Abstract
Given that the single-terminal traveling wave location method has significant errors, a novel fault location method based on the spatial domain image fusion and convolutional neural network (CNN) is proposed. Firstly, the three-phase traveling wave can be decoupled by the phase-mode transformation matrix for obtaining the line-mode component of the traveling wave. Secondly, the 1D line-mode traveling wave can be converted into a 2D image by the Gramian angular field (GAF). The 1D line-mode component can be mapped into the color, point, line, and other characteristic parameters of the 2D image. In order to expand the invisible information of the line-mode traveling wave, the images obtained by the Gramian angular summation field (GASF) and Gramian angular difference field (GADF) are weighted and fused. Finally, the CNN can be used to autonomously mine the characteristic parameters of the weight-fusion image and realize fault location. The simulation results show that the proposed method does not need to be considered in the traveling wave head and the traveling wave speed. The localization method is not affected by fault time, fault distance, or transition resistance factors. It possesses high reliability with an absolute range error of no more than 200 m.
Highlights
The medium-voltage distribution network usually adopts a small current grounding system (SCGS) in China, and the single-phase grounding fault has become one of the crucial factors influencing the reliability of the power supply [1, 2]
In order to cope with the problem that the traditional singleended traveling wave ranging method is affected by wave speed and pseudo wave head, the paper proposes a single-ended traveling wave fault location method for distribution networks that combines traveling wave theory, Gramian angular field (GAF), and convolutional neural network (CNN)
The fault distance can be obtained by regression prediction of fault feature maps using the convolution neural network
Summary
The medium-voltage distribution network usually adopts a small current grounding system (SCGS) in China, and the single-phase grounding fault has become one of the crucial factors influencing the reliability of the power supply [1, 2]. Fast and accurate fault location technology can reduce power outage range and power outage time [3] It has an essential significance for improving the safety, reliability, and economy of the distribution network. When the single-phase grounding fault occurs in the small current grounding system, the fault signal presents nonlinear, faint, complexity, and other features. These factors can give the challenge for fault location. Sapountzoglou et al [8] used the gradient boosting trees to establish a fault diagnosis for the low-voltage smart
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