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@article{201439,
        author = {Manav Gopinath and Somiel Gohil and Atharva Amrute and Medha Asurlekar},
        title = {A Cost-Effective IoT and AI-Based System for Real-Time Detection of Low Voltage AC Distribution Overhead Conductor Breakage},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {12},
        pages = {3625-3628},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=201439},
        abstract = {This paper discusses about the design and implementation of a low-cost IoT and AI based real time conductor break-age detection of LV AC distribution overhead conductors. This system utilizes low-cost sensors to constantly measure the electrical parameters (current, voltage, and line continuity). An AI based fault detection algorithm using the XGBoost for data processing in a pipeline identifies breakage patterns from the other normal load or transient faults, thereby reducing the false alerts considerably. The system also dispatches real time alerts to KSEBL control center in case of confirmed faults through GSM networks. The prototype prototype records an accuracy ranging from 85-92% with end-to-end alert latency of below 5 seconds with the total hardware cost being around 2400.},
        keywords = {LV conductor break-age; Fault detection; IoT; Edge computing; XGBoost; Machine Learning; GSM alert; power distribution},
        month = {May},
        }