Abstract
The tree barrier distance of high-voltage transmission line will directly affect the safety of high-voltage power supply and distribution. However, the traditional monitoring method has a small amount of data processing, which leads to the large distance between the automatic monitoring result and the real value. Therefore, a new automatic monitoring method for tree barrier distance of high-voltage transmission line is proposed. In this study, monocular vision technology is used to extract the features from the SCSR model, which are substituted into the feature dictionary of pcanet. The high-resolution image is reconstructed based on sparse regularization model. The new filtering algorithm is used to extract the transmission line points. Through the construction of three-dimensional model, the distance between tree obstacles of high-voltage transmission lines is automatically monitored. Experimental results: the error of the monitoring results of the proposed method is controlled within $\pm 0.3\mathrm{m}$ , while that of the traditional method is within $\pm 1.5\mathrm{m}$ . It can be seen that the automatic monitoring method in this study is more suitable for high voltage transmission line tree barrier distance monitoring.
Published Version
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