Transformer System for Precise Health Surveillance Via IoT Technology

This paper presents a comprehensive software demonstration of a transformer equipped with temperature, voltage, and current sensors to evaluate the performance of a newly developed transformer defect detection system and analyze load effects. The proposed approach leverages the relationship between the differential of the immediate input and output voltage to identify faults effectively. A non-contact access control system that utilizes RFID technology is added through an Arduino-based system which enables authorized personnel to use radio-frequency authentication. The EM-18 RFID module serves to read RFID tags that trigger data processing through an Arduino board to execute authorization for ON-OFF control. The development of a detailed finite element model helps achieve accurate modelling of transformer behaviour when exposed to different winding mechanical deformations. Laboratory tests show that the proposed V-I locus behaves as anticipated when facing winding short-circuit faults and system harmonic conditions which provides practical insights for fault identification. The research pursues a technique optimization by conducting practical tests to analyse how system harmonics affect the VI locus shape. The integration of theoretical modelling activities with experimental verification results in new developments for an intelligent transformer management system that safeguards against faults and remains operational under adverse conditions.