title: Multivariable time series prediction for the icing process on overhead power transmission line.
abstract: The design of monitoring and predictive alarm systems is necessary for successful overhead power transmission line icing. Given the characteristics of complexity, nonlinearity, and fitfulness in the line icing process, a model based on a multivariable time series is presented here to predict the icing load of a transmission line. In this model, the time effects of micrometeorology parameters for the icing process have been analyzed. The phase-space reconstruction theory and machine learning method were then applied to establish the prediction model, which fully utilized the history of multivariable time series data in local monitoring systems to represent the mapping relationship between icing load and micrometeorology factors. Relevant to the characteristic of fitfulness in line icing, the simulations were carried out during the same icing process or different process to test the model's prediction precision and robustness. According to the simulation results for the Tao-Luo-Xiong Transmission Line, this model demonstrates a good accuracy of prediction in different process, if the prediction length is less than two hours, and would be helpful for power grid departments when deciding to take action in advance to address potential icing disasters.
authors.affiliation: Department of Electronic Engineering, Yunnan University, Kunming 650091, China ; Department of Automation, Tsinghua University, Beijing 100084, China ; Yunnan Electric Power Research Institute, China Southern Power Grid Corp., Kunming 650217, China.
authors.forename: Peng
authors.initials: P
authors.lastname: Li
authors.affiliation: Department of Electronic Engineering, Yunnan University, Kunming 650091, China.
authors.forename: Na
authors.initials: N
authors.lastname: Zhao
authors.affiliation: Department of Automation, Tsinghua University, Beijing 100084, China.
authors.forename: Donghua
authors.initials: D
authors.lastname: Zhou
authors.affiliation: Yunnan Electric Power Research Institute, China Southern Power Grid Corp., Kunming 650217, China.
authors.forename: Min
authors.initials: M
authors.lastname: Cao
authors.affiliation: Department of Electronic Engineering, Yunnan University, Kunming 650091, China.
authors.forename: Jingjie
authors.initials: J
authors.lastname: Li
authors.affiliation: Department of Electronic Engineering, Yunnan University, Kunming 650091, China.
authors.forename: Xinling
authors.initials: X
authors.lastname: Shi
mesh_terms: D011211:Electric Power Supplies; D004784:Environmental Monitoring; D008962:Models, Theoretical