın serisi

AdıELECO 2019 - 11th International Conference on Electrical and Electronics Engineering

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???event.eventtypes.event.conference???11th International Conference on Electrical and Electronics Engineering, ELECO 2019
Ülke/BölgeTurkey
ŞehirBursa
Periyot28/11/1930/11/19

Bibliyografik not

Publisher Copyright:
© 2019 Chamber of Turkish Electrical Engineers.

Finansman

This work was supported by The Scientific and Technical Research Council of Turkey (TUBITAK) project no. 118E184.

FinansörlerFinansör numarası
TUBITAK
Consejo Nacional de Investigaciones Científicas y Técnicas
Türkiye Bilimsel ve Teknolojik Araştırma Kurumu118E184

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