In recent decades, leading companies and research groups have extensively conducted Multiphysics computational fluid dynamics (CFD) analyses to evaluate temperature rise in switchgear systems, aiming to meet type-testing requirements specified in IEC standards. However, the complex interaction of geometrical and operational parameters presents significant challenges in interpreting these methods. Artificial intelligence (AI) algorithms have gained attention in various engineering fields to address similar issues. This paper investigates the influence of four key manufacturing and operational parameters, both categorical and continuous, on temperature rise in a medium voltage (MV) switchgear case study. A CFD-based dataset was created from these parameters to target maximum temperature, facilitating the study's objective. Several models for temperature rise estimation, including extreme gradient boosting (XGBoost), support vector regression, decision tree, and random forest, were compared. An explainable artificial intelligence (XAI) technique, Shapley Additive Explanations (SHAP), was applied to the best-performing model to evaluate the importance of each feature in predicting maximum temperature. The results revealed that XGBoost provided the most accurate predictions, with a scatter band (SB) of ±1.01 and average R2 values of 99.98 % and 96.59 % for the training and testing sets, respectively. SHAP analysis identified the most significant variables affecting temperature prediction as current, air velocity, duct area, and switchgear conditions, in that order.

中文翻译：

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使用多物理场 CFD 分析对中压开关设备中的温升进行可解释的机器学习建模


近几十年来，领先的公司和研究小组广泛开展了多物理场计算流体动力学 （CFD） 分析，以评估开关设备系统中的温升，旨在满足 IEC 标准中规定的型式测试要求。然而，几何参数和操作参数的复杂相互作用对解释这些方法提出了重大挑战。人工智能 （AI） 算法在各个工程领域都受到了关注，以解决类似的问题。本文研究了中压 （MV） 开关柜案例研究中四个关键制造和操作参数（分类和连续）对温升的影响。根据这些参数创建了一个基于 CFD 的数据集，以达到最高温度为目标，从而促进了研究的目标。比较了几种温升估计模型，包括极端梯度提升 （XGBoost）、支持向量回归、决策树和随机森林。一种可解释的人工智能 （XAI） 技术 Shapley 加法解释 （SHAP） 应用于性能最佳的模型，以评估每个特征在预测最高温度方面的重要性结果显示，XGBoost 提供了最准确的预测，训练集和测试集的散射带 （SB） 为 ±1.01，平均 R2 值为 99.98 % 和 96.59 %。 分别。SHAP 分析确定了影响温度预测的最重要变量，依次为电流、风速、管道面积和开关设备条件.