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Improvement of safety operating conditions in overhead conductors based on ampacity modeling using artificial neural networks

机译:基于使用人工神经网络的载流量建模的架空导线安全操作条件的改进

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Thermal ratings are usually considered for planning the operating conditions for overhead lines and are usually obtained with static parameters. These conditions can be improved using dynamic ratings based on the region weather forecasts, and this improvement can be ever higher when a local prediction is performed at the point where the line is located. In this work, a model based on artificial neural networks techniques is applied to predict the ampacity property of a transmission overhead line, in order to adjust and optimize the operation point of the grid under safety conditions. These predictions are calculated for a time horizon of 24 hours and are validated with actual conditions of a real overhead line monitored by sensors. With the conclusion that applying the selected model, the operational security of the conductor can be improved, passing from a 17.82% of overheating conditions to only a 3.91%.
机译:通常在计划架空线的工作条件时考虑使用热额定值,并且通常使用静态参数获得热额定值。可以使用基于区域天气预报的动态评级来改善这些条件,并且当在线路所在的位置执行本地预测时,这种改善可能会更高。在这项工作中,基于人工神经网络技术的模型被用于预测输电架空线的载流量特性,以便在安全条件下调整和优化电网的运行点。这些预测是针对24小时的时间范围计算的,并已通过传感器监控的实际架空线的实际条件进行了验证。得出结论,应用所选模型,可以改善导体的操作安全性,将过热条件从17.82%降低到仅3.91%。

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