The mechanical behavior at contact points in high-voltage electrical cable conductors is simulated to find where damage may occur. The presented approach is based on a numerical model that is referred to as the wire model. The critical zones are detected by simulating the mechanical behavior of a cable conductor section with a large number of contacts. For the wire model, a relevant regularized contact law that improves convergence of the contact algorithm compared to the standard contact law is implemented. This model is validated by making comparisons with experimental results available in the literature for various conductors. However, this approach employs a costly finite element method. In order to reduce the evaluation cost and allow sensitivity analysis of the input parameters, a surrogate model is proposed to replace the initial wire model. The surrogate model approximates the input–output relationship of the costly wire model. The choice of the sampling method that allows selection of the best fitting surrogate model for this application is discussed, but comparisons show little influence on the obtained scores. The robustness of the obtained result depends mostly on the chosen surrogate model, where the scores obtained with random forest and neural network are better than the scores obtained with linear and polynomial regression. The application is the French high-voltage network, but the work is relevant for any other network.

模拟高压电缆导体中接触点的机械行为，以找出可能发生损坏的位置。所提出的方法基于称为线模型的数值模型。通过模拟具有大量触点的电缆导体部分的机械行为来检测关键区域。对于线模型，实施了相关的正则化接触定律，与标准接触定律相比，该定律提高了接触算法的收敛性。该模型通过与文献中可用的各种导体的实验结果进行比较来验证。然而，这种方法采用了昂贵的有限元方法。为了降低评估成本并允许对输入参数进行敏感性分析，提出了一种替代模型来代替初始线模型。代理模型近似于代价高昂的线模型的输入-输出关系。讨论了允许为此应用程序选择最佳拟合替代模型的采样方法的选择，但比较显示对获得的分数影响很小。所得结果的稳健性主要取决于所选择的代理模型，其中随机森林和神经网络获得的分数优于线性和多项式回归获得的分数。该应用程序是法国高压网络，但该工作与任何其他网络相关。但比较显示对获得的分数影响不大。所得结果的稳健性主要取决于所选择的代理模型，其中随机森林和神经网络获得的分数优于线性和多项式回归获得的分数。该应用程序是法国高压网络，但该工作与任何其他网络相关。但比较显示对获得的分数影响不大。所得结果的稳健性主要取决于所选择的代理模型，其中随机森林和神经网络获得的分数优于线性和多项式回归获得的分数。该应用程序是法国高压网络，但该工作与任何其他网络相关。