Machine Learning Approach for Smart Distribution Transformers Load Monitoring and Management System

Abstract

Distribution transformers are an integral part of the power distribution system network and emerging smart grids. With the increasing dynamic service requirements of consumers, there is a higher likelihood of transformer failures due to overloading, feeder line faults, and ineffective cooling. As a consequence, their general longevity has been diminished, and the maintenance efforts of utility providers prove inadequate in efficiently monitoring and detecting transformer conditions. Existing Supervisory Control and Data Acquisition (SCADA) metering points are sparsely allocated in the network, making fault detection in feeder lines limited. To address these issues, this work proposes an IoT system for real-time distribution transformer load monitoring and anomaly detection. The monitoring system consists of a low-cost IoT gateway and sensor module which collects a three-phase load current profile, and oil levels/temperature from a distributed transformer network, specifically at the feeder side. The data are communicated through the publish/subscribe paradigm to a cloud IoT pipeline and stored in a cloud database after processing. An anomaly detection algorithm in the form of Isolation Forest is implemented to intelligently detect likely faults within a time window of 24 h prior. A mobile application was implemented to interact with the cloud database, visualize the real-time conditions of the transformers, and track them geographically. The proposed work can therefore reduce transformer maintenance costs with real-time monitoring and facilitate predictive fault analysis.