The paper presents a novel method that by coupling of the measurement data with an advanced 3D, extensible cable model allows for the prediction of power line configuration. The method, which may be considered as an example of the predictive digital twin, is presented in two variants that were validated throughout a comparison of the predictions with an in situ geodetic surveying. It has resulted in an excellent agreement of the computed and the real power cable shapes and confirmed reliability of the proposed approaches. We use on-line measured temperature and inclinations at a certain point on a cable to enhance the non-linear mechanical model (not the simple catenary curve) of the cable sag. The proposed coupling of the real data and a computational model is done twofold, by modification of either selected parameters or equations used to model power cable configuration. In both approaches, our power cable model is combined with a rigid body model of insulator chains. The proposed predictions with quantified uncertainties may support the power line dynamic rating that in turn may significantly increase the capacity of electric transmission systems. The key component of such systems is the processing of the collected data in order to determine the maximum current that can be transmitted safely, without violating the required clearance space. Making use of the advanced 3D, extensible cable model, instead of the catenary curve, insignificantly increases computation time, however it enables taking into account out of plane loading (wind), data overload as well as effective using of measurement uncertainties.

本文提出了一种新颖的方法，通过将测量数据与高级3D耦合，可扩展电缆模型可以预测电源线的配置。该方法可以看作是预测性数字孪生的一个示例，以两种变体形式呈现，在通过对原位大地测量进行的预测比较中，两种方法均得到了验证。它已使计算出的电缆形状和实际的电缆形状获得了极好的一致性，并证实了所提出方法的可靠性。我们使用电缆上某一点的在线测量温度和倾斜度来增强电缆松弛的非线性力学模型（而不是简单的悬链曲线）。实际数据和计算模型的拟议耦合是双重完成的，通过修改所选的参数或用于建模电力电缆配置的方程式。在这两种方法中，我们的电力电缆模型都与绝缘子链的刚体模型相结合。具有量化不确定性的拟议预测可以支持电力线动态额定值，进而可以显着提高电力传输系统的容量。这种系统的关键组件是处理收集到的数据，以便确定可以安全传输的最大电流，而不会违反所需的间隙空间。利用先进的3D可扩展电缆模型代替悬链曲线，不会显着增加计算时间，但是，它可以考虑平面外载荷（风），数据过载以及有效利用测量不确定性的问题。