Deployment of an IoT based sensor node for faults detection and classification in electrical secondary distribution networks

Abstract

The Electrical Secondary Distribution Networks (ESDN) are very complex with high user density, making detection of defects and faults very challenging. In many developing countries, faults in ESDN have been reported mainly by customers and visual inspection by utility personnel. This process is time-consuming, costly, and among the causes of inefficient power supply to the end-users. The existing systems using Supervisory Control and Data Acquisition (SCADA) and Phasor Measurement Units (PMU) in the transmission and primary distribution networks are not efficient for fault detection in ESDN. The PMUs and SCADA systems relied mainly on centralized processing that is inefficient and relatively expensive for the secondary distribution network. This study proposes the architecture for fault detection and classification in the ESDN using Internet of Things (IoT) based architecture on distributed processing. The deployed IoT based sensor nodes were designed using the raspberry-pi and micro-controllers. The algorithms for fault detection and classification were designed and deployed in the prototype. The results show that the deployed sensor node obtained 98% accuracy and 18 ms faults detection time. The results implies that the deployed architecture using the IoT based sensor nodes, which is based on distributed processing, can be used for fault detection and classification in the ESDN.