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...

Detection, Classification, and Estimation of Fault Location on an Overhead Transmission Line Using S-transform and Neural Network

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

According to the government’s energy policy, distributed generation is likely to be operated in connection with the railway system in the future. Circulating current may occur under unbalanced conditions when using the existing non-standard interconnected transformer for the distributed generation, and this circulating current problem must be solved from the perspective of a distributed power provider in order to accommodate the needs of the power utility. In addition, as the load connected to the power system through the rectifier increases, the harmonic current caused in the neutral wire is also increasing, and there is a need to solve various relevant problems. Therefore, it is necessary to develop an auxiliary transformer (grounding transformer), which has multiple auxiliary roles (reduction of excessive circulating current, protection of non-grounding systems by providing effective grounding). The developed zigzag transformer has been verified its performance through a characteristic test and an accredited agency certification test after manufacture. In this paper, the design of the zigzag transformer and the modeling of it by using Power System Computer Aided Design (PSCAD) are described. Simple compensation verification of unbalance situation has been also shown. 

Moonseob Han

Papers

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

Development of Zigzag Transformer for Unbalance Compensation in Railway Power System