Untitled

Abstract

In Malaysia, on-site technical personnel manually inspect power transformers. Some vital condition indicators, such as oil and winding temperatures, are not monitored in real-time. This condition can be hazardous if the transformer gets overheated. Overheating can cause mechanical deformation and insulation degradation if not monitored regularly. Thus, an online monitoring system that meets industry standards is needed to enhance power transformer monitoring and troubleshooting. In this research, the Internet of Things (IoT) based data acquisition (DAQ) system was deployed for real-time oil temperature monitoring and inspection to detect incipient faults in power transformers early. This IoT-based DAQ system was connected to the substation remote terminal unit (RTU) to update real-time data on each power transformer. The long-range (LoRa) technology is proposed for the system to transmit temperature, current, and voltage from the power transformers. The data transmission from the oil temperature indicator (OTI), network server, and database was monitored and compared. It is observed that the temperature data was transferred from the network server to the database without any transmission delay. The average deviation from the two experiments was 0.006 and 0.003, respectively, compared to the manual reading from the OTI scale meter with a digital reading by the proposed DAQ system. For testing purposes, the alert