FAULT MONITORING AND DETECTION OF TRANSFORMER USING INTERNET OF THINGS

The transformer, an essential component in the electric grid, plays a prominent role in transmitting electricity between circuits while adjusting voltage levels. Nevertheless, transformers are often confronted with issues like temperature elevation and various faults. Traditional transformer monitoring approaches are laborious and lack accuracy. In this case, the objective of this work is to develop an IOT Monitoring platform with the capability to monitor fault detection and fault and health monitoring of transformers. This system collects real-time data from the sensors connected to the transformer and is displayed in a platform available dynamically by an IP address configured by the user. The data collection and processing are managed by the ESP8266 microcontroller. The regression model analysis is approached to predict the earlier fault detection. To evaluate the transformer parameters, IOT sensors are used. A LM35 Sensor is used to monitor the winding temperature and compare it with the atmosphere. This way, if there is an overheating issue, a reading can be made immediately and necessary action can be taken. The system also has the ability to isolate the fault. This is achieved by using a ZMPT101B Module. It also has the capability to monitor load voltage. To monitor the short circuit current, a ACS712 current sensor is being used. A DS180Bl sensor is used to monitor the temperature of the transformer oil. The obtained sensor data is then displayed within the IOT dashboard. The results attained from this work demonstrate the effectiveness of the proposed IOT system. It also saves significant time without physically them.