IOT Based Transformer Monitoring and Controlling

Abstract

Abstract: Transformers are critical components of electric powersystems, yet precise fault identificationremains difficult. The study presents a novel transformer defect diagnostic approach based on an Internet of Things (IoT) monitoring sysem and ensemble machine learning (EML). The IoTbased monitoring system is divided into two parts: data measuring subsystemand a data reception subsystem. To begin, the data measuring subsystem measures transformer vibration signals, which are then relayed to the remote server via the data receipt subsystem. Then an EML is proposed that is made up of deep belief networks (DBNs), stacked denoising auto encoders (SDAs) with distinct activation functions, and relevance vector machines (RVMs). DBN sand SDAs are respectively used to extract features from the signals, and RVMs are respectively employed as classifier. In order to ensure efficient of the EML, a novel combination strategy is proposed. A transformer fault diagnosis experiment is performed