Interpretable machine learning modeling of temperature rise in a medium voltage switchgear using multiphysics CFD analysis

IF 6.4 2区 工程技术 Q1 THERMODYNAMICS
Mahmood Matin , Amir Dehghanian , Amir Hossein Zeinaddini , Hossein Darijani
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引用次数: 0

Abstract

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.

Abstract Image

使用多物理场CFD分析的中压开关柜温升的可解释机器学习建模
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,以评估开关柜系统的温升,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在解释这些方法时提出了重大挑战。人工智能(AI)算法在各个工程领域得到了关注,以解决类似的问题。本文以一个中压开关柜为例,研究了四个关键的制造和运行参数(分类参数和连续参数)对温升的影响。根据这些参数创建了基于cfd的数据集,以确定最高温度,从而促进了研究目标的实现。比较了极端梯度增强(XGBoost)、支持向量回归、决策树和随机森林等几种温升估计模型。一种可解释的人工智能(XAI)技术,Shapley加性解释(SHAP),被应用于表现最好的模型,以评估每个特征在预测最高温度中的重要性。结果表明,XGBoost提供了最准确的预测,其散射带(SB)为±1.01,平均R2值分别为99.98%和96.59%。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。
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来源期刊
Case Studies in Thermal Engineering
Case Studies in Thermal Engineering Chemical Engineering-Fluid Flow and Transfer Processes
CiteScore
8.60
自引率
11.80%
发文量
812
审稿时长
76 days
期刊介绍: Case Studies in Thermal Engineering provides a forum for the rapid publication of short, structured Case Studies in Thermal Engineering and related Short Communications. It provides an essential compendium of case studies for researchers and practitioners in the field of thermal engineering and others who are interested in aspects of thermal engineering cases that could affect other engineering processes. The journal not only publishes new and novel case studies, but also provides a forum for the publication of high quality descriptions of classic thermal engineering problems. The scope of the journal includes case studies of thermal engineering problems in components, devices and systems using existing experimental and numerical techniques in the areas of mechanical, aerospace, chemical, medical, thermal management for electronics, heat exchangers, regeneration, solar thermal energy, thermal storage, building energy conservation, and power generation. Case studies of thermal problems in other areas will also be considered.
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