Thermal power generation has been used for over a hundred years. During this period, the technology has improved in several areas, including efficiency, reliability and safety. Even so, thermal power plants continue to face operational issues, particularly as India's demand for electricity increases and the power sector goes through changes. Artificial Intelligence (AI) could help address some of these problems. According to Ratul Puri, there is no need to reinvent an established technology. Instead, AI should be used to deal with the problems that have a direct impact on plant operations, expenses and reliability.
Equipment breakdowns and forced outages are among the issues that thermal power generators need to manage. An unexpected shutdown can affect electricity generation, and the power shortages experienced across several states in recent months have shown why preventing such disruptions matters. AI-based predictive maintenance can help by giving operators an early indication of possible equipment failures. Drones and computer vision can identify defects and thermal hotspots, while AI analytics can help detect signs of deterioration. Past maintenance records and equipment performance data available in systems such as SAP can also be studied to find problems that occur repeatedly. This information can help plant teams decide when and where maintenance is required.
"AI must deliver practical solutions to the challenges that power plants face every day. Preventing equipment failures and improving plant reliability should be among its most important applications," says Ratul Puri.
However, using AI for maintenance is not simply a matter of installing new technology. Plants must also collect data regularly and maintain accurate records. Long-term monitoring is equally important. If the available information is not complete or reliable, AI systems may not produce useful results. With proper data support, plants can gradually move from breakdown-based maintenance to Reliability-Centred Maintenance. This approach can help limit unplanned outages and improve the performance of equipment.
Coal management is another issue that affects the cost and efficiency of thermal power generation. Around 60% of a thermal power plant's operating costs are linked to coal. This makes coal quality and transportation losses important concerns. For example, a difference between the declared and actual Gross Calorific Value (GCV) can affect the plant's performance. Losses during transportation can create further financial difficulties. AI-based surveillance and drone monitoring can help track coal movement by road and rail, while data analytics can highlight possible irregularities. Robotic sampling at both loading and delivery points can make coal quality checks more consistent and accurate. It also reduces the need for manual intervention.
"Technology can bring greater transparency and accountability to coal management. Combining AI-based monitoring with automated sampling can help address long-standing concerns around coal quality and transit losses," Ratul Puri notes.
There is also a growing need for thermal power plants to adjust their generation according to changing grid requirements. As renewable energy capacity expands, thermal plants are expected to change their output more frequently to maintain grid stability. Some plants may have to operate at minimum technical loads of 55% or even lower. Such operating conditions are not easy to manage. Repeated increases and reductions in generation put additional pressure on equipment, cause more wear and tear and can affect efficiency.
This is another area where AI can be useful. Better forecasts of electricity demand and renewable energy generation can help operators decide how much power to generate and when to make changes in plant output. AI can support generation scheduling and help plants plan ramp-ups and ramp-downs more effectively. This allows operators to consider both grid requirements and the condition of their equipment, including its long-term operating life.
"The way forward is to use AI where it creates measurable value. Better forecasting, smarter maintenance and improved operational decisions can help thermal power plants remain reliable and competitive as India's energy mix evolves," says Ratul Puri.
AI offers thermal power plants a way to address existing operational difficulties rather than replace the technology they already use. Its success will depend on the quality of data, how well AI systems work with existing infrastructure and the cooperation between technology providers and plant operators. Giving attention to equipment failures, coal management and changing generation requirements can help the sector improve its operations. These applications can also support India's efforts to maintain an efficient, reliable and sustainable power supply.
About Ratul Puri
Ratul Puri is the Chairman of Hindustan Power, an integrated power generation company with a strong presence in renewable and transitional energy generation. Over the years, Ratul Puri has been involved in the development of large-scale energy infrastructure projects that support India’s growing power requirements and its transition toward cleaner energy sources.
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