Abstract
Abstract With the development of the power system, accurate assessment and fault diagnosis of the health status of transmission lines have become increasingly important. However, the standard data fusion algorithms do not fully exploit the correlation and weight disparity among multiple sensors. This study proposes a technique for analyzing transmission lines using a convolutional neural network-based approach to address this issue. By fusing data collected from multiple sensors in different directions, it can more accurately capture key information about transmission lines and conduct comprehensive analysis to evaluate their health status.
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