Yin Shuiqing, Zhao Shanshan, Wang Zunya, et al. Characteristic analysis of ice accumulation on transmission lines and simulation based on ANN model over China. J Appl Meteor Sci, 2009, 20(6): 722-728.
Citation: Yin Shuiqing, Zhao Shanshan, Wang Zunya, et al. Characteristic analysis of ice accumulation on transmission lines and simulation based on ANN model over China. J Appl Meteor Sci, 2009, 20(6): 722-728.

Characteristic Analysis of Ice Accumulation on Transmission Lines and Simulation Based on ANN Model over China

  • Received Date: 2009-02-10
  • Rev Recd Date: 2016-01-13
  • Publish Date: 2009-12-31
  • An extreme frozen ice and snow disaster brought severe losses in early 2008 to China. A large number of overhead transmission lines are destroyed seriously during this event. It is critical to investig ate the characteristics of ice accumulation and develop appropriate models to estimate transmission line icing in China.Using the observation data of ice accumulation on transmission lines and meteorological data collected from over 600 meteorological stations of China, the characteristics of ice accumulation are analyzed and an ANN model for predicting ice accumulation depth grades is developed. The results mainly include four aspects. First, the area with ice accumulation extremes over 10 mm is mainly located in the southeastern part of Northeastern China, the northeastern part of Inner Mongolia, the middle part of North China, the southern part of Gansu Province and a east-west zone to the south of the Yangtze River. Second, there are 50 stations (1/6 of totalice accumulation observation stations) expe riencing extreme ice accumulation in 2008. Third, trends for yearly maximum of ice accumulation depth and ice accumulation days increase for Huangshan station in Anhui Province and Lushan station in Jiangx iProvince, while decrease for Emeishan station in Sichuan Province and Xifengzhen station in Gansu Province. Finally, an ANN model based on three layer BP network is developed to predict ice accumulation thickness grades. It is used to predict ice accumulation thickness grades in recent 10 years, and the accuracy rate is 81.3%comparing with obser-vation. For 7.5% of the samples, prediction result is one grade higher than observation and for 11.3% it is one to four grades lower. The ANN model underestimates extreme high values seriously, which suggests it needs improving.Besides, the data from weather observation stations might be different from the field surroundings. Nevertheless, in the areas with high risks of freezing hazard, the model can provide information for electricity suppliers to optimize the design of reliable equipments and to take preventive actions avoiding serious damage by ice-snow frozen weather.
  • Fig. 1  The spatial distribution of ice accumulation extremes for each station

    Fig. 2  The spatial distribution of decades when ice accumulations extremes appear

    Fig. 3  Relationship between ice accumulation depths with meteorological parameters cont rolling ice accretion

    (a)daily minimumtemperature, (b)daily relative humidity, (c)daily wind speed, (d)daily rainfall amount

    Fig. 4  Relationship between pairs of meteorological parameters controlling ice accretion

    (yellow, green, blue and red dots represent samples with ice accretion depth of 0 mm, 0-20 mm, 20-40 mm and >40 mm, respectively) (a)daily minimum tempera ture vs daily relative humidity, (b)daily minimum temperature vs daily wind speed, (c)daily minimum tempera ture vs daily rainfall amount, (d)daily relative humidity vs daily wind speed

    Table  1  Statist ics about the stations with ice accretion observation over China

    Table  2  Trend for yearly maximal ice accumulation depth and ice accumulation days

    Table  3  Partial correlation coeff icients between ice accumulation depth and meteorological parameters controlling ice accretion for 4 days including the ice-accretion-depth-observed day and 1-3 days before that

    Table  4  Assessment of ANN model by comparing prediction grades with observation grades

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    • Received : 2009-02-10
    • Accepted : 2016-01-13
    • Published : 2009-12-31

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