Agentic AI and Asset Performance Management Software: The PowerGrids AI Case Study

June 25, 2025
By PowerGrids AI Team
16 min read

Discover how agentic AI transforms transformer maintenance, preventing costly failures. Learn about PowerGrids AI's autonomous, data-driven approach to grid reliability.

Agentic AI and Asset Performance Management Software for Power Transformer Condition-Based Maintenance: A Utility Case Study

Figure: A field technician inspects a high-voltage power transformer; data from such on-site checks and sensors feeds into AI-driven Asset Performance Management (APM) platforms to enable proactive, condition-based maintenance.

Power transformers are mission-critical grid assets whose failures can cause extensive outages and economic losses. Traditionally, transformer upkeep has followed time-based schedules (e.g. fixed-interval oil tests or overhauls), but such approaches often miss incipient faults and lead to unnecessary work on healthy units.

In response, utilities worldwide are shifting to condition-based maintenance (CBM): monitoring equipment health and intervening “as needed” rather than by the calendar. This article explores how a next-generation approach — Agentic AI — can supercharge transformer CBM via smart APM software, yielding major reliability, economic, and even carbon-reduction benefits for electric utilities. It combines current industry practice with visionary case studies and standards, targeting utility executives and engineers.

From Time-Based to Condition-Based Maintenance

In the past, utilities often overhauled transformers on rigid schedules (e.g. every 5–10 years) regardless of condition. While time-based programs address age-related wear, they miss unpredictable failures from latent defects or unusual stresses. For example, many transformer incidents trace back to component failures (like bushings or on-load tap changers) that evolve between inspections. In contrast, Condition-Based Maintenance (CBM) uses actual asset data to decide when and what to service. CBM relies on continuous or periodic monitoring of key indicators — dissolved gas analysis (DGA) in the oil, partial discharge (PD) activity, winding and oil temperatures, moisture, load cycles, etc. — to predict faults and extend life. In essence, CBM aims to keep healthy transformers running and fix only those showing distress, thereby preventing expensive outages and optimizing the maintenance budget. Studies show that targeted monitoring can detect problems long before catastrophic failure: for example, rising DGA gases or PD pulses often signal incipient faults weeks or months earlier. According to CIGRE, most substation transformer outages stem from specific component faults that these methods can catch early.

Key differences:

  • Time-Based (Traditional): Maintenance on fixed intervals, often leading to unnecessary downtime for healthy units and missed warnings during intervals.

  • Condition-Based: Maintenance triggered by actual condition trends. Sensors (online DGA, PD detectors, temperature monitors, smart bushings, etc.) feed data to analytic tools so maintenance is done “as needed”.
    This risk-informed approach aligns with ISO 55000 asset-management principles and has been advocated by industry bodies (CIGRE TB 761) to gradually replace purely time-driven regimes. In practice, an effective CBM strategy can reduce failure rates significantly: modern fleets with robust monitoring now see annual failure probabilities around 0.1–0.2% (versus historical rates up to 1–2%).

Advanced Monitoring Technologies

CBM relies on sophisticated diagnostics. Dissolved Gas Analysis (DGA) — often called the “blood test” for transformers — is widely used: an oil sample is analyzed for key gases (H₂, CH₄, C₂H₂, etc.) that indicate overheating or arcing. Industry experts note DGA is “the most reliable and accurate method for determining the internal health” of oil-filled transformers. Similarly, Partial Discharge (PD) monitoring (via UHF sensors, acoustic, or capacitive probes) detects insulation stress before breakdown. Fiber-optic temperature sensors and thermal imaging identify hot-spots, while smart tap-changer monitors track mechanical wear. All these data can be integrated into APM platforms. For instance, a modern APM software suite ingests nameplate data, historical tests, online sensor streams and GIS/SCADA data to build a composite health index for each transformer. Such health indices have been developed by leading labs (Kinectrics, Hydro-Québec, etc.) to quantify condition by fusing DGA, moisture, load, and other factors.

Asset Performance Management (APM) software thus plays a crucial role: it aggregates transformer data (nameplate specs, oil-test results, maintenance history) and applies algorithms to flag deterioration. When enhanced by AI, the system can continuously update health scores and risk metrics for each unit, something that used to require engineers’ manual effort. An AI-driven APM can automatically correlate a sudden gas rise with temperature and load history, for example, revealing emerging hot spots that a simple threshold rule might miss.

