Tata Consulting Engineers (TCE), in collaboration with Enline and Gridpulse, has commissioned a 400 kV dynamic line rating (DLR) project for Power Grid Corporation of India (PGCIL) on the Madurai–Tuticorin transmission line in Tamil Nadu.

The project employs a real-time, artificial intelligence (AI)-driven approach to improve renewable energy integration, optimize existing grid assets, and enhance operational reliability.

TCE delivered what is claimed as India’s first 400 kV DLR project on a 95 km double-circuit transmission line. The project used DLR sensors placed on critical spans of the Madurai–Tuticorin line to collect real-time weather and line data.

The installation was carried out using hotline methods to avoid power interruptions and adhered to safety and regulatory standards.

The project’s AI-powered platform provided 168-hour forecasting capabilities, offering grid operators advanced warning of potential overloads and weather-related challenges. The DLR deployment is part of PGCIL’s effort to digitize grid operations and accommodate the growing share of renewable energy in Southern India. The Madurai-Tuticorin line plays a key role in evacuating wind power from Tamil Nadu’s coastal and inland wind zones, which frequently experience high generation during cool, windy conditions that favor higher line ampacity.

Digital Technology for Smarter Grids

DLR is recognized in the National Electricity Plan (NEP) 2024 as a critical digital technology for enabling energy transition, improving grid reliability, and optimizing transmission assets.

Unlike traditional static line rating (SLR), which operates on conservative assumptions based on worst-case weather conditions, DLR continuously recalculates a transmission line’s actual current-carrying capacity in real time using weather and operational data. This approach ensures safe, efficient power transfer while unlocking previously untapped capacity.

DLR systems use sensors and satellite data to monitor line conditions, including ambient temperature, wind speed and direction, solar radiation, conductor temperature, and sag.

The information is processed through AI and predictive analytics to adjust line ratings dynamically every few minutes. This allows operators to increase usable transmission capacity by 30%–50% without constructing new infrastructure, significantly improving efficiency and reducing congestion in renewable-rich regions.

Technology Implementation

The system integrates data from meteorological satellites, ground-based sensors, and trusted weather providers with advanced AI algorithms to model conductor cooling behavior and forecast real-time capacity.

Sensor arrays installed on the transmission line measured conductor temperature, sag, mechanical tension, and vibration.

This data was cross-validated with weather inputs to ensure accuracy and reliability. Enline’s hybrid AI model maintained functionality even during sensor or data feed failures by relying on weather-based estimates.

The platform also incorporated a digital twin architecture, creating a virtual replica of the transmission line to simulate and predict performance under various conditions, such as changes in temperature or wind speed.

The real-time dashboards provided operators with a detailed view of line performance, including temperature, sag, and available headroom, enabling immediate operational decisions.

Benefits and Operational Impact

According to TCE, the implementation of DLR on the 400 kV Madurai–Tuticorin line has resulted in measurable operational improvements. The system increased the line’s utilization by up to 25% during favorable weather conditions, enabling more efficient power evacuation from nearby wind farms.

This has directly contributed to reducing renewable energy curtailment, improving transmission reliability, and enhancing overall system stability.

DLR offers significant capital efficiency by deferring the need for new transmission infrastructure. Traditional high-voltage corridor construction can take 5–10 years and involves high costs, regulatory approvals, and land acquisition challenges.

In comparison, the cost of DLR deployment is estimated to be less than 5% of constructing new transmission lines with equivalent capacity. By optimizing existing infrastructure, utilities can redirect capital toward digital modernization and other grid enhancement projects.

The technology enables operators to make informed decisions about load management and maintenance scheduling, thereby reducing the risk of outages and equipment failures.

The Ministry of Power targeted expanding India’s power transmission network to 648,000 circuit kilometers (ckm) in 2032 from 485,000 ckm in 2024. The plan is to meet a peak electricity demand of 458 GW by 2032. This plan will help meet the increasing electricity demand and facilitate the integration of renewable energy and green hydrogen into the grid.

The Central Electricity Authority has proposed the installation of one automatic weather station for each renewable energy project with a capacity of 50 MW. The proposal addresses the need for real-time weather measurement to optimize renewable energy generation, given that solar and wind generation are weather-dependent.