Industrial automation of process for transformer monitoring system using IoT analytics

Abstract

Multiple devices interconnected with each other via the Internet are the key concept behind IoT. It allows autonomous devices with the possibility to use the Internet for communication and exchange of data. This paper focuses on monitoring the transformer in real-time fault detection and records distinct operating parameters of the transformer like voltage imbalance, load current, transformer oil levels, temperature, vibration. Based on these parameters, the transformers fail state (i.e. a state where transformer malfunctions or completely stops working) and health (i.e. voltage, current, oil levels, temperature and vibration) are predicted by making use of an artificial neural network (ANN) algorithm. Use of this technology can minimize working efforts, thereby improving accuracy, stability, efficiency. Thus, remote monitoring and machine control are achieved, as well as ANNs help to determine the performance and yield appropriate measures accordingly. In this case, sensors are used to sense the important parameters of equipment such as current, voltage oil level in any operating transformer. By analyzing relevant data using ANNs, this system will be beneficial in many industries. Likewise, this system is generalized to be used in a wide array of industrial automated machines.