Fault Location in the Transmission Network Using a Discrete Wavelet Transform

Abstract

In this paper, a discrete wavelet transform (DWT) has been utilized for processing the current signal in order to faultlocation evaluation in network transmission using pre-fault and post-fault current data of both the terminals of a transmission line. In fact, the basis of the work is based on the information recorded before the fault at the end of the line and after the fault at the beginning of the line received by the relay. Obviously, high-frequency components are created at the time of the fault, which is a way of extracting these components using a wavelet transform. In this design, characteristics extorted from synchronous recording of three-phase current signals at the two terminals using DWT. In the following, can accurately estimate the exact location of the fault in the transmission network by extraction and subtracting of the minimum and maximum components of the DWT approximate and detail components of the signal before and after the fault (pre-fault and post-fault).The simulation results reveal that the minimum and maximum extracted components are highly dependent on the fault resistance. Hence, due to increase the fault resistance, the level of signal decomposition has to be increased so that the algorithm is not compromised. Eventually, the proposed method is tested on the transmission network of 735 kV at different distances of the transmission line, which indicates that the proposed algorithm can accurately estimate the fault distance, depending on the type of fault (including low-impedance and high-impedance fault) by changing the signal decomposition level.

Efficient Human Face Recognition in Real-Life Applications using the Discrete Wavelet Transformation (HFRDWT)

“…For the purpose of filtering and compressing the detected faces images, Discrete Wavelet Transform (DWT) was used in the proposed HFRDWT system as an analog filter that captures both frequency and location information [5] showing great efficiency in such tasks. Moreover, Applying the DWT as a descriptor reduced the resolution of the detected faces images to a quarter by dividing it into four coefficients, Approximation, Diagonal, Horizontal, and Vertical [12] which can be expressed by the following equation:…”

Efficient human face recognition in real-life applications using the discrete wavelet transformation (HFRDWT)

“…For the purpose of filtering and compressing the detected faces images, Discrete Wavelet Transform (DWT) was used in the proposed HFRDWT system as an analog filter that captures both frequency and location information [5] showing great efficiency in such tasks. Moreover, Applying the DWT as a descriptor reduced the resolution of the detected faces images to a quarter by dividing it into four coefficients, Approximation, Diagonal, Horizontal, and Vertical [11] which can be expressed by the following equation:…”

Voltage Control in Distribution Networks in Presence of Distributed Generators Based on Local and Coordinated Control Structures

“…Reference [15] presents an algorithm that minimizes reactive power that is required from transmission network. The other methods can be used both for centralized plans and also decentralized plans like reference [15][16][17][18][19][20].…”