Monitoring of Transformer Hotspot Temperature Using Support Vector Regression Combined with Wireless Mesh Networks

The accurate monitoring of the internal hotspot temperature in transformers is crucial for ensuring the stability of power grid operations. Traditional methods typically measure only the surface temperature of transformers, whereas this study proposes a non-invasive thermal inversion algorithm based on a wireless mesh network that effectively predicts the internal hotspot temperature. An electromagnetic-thermal-fluid coupling simulation model was developed to simulate the temperature distribution in transformers under various operating conditions. Subsequently, this study employed a Support Vector Regression (SVR) algorithm to train the sample dataset, optimizing the SVR model using a grid search and cross-validation to enhance the predictive accuracy. After training, the model estimates the hotspot temperature based on surface measurements obtained through a non-contact infrared sensor network. The wireless mesh network, based on the Wi-Fi protocol, provides robust and real-time monitoring even in harsh environments, with data transmitted to a central root node via multiple sensor nodes. The experimental results demonstrate that this method is highly accurate, with predicted temperatures closely matching the results from traditional measurement techniques. This method enhances transformer condition monitoring, helping to extend the transformer lifespan and improve power grid stability.