Predicting Temperature Rise in Medium Voltage Switchgear within the Smart Grid Environment and the Application of a Random Forest Model

With the continuous development of smart grid technology, the reliability and stability of electrical equipment have become particularly important. In this context, medium-voltage switchgear is one of the critical components of the power system, and its temperature rise problem has a potential impact on the regular operation of the equipment and the system's stability. To address this problem, this study proposes a temperature rise prediction method based on the random forest model based on the smart grid environment, aiming to identify possible temperature rise problems in advance and take appropriate measures. We obtained operating data from the 6kV medium-voltage switchgear, including temperature, current, and voltage characteristics. The research also considered factors related to temperature rise, such as load status, ambient temperature, and humidity. A random forest model was constructed and trained through feature extraction and data preprocessing. The results and analysis section shows the temperature rise results predicted by the random forest model. It compares them with actual values, highlighting the advantages of the random forest model in predicting the temperature rise of medium-voltage switchgear and its application in smart grid environments. This research is of great significance to improving the reliability of electrical equipment and provides a valuable reference for temperature rise prediction in a smart grid environment.