What is Agentic AI?

“Agentic AI” is a term for autonomous, goal-directed AI agents that can perceive their environment and act without constant human oversight. Unlike conventional analytics (which might only generate reports or alerts that humans must interpret), an agentic system can diagnose conditions and even initiate actions. In this context, an agent could watch all data for a transformer (SCADA, DGA, PD, weather, grid load) and decide on the best next step. Recent generative and reinforcement-learning advances have demonstrated AI agents in areas like smart grids and manufacturing, and the concept is now moving into maintenance. For example, an agent could notice a trending increase in hydrogen in a transformer’s oil, correlate it with peak load events and elevated ambient temperature, and then autonomously recommend or schedule a cooling adjustment or inspection. In practice, we envision utility planners deploying a “digital twin agent” for each critical transformer: this AI agent continuously learns from that transformer’s data and coordinates with system-wide goals (e.g. reliability targets).

Integrating Agentic AI into CBM

Agentic AI for transformer maintenance means tightly integrating AI agents with existing utility systems: SCADA for real-time telemetry, GIS for location/context, and APM/CMMS (Computerized Maintenance Management) systems for asset data and work orders. The AI platform interfaces with all these. For example, real-time SCADA feeds temperatures and currents; on-line DGA instruments feed gas levels; the AI cross-references these with nameplate limits and historical trends. When an agent detects a worrying pattern (such as an accelerating gas trend or unexplained PD bursts), it can autonomously raise an alert or even trigger a workflow. This can include automatically logging a condition report in the maintenance system or dispatching an inspection crew. By continuously updating each transformer’s health index using multi-sensor fusion, an agentic APM system transforms condition monitoring into proactive risk management.

The integration pays off: PowerGrids AI and industry whitepapers note that agentic platforms can reduce transformer failures and outages by enabling true predictive maintenance. In effect, the AI augments engineers’ expertise with tireless data analysis. This vision builds on existing IEEE/IEC best practices (such as the IEC 60599 DGA guide and IEEE C57.143 monitoring guide) by embedding them in software that acts continuously.

Utility Use Cases and Case Studies

Case Study – U.S. Northeast Utility (500 MVA Autotransformer): A major investor-owned utility pilot deployed online DGA monitors and fiber-optic temperature sensors on a 345 kV, 500 MVA transformer, then connected the data to an AI platform. Over six months, while absolute gas levels remained safe, the AI agent noticed the rate of ethylene risesharply increasing during high-load, high-temperature days. It calculated a health-index drop (following IEC 60599 logic) and alerted engineers. Acting on the AI’s recommendation, the utility scheduled a maintenance outage. Inside the transformer they found a partially blocked oil duct causing a hot spot; a piece of degraded pressboard was fixed and insulation replaced. Because the AI caught this early, the unit was restored with only ~$200K in repairs. Without the intervention, the transformer likely would have burned (a failure replacing it might cost ~$4M plus collateral damages). In this case the monitoring and AI pilot cost ~$50K, and the avoided loss was ~$5M, yielding a ≈20:1 benefit–cost ratio. This incident convinced the utility to roll out AI monitoring fleet-wide.

Case Study – European TSO (400 kV Transformer): A Southern European transmission operator faced an aging 250 MVA, 40-year-old transformer that annual tests flagged for high moisture and furan (paper aging). Rather than replace it immediately, they implemented an AI-based cooling control. The AI continuously modeled the transformer’s thermal behavior (per IEC 60076‑7) using load and temperature data. It discovered that under existing settings, the transformer’s hottest spot was reaching ~105 °C on summer peaks. The AI recommended pre-cooling during peak hours and adding a new fan bank. The utility enabled the AI to autonomously start fans based on predicted hotspot temperature (rather than just oil temp). As a result, the hottest spot dropped ~10 °C on peak days. The aging rate slowed (confirmed by reduced gas generation), and the AI estimated remaining life extended from ~2 years to 5+ years under the new regime. Consequently, the utility deferred the transformer’s replacement by ~5 years, saving millions in capital while maintaining grid reliability.

