Rudrendu Kumar Paul is a revered name in AI and data science. Paul’s contributions through the use of Data Science techniques in India’s Renewable Energy and national power grid integration systems are considered to be one of the earlier and more influential implementations at such a large scale.

An electrical engineer by training, Rudrendu has been instrumental in integrating AI, data science, and analytics into the operation and maintenance of high voltage power transmission systems, operations, and maintenance of substations, transforming the way India manages its power infrastructure. His work has impacted the lives of billions of people across India by maintaining a smooth flow of power from renewable energy sources such as the hydropower generating stations in the remote Himalayas to busy cities across India.

Enhanced Operations and Maintenance with Data Science

High-voltage power grid systems such as 400, 220, and 132 KV (kilo volts) gas-insulated switchgear substations generate vast amounts of data. This data was primarily used for condition monitoring of high voltage assets and was traditionally a challenge to manage given the manual processes involved in monitoring the vast amounts of data. However, Rudrendu’s innovative approach to data storage, application of advanced analytics, and data science techniques have streamlined this process.

For high-voltage power transformers, Rudrendu developed systems to efficiently store data related to internal coil temperature, oil test results, bushing IR, tan delta, and partial discharge (PD). By cleaning and processing this data using advanced data engineering pipelines, Rudrendu’s team could apply analytics and data science techniques to predict potential insulation failures ahead of time. This proactive approach not only minimized system downtime but also prevented equipment failure.

Similarly, Rudrendu’s innovations have enabled precise monitoring of the most important component of the power grid and equipment protection system: high-voltage circuit breakers. He developed data science techniques to detect anomalies from circuit breakers’ Dynamic Contact Resistance Measurement Test (DCRM) results. These data-driven insights were then used to predict potential failures ahead of time and monitor the condition of circuit breaker insulation and contacts.

Improved Inventory Management with Data Science

Inventory management is key for smooth operations and maintenance of power transmission systems, especially for high-voltage power substations. Rudrendu applied data science techniques across multiple stores across the entire state. He provided insights into inventory and store handling by integrating data from internal operational usage and ERP systems. Patterns in the use of spares and connectors were utilized to develop data science models. These models enhanced the understanding of store utilization, and maintenance patterns and led to optimal use of store materials, timely procurement of spares, and identification of obsolete materials. This data-driven approach significantly reduced maintenance costs and equipment failures.

By developing predictive stock outage models, Rudrendu ensured that every store could effectively manage their stock availability and spare demands for operations and maintenance use of the transmission systems. Over time, the real-time insights generated by these data science models became increasingly accurate, further enhancing the efficiency of the power transmission system.

Paul’s pioneering work in adopting AI and data science to ensure a smooth flow of power from India’s renewable energy sources and India’s national power grid integration system continues to impact the lives of billions of people by ensuring the uninterrupted flow of renewable hydropower from the remote corners of the Himalayas to the major cities across India. His innovations have optimized operations and maintenance of high-voltage power system equipment, redefined store inventory management systems, and inspired a large cluster of data scientists and engineers in the country and beyond.