@inproceedings{scholars391,
            year = {2008},
             doi = {10.1109/PECON.2008.4762603},
            note = {cited By 4; Conference of 2008 IEEE 2nd International Power and Energy Conference, PECon 2008 ; Conference Date: 1 December 2008 Through 3 December 2008; Conference Code:75682},
           pages = {901--906},
         journal = {PECon 2008 - 2008 IEEE 2nd International Power and Energy Conference},
           title = {Taguchi's method for optimized neural network based autoreclosure in extra high voltage lines},
         address = {Johor Baharu},
            isbn = {9781424424054},
          author = {Desta, Z. F. and Rao, K. S. R.},
        abstract = {This paper presents a method to discriminate the temporary faults from the permanent ones in an extra high voltage transmission line so that improper reclosing of the line into a fault is avoided. The fault identification prior to reclosing is based on optimized artificial neural network associated with Levenberg Marquardt algorithm to train the ANN and Taguchi's Method to find optimal parameters of the algorithm and number of hidden neurons. The algorithms are developed using MATLABTM software. A range of faults are simulated using SimPowerSytemsTM and the spectra of the fault data are analyzed using Fast Fourier Transform which facilitates extraction of distinct features of each fault type. For both training and testing purposes, the neural network is fed with the normalized energies of the DC component, the fundamental and the first four harmonics of the faulted voltages. The developed algorithm is verified with dedicated testing data. The results show that it is possible to effectively distinguish the type of fault and practically avoid reclosing into faults. {\^A}{\copyright}2008 IEEE.},
             url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-63049108927&doi=10.1109\%2fPECON.2008.4762603&partnerID=40&md5=017ca117ee59727552b8bb7d74fc0280},
        keywords = {Algorithms; Backpropagation; DC generators; EHV power transmission; Electric fault location; Electric lines; Fast Fourier transforms; Feature extraction; Power transmission; Transmission line theory, Artificial neural networks; Autoreclosure; EHV transmission line; Levenberg marquardt algorithm; Taguchi's method; Transmission line faults, Neural networks}
}