Physics Informed Neural Networks Predicting Junction Temperature of Power Devices for PV-Based DC Microgrids

This paper proposes a physics-informed neural network model to predict the junction temperature of power devices utilized in the converter that integrates photo voltaic (PV) to the DC microgrid. The power converter operates at maximum power tracking point mode. The model considers solar irradiance, ambient temperature, output voltage of PV, and switching frequency as inputs. The model is trained using a wide range of real-world operating conditions from the National Renewable Energy Laboratory (NREL) database. The importance and utility of the proposed model are presented using two different scenarios. In one of the scenarios, the model is used to predict the duration of different overload conditions that the device can sustain. Such a prediction can help to develop prognostic methods to ensure a device operates within the allowed temperature limits. The other scenario demonstrates the modification in the switching frequency of the converter if the prediction temperature of a device exceeds the allowed limit. This scenario eliminates the need for temperature sensors and again ensures the safe operation of a device.