Wavelet transform has received greater attention in fault analysis due to its ability in analyzing the travelling waves than conventional methods of analysis In this paper, the method using wavelet transforms for detecting a HVDC transmission line faults is proposed after simulating the HVDC system for various faults The simulation results show that the application of wavelet technique leads to a more reliable solution for recognition of faults and provides a good basis for the new protection scheme for the HVDC lines

Wavelet Transform Approach for Detection and Location of Faults in HVDC System

Directional relaying using support vector machine for double circuit transmission lines including cross-country and inter-circuit faults

A universal wavefront positioning correction method on traveling-wave-based fault-location algorithms is proposed. According to the location accuracy analysis for the faults of which the fault positions change successively, it is noted that the real fault distance generally cannot be divided exactly by the distance corresponding to an integer sampling interval. It will lead to unavoidable truncated error when the discrete sampling points of traveling-wave-based fault-location algorithms are applied. Furthermore, it is disclosed that the fault-location error can be limited within a single sampling interval by the simple wavefront identification method in the event of low sampling rate. In view of the aforementioned two scenarios, an appropriate correction strategy in the event of the appropriate sampling rate is put forward. The fault-location accuracy can be improved greatly with the aid of the proposed correction strategy. The effectiveness of the proposed correction strategy is verified with various typical EMTDC-based simulation test results.

Universal Wavefront Positioning Correction Method on Traveling-Wave-Based Fault-Location Algorithms

This paper proposes a technique using discrete wavelet transform (DWT) and back-propagation neural network (BPNN) for locating of fault location on single circuit transmission lines. The ATP/EMTP was used to simulated fault signals. The mother wavelet daubechies4 (db4) is employed to decompose, high frequency component from these signals. The first peak time in first scale of each bus that can detect fault are used as input pattern for the training pattern. It is shown that the proposed technique gives satisfactory.

Discrete wavelet transform and back-propagation neural networks algorithm for fault location on single-circuit transmission line

This article proposes an application of the discrete wavelet transform DWT and back-propagation neural networks BPNN for fault diagnosis on single-circuit transmission line. ATP/EMTP is used to simulate fault signals. The mother wavelet daubechies4 db4 is used to decompose the high-frequency component of these signals. In addition, characteristics of the fault current at various fault inception angles, fault locations and faulty phases are detailed. The DWT is employed in extracting the high frequency component contained in the fault currents, and the coefficients of the first scale from the DWT that can detect fault are investigated, and the decision algorithm is constructed based on the BPNN. The results show that the proposed technique provides satisfactory results.

An application of a discrete wavelet transform and a back-propagation neural network algorithm for fault diagnosis on single-circuit transmission line

This paper presents a new method to diagnose faults in a transmission system This is based on detecting high frequency components contained in a fault signal spectrum The discrete wavelet transform (DWT) is used in the analysis in order to classify fault types and to locate fault positions Simulations are performed using ATP/EMTP It is found that the proposed method gives satisfactory results, and will be very useful in the development of a power system protection scheme

S. Jiriwibhakorn

Papers

Fault diagnosis in transmission lines using wavelet transform analysis