Explainable artificial intelligence modeling of internal arc in a medium voltage switchgear based on different CFD simulations

Abstract

The internal arc represents an unintentional release of electrical energy within the switchgear industry. Manufacturers must address this electro-thermal issue in their switchgears. Over the past decades, various researchers and engineering groups have examined the internal arc pressure rise in switchgears to mitigate damages. The high variability in pressure rise among switchgears due to diverse factors such as design, manufacturing, and electrical parameters results in varying reported pressure increases. This issue motivates the application of artificial intelligence (AI) in interpreting internal arc modeling. The present paper explores the impact of manufacturing parameters such as total duct width (TDW), height (H), and ducts condition (DC), along with environmental parameters like initial pressure (IP) and initial temperature (IT), on the maximum pressure (MP) generated during an internal arc in a medium voltage (MV) switchgear. For this purpose, 54 different computational fluid dynamics (CFD) models were built using the parameters indicated. An extreme gradient boosting (XGBoost) machine learning (ML) model was trained using different CFD models, with MP serving as the target variable for the ML model. The obtained results reveal a variation in the MP of the internal arc under the mentioned parameters, ranging from 17835.45 Pa to 144423.2 Pa. Using SHAP data revealed that IP, TDW, and DC were the most significant factors affecting the pressure increase of the internal arc phenomena.

Keywords

• Machine learning
• Internal arc of medium voltage switchgear
• Computational fluid dynamics
• Shapley additive explanation
• Extreme gradient boosting