For decades, the power industry operated on a relatively predictable cycle of asset management. Transformers were overbuilt, loads were stable, and maintenance was largely dictated by the calendar. However, as the global energy landscape undergoes a seismic shift, the “set it and forget it” mentality is no longer viable. Today, the industry faces a perfect storm: an aging infrastructure, a massive surge in demand driven by electrification and data centers, and a supply chain that has pushed lead times for new units into the next decade.

In a recent panel discussion hosted by Alan Ross, Managing Editor of APC Media, industry experts from Qualitrol, Hitachi Energy, and Advanced Power Technologies (APT) gathered to discuss the current state of transformer monitoring and the technological advancements aimed at keeping the lights on. The consensus was clear: monitoring has evolved from a secondary accessory to a critical operational necessity.

The Capacity Crisis and the Strategic Shift

The transformer market is currently gripped by what Tucker Reed, Digital Offerings Manager for North America at Hitachi Energy, describes as a “super cycle”. With factories worldwide running at capacity, the luxury of simply replacing a failing unit has vanished. Emilio Morales, a veteran with over four decades in transformer engineering and current expert at Qualitrol, highlights the gravity of the situation. According to Morales, “The demand is so high that lead times in some places have gone to five or even seven years lead time”.

Today, the industry faces a perfect storm: an aging infrastructure, a massive surge in demand driven by electrification and data center, and a supply chain that has pushed lead times for new units into the next decade.

This scarcity has forced utilities to move away from traditional time-based maintenance in favor of strategies focused on extending asset life and managing risk. As the fleet ages, it is also being subjected to less predictable, more dynamic loads from renewables and grid congestion. This shift in strategy necessitates a deeper understanding of an asset’s real-time health. Morales notes that for many utilities, this “changes the strategy from replace to extend and manage the risk of the existing fleet”.

Beyond Data: The Quest for Actionable Intelligence

While the industry has successfully deployed thousands of sensors across the grid, the sheer volume of information has created a new challenge: data fatigue. The experts argue that simply having data is not enough; it must be “actionable.”

Trent Williams of APT emphasizes that the industry often suffers from an “analysis paralysis” where monitoring systems scream with alarms that go unheeded. “Actionable data is what we really care about at this point” Williams explains. He suggests that a successful monitoring program must be grounded in a commitment to act. In a piece of advice, he admits, somewhat controversial, Williams tells young engineers, “You should only implement a monitoring system that you plan to use the data from or take action upon”.

With factories worldwide running at capacity, the luxury of simply replacing a failing unit has vanished.

Reed builds on this, introducing the concept of making an asset “data-ready.” In his view, monitoring is the preparation of the transformer so that it can be plugged into diagnostic tools that provide immediate value. This is particularly crucial as the industry faces a massive loss of “tribal knowledge” with the retirement of senior engineers. “The question becomes: how do we embed that knowledge into these new resources?” Reed asks, suggesting that automated monitoring systems can help bridge this expertise gap.

The Rise od the Digital Twin and AI

The next frontier in transformer management lies in the integration of condition data with design specifications and system simulations, a concept known as the Digital Twin. This goes beyond simple sensor readings to create a comprehensive virtual model of the asset.

Reed describes the Digital Twin as “an aggregation of not only condition understanding of the information, but design understanding of the asset, and then also simulation of that asset inside of the system it’s sitting in”. By combining these three elements, utilities can predict how an asset will behave under various environmental conditions or load shifts before they occur.

While Artificial Intelligence (AI) is often discussed as a “silver bullet” for these complex models, the panelists remain cautious. They agree that while AI and Large Language Models can consume vast data sets to provide context, human expertise remains vital for the foreseeable future. “We still have to have the expertise in place to educate the system to develop the algorithms,” Williams notes.

Practical Specifying: A Bottom-Up Approach

For engineers tasked with specifying monitoring for new or retrofitted transformers, the panel recommends a tiered approach based on budget and criticality. Even on a limited budget, certain sensors are deemed essential.

Reed suggests that the bare minimum for any unit should include an Intelligent Temperature Monitor (ITM) and a hydrogen sensor.

Hydrogen acts as the “check engine light” of the transformer, providing the first indication of a developing fault. “At the very bare minimum, lowest budget possible, I would recommend on all transformers that come out of any factory to have a minimum of an intelligent temperature monitor and at least a hydrogen sensor”.

Morales adds that moisture monitoring is the logical next step, as it allows operators to infer the risk of bubble formation during overloading.

The industry suffers from an “abnalysis paralysis” where monitoring systems scream with alarms that go unheeded.

However, the most sophisticated sensors, such as those for Partial Discharge (PD), offer the greatest “speed” in detecting incipient faults. Morales cites a case where PD monitoring saved a utility an estimated $70 million by identifying issues in real-time that other sensors missed.

The Longevity Gap

One sobering reality discussed by the panel is the discrepancy between the life of the transformer and the life of the monitoring hardware. While a well-maintained transformer might last 40 to 80 years, the electronic monitors typically have a lifespan of 10 to 15 years. “The issue has been more updates than failures,” Morales observes, noting that hardware often becomes obsolete before it physically breaks. This necessitates a long-term plan for hardware refreshes and software updates throughout the transformer’s life cycle.

Conclusion: Collaboration and the Path Forward

As the grid becomes more complex, the siloed approach to data management is beginning to crumble. The experts believe that for monitoring to reach its full potential, there must be greater collaboration across the industry, potentially facilitated by organizations like the IEEE or CIGRE.

The future of power reliability depends on turning transformers into “intelligent” assets. By focusing on reliable sensors, data aggregation, and actionable analytics, the industry can navigate the current capacity crisis. As Morales summarizes, the ultimate goal of these technological advancements is simple yet profound: “We create tools to help keep the lights on”.

Watch the full Power Panel on our website.