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

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

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

9 Citations (Scopus)

Abstract

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.

Original languageEnglish
Title of host publicationELECO 2019 - 11th International Conference on Electrical and Electronics Engineering
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages156-160
Number of pages5
ISBN (Electronic)9786050112757
DOIs
Publication statusPublished - Nov 2019
Event11th International Conference on Electrical and Electronics Engineering, ELECO 2019 - Bursa, Turkey
Duration: 28 Nov 201930 Nov 2019

Publication series

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

Conference

Conference11th International Conference on Electrical and Electronics Engineering, ELECO 2019
Country/TerritoryTurkey
CityBursa
Period28/11/1930/11/19

Bibliographical note

Publisher Copyright:
© 2019 Chamber of Turkish Electrical Engineers.

Funding

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

FundersFunder number
TUBITAK
Consejo Nacional de Investigaciones Científicas y Técnicas
Türkiye Bilimsel ve Teknolojik Araştırma Kurumu118E184

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