APEPDCL Deploys AI-Driven Grid Monitoring to Predict and Prevent Outages

APEPDCL deploys AI-driven grid system to predict and prevent outages

APEPDCL has upgraded grid maintenance with a practical AI rollout that fits existing field routines, avoids expensive drone programs, and keeps crews focused on work that matters. Linemen and assistant executive engineers now use smartphones to spot issues earlier and cut outage risk without changing how they work.

"I envisioned a system that could identify damaged power poles before catastrophic failures occur," said chairman and managing director Prudhvi Tej Immadi. The initiative was built by APEPDCL's IT team, students from Andhra University, and a Bengaluru-based technology firm.

How APEPDCL built it

In March 2025, the utility stood up an on-premise AI server and integrated a computer vision workflow into routine field surveys. Crews capture images with smartphones; the model flags leaning poles and tilted cross-arms with high consistency.

Students were trained in GPU programming and AI workflows, while the IT team-led by Van Srinivas-coordinated field data collection to train and refine the models. The result: a low-cost, high-leverage system that scales with everyday patrols.

What it delivers now

• Automated detection of leaning poles and tilted cross-arms during standard surveys.
• Planned extensions for insulator defect detection and vegetation growth monitoring.
• Smart meter analytics for faulty meter detection (to prevent revenue loss), granular load graphs to identify low-voltage pockets, and improved load forecasting for proactive grid management.

These analytics feed directly into daily decision-making, giving supervisors earlier signals and clearer priorities for field deployment.

Next on the roadmap

• LLM hosted inside APEPDCL's firewall so officers can query systems in natural language, access SOPs, and receive step-by-step corrective actions.
• Local demand forecasting at feeder and distribution transformer levels to surface early overload risk and schedule timely load-shifting with the right crew at the right time.

Why this matters for operations

• Faster issue triage: From "find and fix" to "predict and schedule" using image and meter signals.
• Lower field friction: Uses current patrols and smartphones-no new hardware burden for crews.
• Better revenue protection: Detects faulty meters and low-voltage zones before complaints and losses mount.
• Actionable visibility: Feeder/DT-level demand forecasts improve switching and manpower planning.

Action checklist for operations leaders

• Start with what you have: Integrate image capture into routine patrols; avoid parallel processes.
• Build a labeled dataset: Leaning poles, tilted cross-arms, damaged insulators, vegetation proximity.
• Stand up an on-prem GPU node: Keep data in-network; shorten training and inference loops.
• Close the loop: Route AI flags into existing ticketing/dispatch and track resolution times.
• Instrument KPIs: Outage minutes avoided, meter fault rate, low-voltage incidents, forecast error at feeder/DT level, crew response and clearance times.
• Train the field: Short modules on photo capture angles, distance, and lighting improve model accuracy quickly.

Governance and risk

• Security: Host AI and LLM inside the firewall; apply role-based access to SOPs and operational data.
• Model lifecycle: Monitor drift, re-label edge cases from the field, and retrain on a fixed cadence.
• Change management: Keep crews in the loop; share weekly wins (issues detected early, outages avoided) to drive adoption.

Related context

For broader context on advanced metering programs, see India's national efforts on smart metering via EESL smart meters initiative. If your team is planning internal upskilling for AI workflows in utility operations, explore curated training by role at Complete AI Training.