<![CDATA[Distribution transformers are crucial components in power distribution systems that require real-time monitoring to prevent disruptions and ensure efficient operation. Internet of Things (IoT)-based monitoring systems offer innovative solutions with the ability to detect disruptions early and provide real-time information to operators. This study proposes an optimized early-detection monitoring system for distribution transformers by utilizing IoT devices, including temperature, pressure, and humidity sensors installed on the transformers to monitor their operational conditions. Data collected through the sensors is then processed using a cloud platform for further analysis, with machine learning algorithms used to detect anomalous patterns indicating potential disruptions. Furthermore, the system is designed to send automatic notifications to operators via a mobile application or web dashboard when a disruption is detected, enabling faster response and prevention of further damage. Test results show that the system is capable of detecting disruptions with high accuracy and can improve maintenance efficiency and reduce transformer operational downtime. The implementation of IoT in distribution transformer monitoring also opens up opportunities for optimizing condition-based maintenance management that is more economical and proactive.]]>
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