Transformer vibration and noise monitoring system using internet of things

Abstract

Abstract During continuous operation, transformer problems can occur due to various reasons. In reality, the operating parameters of the transformer have been collected and monitored through the supervisory control and data acquisition (SCADA) systems. However, these systems face many challenges when applied to no‐human substations. Currently, noise signals have been used to detect transformer errors. Abnormal noise recognition and vibration monitoring can recognize the transformer's potential defects and errors. In this study, the authors built an internet of things (IoT) system that allows remote control centres to monitor the condition of transformers through noise and vibration at non‐human substations. The proposed model was equipped with a wireless sensor network node consisting of vibration sensors, audio collectors, Arduino modules, and Lora modules. The authors set up two schemes for the IoT network: one sensor node for a 220‐kV transformer and three sensor nodes for all three phases of the 500‐kV transformer. The data obtained from the sensor node were sent to LoRa Gateway and displayed on the computer through LabVIEW. The study also enabled monitoring of parameters through IoT devices such as Desktops, Laptops, Smartphones from LabVIEW NXG Web VI platform, ThingSpeak, and Amazon S3 storage cloud. In addition, a Model Predictive Control (MPC) algorithm was applied to predict the deterioration of transformer health to maintain the system stability and, hence, prolong the transformer life and operability.

Keywords

• internet of things
• LoRa
• model predictive control
• transformer monitoring
• transformer vibration
• Zigbee