The design of monitoring and predictive alarm systems is necessary for successful overhead power transmission line icing. Given\nthe characteristics of complexity, nonlinearity, and fitfulness in the line icing process, a model based on a multivariable time series\nis presented here to predict the icing load of a transmission line. In this model, the time effects of micrometeorology parameters\nfor the icing process have been analyzed. The phase-space reconstruction theory and machine learning method were then applied\nto establish the prediction model, which fully utilized the history of multivariable time series data in local monitoring systems to\nrepresent the mapping relationship between icing load and micrometeorology factors. Relevant to the characteristic of fitfulness\nin line icing, the simulations were carried out during the same icing process or different process to test the model�s prediction\nprecision and robustness. According to the simulation results for the Tao-Luo-Xiong Transmission Line, this model demonstrates\na good accuracy of prediction in different process, if the prediction length is less than two hours, and would be helpful for power\ngrid departments when deciding to take action in advance to address potential icing disasters.
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