Volume 9, Number 3 (September 2013)                   IJEEE 2013, 9(3): 177-188 | Back to browse issues page

XML Print

Download citation:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:

Mollanezhad Heydar-Abadi M, Akbari Foroud A. Accurate Fault Classification of Transmission Line Using Wavelet Transform and Probabilistic Neural Network. IJEEE. 2013; 9 (3) :177-188
URL: http://ijeee.iust.ac.ir/article-1-563-en.html

Abstract:   (6127 Views)
Fault classification in distance protection of transmission lines, with considering the wide variation in the fault operating conditions, has been very challenging task. This paper presents a probabilistic neural network (PNN) and new feature selection technique for fault classification in transmission lines. Initially, wavelet transform is used for feature extraction from half cycle of post-fault three phase currents at one end of line. In the proposed method three classifiers corresponding with three phases are used which fed by normalized particular features as wavelet energy ratio (WER) and ground index (GI). The PNNs are trained to provide faulted phase selection in different ten fault types. Finally, logic outputs of classifiers and GI identify the fault type. The feasibility of the proposed algorithm is tested on transmission line using PSCAD/EMTDC software. Variation of operating conditions in train cases is limited, but it is wide for test cases. Also, quantity of the test data sets is larger than the train data sets. The results indicate that the proposed technique is high speed, accurate and robust for a wide variation in operating conditions and noisy environments.
Full-Text [PDF 521 kb]   (1832 Downloads)    
Type of Study: Research Paper | Subject: Fault Diagnosis
Received: 2013/03/22 | Accepted: 2013/07/22 | Published: 2013/07/22

Add your comments about this article : Your username or email:
Write the security code in the box

© 2015 All Rights Reserved | Iranian Journal of Electrical and Electronic Engineering

Designed & Developed by : Yektaweb