Case Study – Gulf Utilities: In the harsh Gulf region, high ambient heat accelerates transformer aging. Utilities there have aggressively modernized with digital monitoring. For example, Saudi Electricity Company (SEC) reported a 50% reduction in 380 kV transformer failures between 2010 and 2020 after installing continuous online DGA and analytics. In one year, DGA alarm alerts reportedly prevented at least five major failures, saving on the order of SAR 50 million (~US$13 million) in equipment damage and avoided outages. Saudi Aramco’s power division has gone further: they implemented “smart transformer” fleets with DGA, PD, bushing, tap-changer, and dynamic cooling controls. By integrating this data under one AI-capable platform, Aramco extended the lives of some older transformers by ~5 years beyond their planned retirement, because AI-confirmed health scores showed no need for replacement. Simultaneously, they identified a few units with hidden bushing issues (exacerbated by desert heat) and replaced them early. Such GCC examples show that even in extreme climates, CBM backed by AI can massively improve uptime and defer new purchases.

Taken together, these case studies illustrate that agentic AI systems can turn sparse alarms into smart action. By considering multi-parameter trends, AI caught problems that simpler alarms would miss, and it enabled nuanced responses (load redistribution, pre-cooling, targeted repairs). The net result for utilities adopting AI-enhanced CBM is substantially higher reliability and safety, with quantifiable ROI.

Comparison: Agentic AI vs Traditional CBM

Aspect

Traditional CBM (No AI)

Agentic AI-enabled CBM

Data Usage

Occasional oil or electrical tests; manual reviews

Continuous multi-sensor integration (SCADA, DGA, PD, GIS, etc.)

Analytics

Threshold checks; annual/periodic health indices

Real-time analytics and machine learning models; health index auto-updated

Decision Speed

Days to weeks (manual planning)

Seconds to minutes (autonomous alerts and interventions)

Maintenance Action

On fixed schedule or after single alarms

Adapted to evolving condition; can defer or advance work as needed

Resource Efficiency

Potentially over-servicing good assets

Prioritizes truly at-risk assets, deferring routine for healthy ones

Standards Alignment

Often guided by tests (e.g. DGA gas limits IEC 60599)

Builds on IEC/IEEE/CIGRE frameworks but can exceed them by automating best practices

Outcome

Risk of unexpected failures or unnecessary outages

Lower failure rates and optimized life-cycle costs; evidence of 0.1–0.2% annual failure rates in well-managed fleets

In summary, utilities still using conventional CBM may catch many issues, but AI-driven CBM greatly enhances predictive power and efficiency. The agentic approach reduces human lag time, eliminates siloed data gaps, and aligns maintenance with actual risk.

Benefits and ROI for Utilities

Agentic AI-based CBM offers multiple tangible benefits for utilities:

  • Improved Reliability: By catching faults early, AI reduces unplanned outages. For example, National Grid (UK) saw ~25% fewer unplanned transformer outages by combining online DGA with analytics. The recent CIGRE TB 939 (2024) reports that global transmission transformer failure rates have fallen to ~0.1–0.2% annually, largely due to better monitoring and maintenance. Agentic AI can push this even lower by automating anomaly detection across the fleet.

  • Economic ROI: The case studies above show dramatic ROI. In one North American pilot, a $250K investment in sensors and AI averted a $5M loss (≈20:1 ROI). In Saudi Arabia, condition-monitoring programs with AI analytics saved tens of millions in equipment and outage costs each year. Even aside from avoided failures, AI helps defer capital replacement: extending a transformer's life by a few years means postponing a multi-million-dollar purchase.

  • Optimized Maintenance Spending: AI agents prioritize maintenance on the units that need it most. This means utilities spend O&M budgets more efficiently: assets in good shape get lighter-touch care, while ones at higher risk receive timely intervention. Over time this reduces total maintenance cost. Research also shows that using CBM instead of time-based care can cut maintenance costs by 10–30% in transformer fleets, not counting the savings from fewer failures.

  • Regulatory and Standards Alignment: Adopting AI-supported CBM helps utilities meet regulatory expectations. For instance, NERC encourages robust asset management under its reliability standards, and demonstrating proactive maintenance (even via AI) can support compliance. In the EU/UK, regulators (like Ofgem in the UK) incentivize reliability: proactively reducing outages improves regulatory scores and allowed returns. Moreover, standards like IEC 60076‑18 (monitoring systems) are emerging; using agentic AI platforms is a forward-leaning way to satisfy such frameworks. Finally, ISO 55000 asset-management principles emphasize risk-based decision-making, which AI-driven CBM embodies.

