A novel wavelet transform aided neural network based transmission line fault analysis method

A survey on intelligent system application to fault diagnosis in electric power system transmission lines

Statistical decision-tree based fault classification scheme for protection of power transmission lines

The ability to locate the faults as well as to identify the type of fault in overhead transmission lines is of prime importance for the economic operation of modern power systems. An expert system based on an artificial neural network for fault classification and distance estimation is proposed in this article. The power system network has been simulated using EMTP/ATP software, and signal analysis has been performed in MATLAB environment (The MathWorks, Natick, Massachusetts, USA). Various types of faults have been simulated at different locations along the transmission line. The faulty voltage signals have been analyzed through wavelet transform using the Db4 mother wavelet. The entropies of the wavelet decompositions have been fed to the neural networks for classification and fault distance evaluation. The suggested technique is proven to be successful for classification and location of the faults.

Transmission Line Fault Classification and Location Using Wavelet Entropy and Neural Network

This article demonstrates a technique for diagnosis of fault type and faulty phase on overhead transmission lines. A method for computation of fault location is also incorporated in this work. The proposed method is based on the multi-resolution S-transform, which is used for generating complex S-matrices of the current signals measured at the sending and receiving ends of the line. The peak magnitude of the absolute value of every S-matrix is noted. The phase angle corresponding to every peak component is obtained from the argument of the relevant S-matrix. These features are used as input vectors of a probabilistic neural network for fault detection and classification. Detection of faulty phase(s) is followed by estimation of fault location. The voltage signal of the affected phase is processed to generate the S-matrix. The frequency components of the S-matrices for different fault locations are used as input vectors for training a back-propagation neural network. The results are obtained with s...

Chang-Mu Lee

Papers

Novel technique for fault location estimation on parallel transmission lines using wavelet