AI-Based Temperature Monitoring System for Hydro Generators

Hydrogenerators operate in challenging environments, and temperature variations can significantly impact their performance. Temperature monitoring systems often rely on remote infrared and contact real-time temperature measurement of the generator. AI-based temperature monitoring systems offer a state-of-the-art solution, providing continuous, automated, and predictive maintenance capabilities. The paper presents a complex AI-based fuzzy decision-making algorithm for anomaly (overheated rotor pole) detection and failure prediction. Time-based analysis of operational parameters from SCADA systems is essential for failure prediction. The use of recurrent neural networks for time-series analysis and convolutional autoencoders for anomaly detection is especially emphasized. The paper also presents a fuzzy decision-making method that uses fuzzy logic to make decisions after analyzing the output of the AI models.