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首页> 外文期刊>Journal of Computers >Hybrid GSA-ANN Methods to Forecast Sheath Current of High Voltage Underground Cable Lines
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Hybrid GSA-ANN Methods to Forecast Sheath Current of High Voltage Underground Cable Lines

机译：Hybrid GSA-ANN方法预测高压地下电缆线鞘电流

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摘要

Electrical safety is major issue for electric networks, so high voltage underground cable lines have been used instead of overhead line recently in city center and neighborhood. However, sheath current generates on metallic sheath of high voltage underground cable, and sheath current causes major cable faults and electroshock. Single point bonding, solid bonding and cross bonding are used to reduce sheath current and voltage. If sheath current is determined before high voltage underground cable line is installed, the most suitable method can be used to reduce sheath current and voltage. Hence, cable faults and electroshock can be prevented. There are many factors in formation of sheath current. Thus, formulation of sheath current is very complex and difficult. In this case forecasting methods can be used to determine sheath current, and artificial neural network (ANN) is a powerful method for forecasting studies. In this study, Gravitational Search Algorithm (GSA) and artificial neural network (ANN) is used to reduce training error, and hybrid GSA-ANN method is obtained. It is seen at the end of this study that errors of hybrid GSA-ANN method are less than errors of classic ANN.
机译：电气网络的电气安全是电网的主要问题，因此在市中心和邻域最近使用了高压地下电缆线代替架空线。但是，护套电流在高压地下电缆的金属护套上产生，并且护套电流会导致主电缆故障和静电。单点键合，固体粘接和交叉键合用于减少鞘电流和电压。如果在安装高压地下电缆线之前确定护套电流，则可以使用最合适的方法来减少鞘电流和电压。因此，可以防止电缆故障和电孔。鞘电流形成很多因素。因此，鞘电流的配方非常复杂和困难。在这种情况下，预测方法可用于确定鞘电流，人工神经网络（ANN）是预测研究的强大方法。在本研究中，使用引力搜索算法（GSA）和人工神经网络（ANN）用于减少训练误差，获得混合GSA-ANN方法。在本研究结束时可以看到，混合GSA-ANN方法的误差小于经典安的误差。

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