Smart transformers facilitating efficient power flow and grid intelligence

The growing energy demand projected to reach 388 GW by FY32, necessitates investments in advanced transformer technologies to enhance grid efficiency and integrate renewable energy.

The power sector is undergoing a rapid change, boosted by rising electricity demand, renewable integration, and grid modernisation. Anand Pandey from Electrotherm India Ltd discusses the evolving landscape, smart transformer technologies, and centralised systems like the National Unified Network Management System (N-UNMS), which are likely to play a critical role in enhancing efficiency, reliability, and sustainability across the grid. Let us hear him.

How are Indian transformer manufacturers using smart technologies to enhance grid efficiency and sustainability?

As an Indian transformer manufacturer, we are driving innovation by developing smart, efficient and reliable transformers to support a smarter transmission and distribution infrastructure. We are incorporating IoT-enabled monitoring and advanced materials to enhance energy efficiency, as well as modular designs for increased flexibility. Additionally, we are increasingly prioritising sustainability through the use of eco-friendly materials and implementing predictive maintenance via digital twin technology. Our smart transformers are equipped with sensors and communication capabilities to monitor voltage, current, and temperature parameters in real-time. By analysing this sensor data using AI and machine learning, we can optimise power flow, detect potential issues early and enable predictive maintenance. This real-time monitoring and data-driven approach helps us reduce energy losses and significantly enhance grid efficiency. Data collected from these sensors is analysed using AI and machine learning algorithms to identify potential issues and enable predictive maintenance. This real-time monitoring and analysis help reduce energy losses and increase grid efficiency.

How is the rising integration of renewable energy driving demand for specialised transformers in India’s evolving power landscape?

We are witnessing how the increasing demand for power and the integration of renewable energy sources are reshaping the landscape. This growth is pushing us to expand and modernise the power infrastructure to manage rising electricity consumption and the influx of variable renewable energy. We are actively supporting the development of smart grids and prioritising energy efficiency, both of which are playing a major role in shaping the market. With the country’s rapid economic development and urbanisation, electricity demand is surging across the residential, commercial, and industrial sectors. To meet this growing need, we are upgrading the power sector by expanding transmission and distribution networks, which involves deploying a significant number of new transformers. The target of 500 GW by 2030 is driving the need to integrate large volumes of solar, wind and other green energy sources into the grid. This integration of renewable energy sources requires specialised transformers that can handle the variable power output and maintain grid stability.

How can N-UNMS improve transformer monitoring and fault detection in transmission and distribution networks?

With the National Unified Network Management System (N-UNMS), we can increase transformer monitoring and fault detection across the transmission and distribution network. By enabling centralised monitoring, advanced analytics, and faster fault response, we will improve reliability, reduce downtime, and optimise operational efficiency. Through N-UNMS, we will be able to collect real-time data from various sensors and monitoring devices installed on transformers throughout the network. This includes critical parameters such as voltage, current, oil levels, and dissolved gases, all accessible via a centralised platform that provides us with continuous insights into transformer health. The system provides a comprehensive view of each transformer’s status, enabling us to detect anomalies early and identify potential issues before they escalate into major faults. By analysing the collected data, we can spot subtle behavioural changes that might signal an impending failure. This will enable us to take proactive maintenance actions such as scheduling timely repairs or replacing components, minimising downtime and avoiding catastrophic failures.