International Journal For Multidisciplinary Research

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Intelligent UAV Surveillance and AI Analytics Framework for DISCOM Operations

Author(s) Mr. Prashant Dutta, Ms. Bidisha Roy, GP CPT Nirmalya DasGupta, GP CPT Sanjay Kumar Pandey, Dr. Pali Sahu, Major Pawan Kumar Thapliyal, Mr. Sachin Agrahari
Country India
Abstract Electricity Distribution Companies (DISCOMs) manage extensive and widely distributed power networks, where traditional manual inspections are often slow, labor-intensive, and no longer sufficient for today’s expectations of reliability and efficiency. This work introduces a holistic approach for implementing drone (UAV) technologies combined with Artificial Intelligence (AI) to advance operations, safety, and business performance in power distribution. Employing drones equipped with various sensors such as RGB, thermal, zoom, and LiDAR, it examines essential applications like equipment health checks, monitoring of vegetation and right-of-way, outage assessments, theft prevention, and consumer mapping. An integrated technical solution is outlined, covering the entire process from drone-based data collection and on-the-spot processing to cloud storage, AI-powered fault detection and prioritization, and automated work order creation that connects with DISCOMs’ existing enterprise platforms (GIS, OMS, ERP, MDM). The paper also outlines practical strategies for gradual deployment in Indian utilities, focusing on tangible metrics including reduced outage durations (SAIDI/SAIFI), decreased AT&C losses, and improved crew safety. The findings show that combining drones with AI not only improves inspection efficiency but also transforms them into key tools for predictive maintenance, revenue assurance, and informed decision-making within power distribution.
Keywords Drones, UAV, Artificial Intelligence, DISCOM, LiDAR, Revenue, Cloud Computing.
Field Computer > Artificial Intelligence / Simulation / Virtual Reality
Published In Volume 8, Issue 1, January-February 2026
Published On 2026-02-13
DOI https://doi.org/10.36948/ijfmr.2026.v08i01.68865
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