基于最优时间步长模型的输电导线雾凇覆冰预测

doi:10.19595/j.cnki.1000-6753.tces.170941

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Abstract Transmission line icing is one of the major natural disasters affecting the safe and stable operation of power system. It has great significance to study the influence of atmospheric environmental parameters on the conductor icing process and accurately predict the ice quality and shape. In this paper, the time step of ice prediction that directly affects the time cost and accuracy of prediction was considered, and the optimal time step model of icing prediction was established. For the rime accretion problem, the real environmental parameters were re-determined in each step of prediction. The finite volume method (FVM) was used to solve the k-ε turbulence model based on the RNG method to obtain the airflow field. Lagrange method was utilized for calculating the trajectory of droplets based on the airflow field obtained, and then the quality and ice shape of rime were predicted through the post-processing. The influence of each factor (airflow velocity, air temperature, liquid water content, median droplet volume diameter and diameter of conductor) on the rime accretion on conductor was studied and summarized. In order to validate the rime accretion prediction model proposed, the natural icing experiment was carried out at the Natural Icing Test Station of Chongqing University at Xuefeng Mountain. The ice quality and ice shape of the experiment were consistent with the prediction results, which proved that the prediction model was accurate and effective.

Key words： Conductor rime accretion optimal time step ice shape ice quality time-dependent parameter prediction

Received: 02 July 2017 Published: 26 September 2018

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TM726

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	Jiang Xingliang
	Jiang Fangyi
	Wang Quanlin
	Luo Bing
	Han Xingbo

Cite this article:

Jiang Xingliang,Jiang Fangyi,Wang Quanlin等. Prediction of Rime Accretion on Transmission Line Based on Optimal Time Step Model[J]. Transactions of China Electrotechnical Society, 2018, 33(18): 4408-4418.

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https://dgjsxb.ces-transaction.com/EN/10.19595/j.cnki.1000-6753.tces.170941 OR https://dgjsxb.ces-transaction.com/EN/Y2018/V33/I18/4408

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