冰区导线脱冰振动会引起绝缘间隙减小，严重时甚至导致闪络和跳闸等电气事故。首先利用数值方法模拟得到各种参数条件下导线的脱冰动力响应，获得导线的最大脱冰跳跃高度。进而基于数值模拟结果和BP神经网络构建导线脱冰跳跃高度预测模型，将线路的导线分裂数、导线型号、档距、高差等结构参数以及初始应力、覆冰厚度和脱冰率等载荷参数作为输入，最大冰跳高度作为输出，通过机器学习，并采用评价指标评估其准确性，对模型进行优化。该模型可以方便快捷地确定导线的最大脱冰跳跃高度，为冰区输电线路绝缘间隙设计提供参考。
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.
关键词
导线 /
数值模拟 /
脱冰跳跃高度预测 /
BP神经网络 /
机器学习
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Key words
wire /
numerical simulation /
de-icing jump height prediction /
BP neural network /
machine learning
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参考文献
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