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Calculation of transient temperature of three-core cables based on BP neural network trained by particle swarm optimization algorithm

机译:基于粒子群算法训练的BP神经网络的三芯电缆瞬态温度计算

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摘要

The conductor temperature is an important state indicator of an operation cable,. But direct measurement of the cable conductor temperature is difficult to realize in practice, which is usually obtained by calculation. Presently, thermal circuit method and numerical calculation are adopted to calculate the conductor temperature, however the accuracy of the two methods are greater affected by physical parameters of the cable itself. Therefore, a method of calculation of three-core cable conductor transient temperature by using BP neural network based on particle swarm optimization is introduced in this paper. A four-layer neural network model is built by using the load current and outer sheath temperature as inputs, using cable conductor temperature as output. Particle swarm optimization algorithm is used to optimize the network first, and then BP algorithm is used to make a second optimization. The research shows that BP neural network based on particle swarm optimization can be used in accurately calculating three-core cable conductor temperature, avoiding of the effecting of its physical parameters, moreover, it also can give a reference on monitoring the state of operating three-core cable.
机译:导体温度是操作电缆的重要状态指示器。但是实际上很难直接测量电缆导体的温度,这通常是通过计算获得的。目前,采用热回路法和数值计算法来计算导体温度,但是两种方法的精度受电缆本身物理参数的影响较大。因此,本文提出了一种基于粒子群算法的BP神经网络计算三芯电缆导体瞬态温度的方法。通过使用负载电流和外部护套温度作为输入,使用电缆导体温度作为输出,构建了一个四层神经网络模型。首先使用粒子群优化算法对网络进行优化,然后使用BP算法进行第二次优化。研究表明,基于粒子群算法的BP神经网络可用于准确计算三芯电缆导体温度,避免了其物理参数的影响,也可为监测三芯电缆运行状态提供参考。芯线。

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