A comparison between ANN based methods of critical clearing time estimation

Araştırma sonucu: Kitap/Rapor/Konferans Bildirisinde BölümKonferans katkısıbilirkişi

3 Atıf (Scopus)

Özet

This paper presents a methodology based on Artificial Neural Network (ANN) structures for the dynamic security assessment (DSA) of power systems. Proposed methodology involves, ANN approach for fast and accurate estimation of critical clearing time (CCT) values of credible faults occurring in the system, considering changes in the loading conditions and system topology. CCT is an important indicator that measures the transient stability of the system against critical contingencies. Offline trained ANNs can monitor online DSA without suffering from excessive computational burden of time domain simulations (TDS). Decision Trees are used as a feature selection tool to reduce the training time and ANN complexity, increasing the CCT estimation performance of the ANN applications studied in this work, Multi-Layer Perceptron, Radial Basis Neural Network, Generalized Regression Neural Network and Adaptive Neuro-Fuzzy Inference Systems. The proposed approach is applied to 16 generator-68 bus test system operating at various loading conditions and system topologies.

Orijinal dilİngilizce
Ana bilgisayar yayını başlığıELECO 2013 - 8th International Conference on Electrical and Electronics Engineering
YayınlayanIEEE Computer Society
Sayfalar132-136
Sayfa sayısı5
ISBN (Basılı)9786050105049
DOI'lar
Yayın durumuYayınlandı - 2013
Etkinlik8th International Conference on Electrical and Electronics Engineering, ELECO 2013 - Bursa, Turkey
Süre: 28 Kas 201330 Kas 2013

Yayın serisi

AdıELECO 2013 - 8th International Conference on Electrical and Electronics Engineering

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???event.eventtypes.event.conference???8th International Conference on Electrical and Electronics Engineering, ELECO 2013
Ülke/BölgeTurkey
ŞehirBursa
Periyot28/11/1330/11/13

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