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Predicting the probability of ice storm damages to electricity transmission facilities based on ELM and Copula function |
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| Authors: | Hongming YangAuthor VitaeJunhua ZhaoAuthor Vitae Dianhui WangAuthor VitaeZhaoyang DongAuthor Vitae |
| Affiliation: | a School of Electrical and Information Engineering, Changsha University of Science and Technology, Changsha, PR China b School of Electrical Engineering, Zhejiang University, Hangzhou, Zhejiang, PR China c Department of Computer Science and Computer Engineering, La Trobe University, Melbourne, Australia d Centre for Intelligent Electricity Networks, The University of Newcastle, Newcastle, Australia |
| Abstract: | In this paper, a novel method based on extreme learning machine (ELM) and Copula function is proposed to predict the damages to electricity transmission facilities during ice storms. The ELM is firstly trained based on the historical data of wind speed, freezing precipitation, temperature, as well as the distribution parameters of wind and ice loads. The ELM can then be employed to predict the distributions of the real-time wind and ice loads on electricity transmission facilities. Furthermore, the correlation between wind load and ice load is modeled with Copula functions. On the basis of ELM and Copula function, the joint probability distribution of wind and ice loads can be finally formulated and applied to predict the potential damages to electricity transmission facilities such as transmission lines and towers. The proposed method is tested with a real dataset to demonstrate its effectiveness. |
| Keywords: | Copula function Extreme learning machine Joint probability distribution Electricity transmission lines and towers Ice storms |
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