Prediction model of wire de-icing jump height based on BP neural network BP neural network

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Journal of Vibration and Shock ›› 2021, Vol. 40 ›› Issue (1) : 199-204.

WEN Nan1, YAN Bo1, LIN Xiang1, HUANG Guizao1, L Zhongbin2, ZHANG Bo2

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Abstract

De-icing vibration of wire in ice area can reduce insulation gap, even cause flashover, trip and other electrical accidents. The maximum de-icing jump heights of wire under conditions of different parameters were numerically simulated, and numerical simulation results and a BP neural network were used to build a prediction model for wire de-icing jump height. Taking structural parameters, such as, number of wire splits, wire type, wire span, and height difference, and load parameters, such as, initial stress, icing thickness and de-icing rate as the input and the maximum wire de-icing jump height as the output, the model was optimized through machine learning and the evaluation index was used to assess the model’s accuracy. It was shown that the model can determine the maximum wire de-icing jump height conveniently and quickly; it can provide a reference for insulation gap design of transmission line in ice area.

Key words

wire / numerical simulation / de-icing jump height prediction / BP neural network / machine learning

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WEN Nan1, YAN Bo1, LIN Xiang1, HUANG Guizao1, L Zhongbin2, ZHANG Bo2. Prediction model of wire de-icing jump height based on BP neural network BP neural network[J]. Journal of Vibration and Shock, 2021, 40(1): 199-204

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