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首页> 外文期刊>Journal of nano research >Neural Network Model to Estimate Resistivity of Ground Enhancers Reinforced with Graphene Nano Particles for Transmission Lines
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Neural Network Model to Estimate Resistivity of Ground Enhancers Reinforced with Graphene Nano Particles for Transmission Lines

机译：用石墨烯纳米颗粒加固耐膨胀管施加速度升温的神经网络模型

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摘要

For many high voltage transmission lines, lightning is the first cause of outages. Different alternatives have been used to diminish these outages, like the use of counterpoise wires, installation of surge arresters, and the improvement of the grounding system using ground enhancers or chemical enhancers. In this paper, graphene nano particles were used to reformulate commercial ground enhancers. The results of this research end up in an improvement factor of up to 100 times the reduction in resistivity, when graphene nano particles are used. After lightning current impulse tests done on both types of samples, the performance of the un-reformulated ground enhancer samples shows a faster deterioration than the graphene reinforced ground enhancer samples. In order to establish a criterion to quantitatively rank the chemical ground enhancers' performance after the lightning impulse current tests, a neural network model was developed.
机译：对于许多高压传输线，闪电是第一次停用的原因。已经使用不同的替代方案来减少这些中断，例如使用逆线，电涌避雷器的安装，以及使用地面增强剂或化学增强剂的接地系统的改进。在本文中，石墨烯纳米颗粒用于重新制备商业植物增强剂。当使用石墨烯纳米颗粒时，该研究的结果最终处于高达100倍的提高因子，电阻率降低100倍。在两种类型的样品上完成雷电电流脉冲测试后，未重新重新重整的地面增强剂样品的性能显示比石墨烯增强地增强剂样品更快的劣化。为了建立定量等级地等级的标准，在雷电脉冲电流测试之后进行化学基础增强剂的性能，开发了一种神经网络模型。

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