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By: Alok Kumar and Satyam Kumar.
1.Assistant Professor, Department of Electrical Engineering Bansal Institute of Engineering and Technology Lucknow, India
2.Student, Department of Electrical Engineering Bansal Institute of Engineering and Technology Lucknow, India
Quickly detecting and pinpointing failures in electrical distribution lines are important for shortening the duration of service interruption, reducing maintenance costs and increasing safety. In this work, we propose a realistic architecture for an Internet-of-Things (IoT)-enabled fault detection system designed for distribution networks. In order to continually assess electrical characteristics including voltage, current, frequency, temperature, and line status, the suggested architecture combines inexpensive, dispersed sensor nodes placed along feeders and important network points. These sensor nodes connect to edge devices via dependable wireless protocols, where they execute initial data preprocessing, filtering, and anomaly detection in order to minimize latency and bandwidth consumption. Deployed at the edge gateway or in the cloud, advanced machine learning (ML)-based analytics are utilized for fault detection, classification (e.g., line-to-ground, line- to-line, open conductor), and severity estimation. The architectural proposal fuses low-cost sensor nodes as down to the network, edge based data preprocessing and cloud or edge gateway oriented ML based analytics. We talk about the elements of the system, the data flow, fault detection and classification techniques (both rule-based and ML- based), as well as potential approaches to fault localization. Finally, we present highlevel sketch of an experimental plan and discuss the involved trade-offs. The idea is to help utilities transition from reactive maintenance to real-time monitoring and active fault management.
Keywords – IoT, distribution lines, fault detection, machine learning, smart grid edge computing.
Citation:
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