Transmission Line State Identification Based on Electromagnetic Sensing Data

High-voltage overhead transmission lines are susceptible to abnormal conditions such as icing, wind deviation, and galloping due to extreme weather conditions, which has a serious impact on the safe and stable operation of the power system. Therefore, it is necessary to establish an online monitoring system to identify and warn the abnormal conditions that may occur on overhead transmission lines in real time. In this paper, a scheme of transmission line state identification based on electromagnetic sensing data and machine learning method is proposed. Firstly, the catenary model of transmission line is modeled and analyzed, and the mathematical expression of the model in abnormal state is defined. Then, based on the law of spatial magnetic field and electric field distribution of overhead transmission lines, the installation strategy of sensors is discussed, and the feature quantities that can be used for machine learning are constructed. Finally, the electromagnetic field data is calculated and generated based on the specific sensor deployment scheme, and the task instance of transmission line state identification is completed based on the idea of machine learning.