Cloud Based IoT Solution for Fault Detection and Localization in Power Distribution Systems /

Abstract

This research work presents a robust, scalable and novel cloud based internet of things (IoT) system for localization and identification of faults in power distribution systems (PDS). Efficient fault localization techniques in power distribution systems can result in reduced power recovery times. Target of this research work is to design a new algorithm based on the modern IoT communication infrastructure that can detect simultaneous and individual faults in power distribution network. The algorithm employs a zone-based approach to detect and localize faults. To reveal different faults, current sensing devices (CSD) are installed at zone boundaries. The objective of CSD is to yield time synchronized current measurements. The current measurements are communicated to cloud server through an edge device (ED). Furthermore, context aware policy (CAP) was introduced in ED. Using CAP only that data was sent to the cloud which was different from previously transmitted measurements. As a result, significant improvement was achieved in terms of communication channel bandwidth. Furthermore, a device failure mechanism was added to detect single or multiple simultaneous device failures. In case of device failures or unavailability of complete sensor data, the proposed system can modify the underlying algorithm in cloud server automatically to provide optimal results based on the available information. Fault data was stored in a relational database model also hosted in the cloud server. In order to substantiate the performance of presented algorithm, an IEEE 37 node test feeder was selected as PDS. Three test cases were implemented to observe the performance under different conditions. First two test cases were designed to validate the performance of our system under individual and multiple simultaneous faults in PDS. In the third test case single as well as multiple device failures were introduced to establish the robustness and scalability of the proposed solution. It was found that the new algorithm successfully detected the faults for all three test cases. Consequently, due to the adoption of CAP, significant reductions were noticed in the volume of information sent over the network. In the end, a comparative study was presented with existing methods to further highlight the benefits of our technique.