Recurrent Neuronal Networks for the Prediction of the Temperature of a Synchronous Machine During Its Operation

This work presents the development of an adaptive thermal protection system for synchronous machines (SMs), taking into consideration the final cooling temperature and the operation point of the machine. This system aims to improve current thermal protections, which consist of a fixed alarm and trip thresholds regardless of the generator's operating point or ambient temperature. A recurrent neural network (RNN)-based approach has been employed to predict SM temperatures during operation. Multiple tests have been conducted on a specially designed test bench. Inside the windings and iron core of the 5.5 kVA generator, multiple Pt100 sensors have been installed to train the neural network with real temperature values, enabling accurate predictions. The selected RNN model is Long Short-Term Memory (LSTM). Its inputs include electrical variables and the inlet and outlet air temperatures of the SM's cooling system. The results show that the model accurately defines warning and trip thresholds, significantly improving thermal protection, as these thresholds are no longer fixed values. Additionally, the study suggests validating the model under cooling system failures and exploring its application in water-cooled systems. This research is supported by a patent on real-time thermal diagnostics for synchronous machines, highlighting its potential contribution to predictive maintenance and the monitoring of power generation systems.