  • Environmental/Carbon Impact: Extending transformer life and avoiding replacements have carbon benefits. Manufacturing a large transformer involves substantial embodied CO₂ (from copper, steel, varnish, transport). On-site refurbishment reuses 60–90% of components and drastically cuts lifecycle emissions. For example, Hitachi Energy notes that refurbishing an active part on-site (versus replacing) yields about 1.7% lower lifetime carbon emissions. AI-enabled CBM that keeps older transformers safe means fewer new builds and less material waste. Additionally, preventing catastrophic failures avoids oil spills and fires that have environmental costs. In sum, by optimizing asset use in-place, AI CBM aligns with utilities’ carbon-reduction goals.

  • Operational Efficiency: AI can even optimize loading and cooling to save energy losses. In our European case, the AI-controlled cooling reduced hotspot temperature, slowing insulation aging. In grids dominated by fossil back-up, avoiding outages means less emergency generation (often less efficient and higher-emitting). Agentic AI can also help integrate renewables more smoothly by improving grid visibility and resilience.

In short, agentic AI for CBM offers both bottom-line savings and sustainability upsides. The investment in sensors and AI often pays back many times in avoided capex and opex. Utilities making this shift can benchmark themselves at the industry forefront of reliability. As one analysis concludes, although agentic AI CBM requires upfront investment and new skills, the long-term gains in reliability, asset utilization, and cost efficiency “justify it many times over”.

Regulatory and Standards Context

Utility assets are governed by many standards. IEC 60076 (power transformer series) and IEEE C57 guides define testing and DGA interpretation. CIGRE technical brochures (e.g. TB 630 on monitoring, TB 761 on condition assessment) outline CBM best practices. Our agentic AI approach builds on these: for instance, the AI’s diagnostic logic can follow IEC 60599/IEEE C57.104 gas-analysis criteria, but it enhances them by detecting complex patterns over time. Likewise, IEEE is working on formal condition-assessment guides (e.g. PC57.170 draft) to standardize health indices.

From an asset-management perspective, ISO 55000/55001 encourages risk-based strategies. Agentic AI–supported CBM embodies that by quantifying risk via health scores and automating risk mitigation actions. NERC’s reliability reports emphasize that equipment failures (including transformers) still contribute to disturbances, so advancing CBM is consistent with their emphasis on resilient grids. Notably, CIGRE TB939’s finding of reduced failure rates underlines that proactive maintenance works, and regulators view such performance improvements favorably.

Utilities in regulated markets may have additional incentives: some regulators allow accelerated cost recovery if a utility demonstrates improved asset reliability or reduced environmental risk. In markets like the UK, proactive maintenance that avoids long outages can even lead to incentive payments. Therefore, embracing agentic AI CBM can help meet existing rules (e.g. NERC TPL for transmission planning, requiring low risk of N–1 failures) and prepare for future digital-grid standards (IEC’s ongoing work on transformer monitoring systems).

Frequently Asked Questions (FAQ)

  • What is condition-based maintenance (CBM)? CBM means servicing a transformer based on its actual health data, not just on a calendar. It uses sensor readings (oil-gas levels, temperature, PD, etc.) to detect early signs of trouble. The goal is to maintain the asset as needed to prevent failures and optimize life.

  • What is “agentic AI” in this context? Here, agentic AI refers to autonomous AI agents that can monitor transformer data and make decisions (or recommendations) on maintenance actions without waiting for a human. In other words, it’s an AI that “acts” like an engineer: it perceives conditions, diagnoses issues, and triggers responses on its own. This goes beyond simple alerts by computers and enables real-time, adaptive maintenance.

  • How does Agentic AI improve transformer maintenance? By integrating with Asset Performance Management (APM) software and real-time data, agentic AI provides continuous diagnostics. For example, instead of just flagging a single high gas reading, the AI observes trends across many parameters. It can detect subtle fault precursors and even predict which component will fail. It can autonomously update each transformer’s health index and recommend (or even initiate) optimal maintenance. This leads to catching faults earlier and planning interventions with precision.

  • What benefits do utilities see? Major benefits include higher reliability, cost savings, and carbon reduction. We’ve seen examples where AI-driven CBM avoided multi-million-dollar failures, yielding payback ratios like 20:1. Utilities report large decreases in outage rates (e.g. –25% transformer outages or –50% failure rates) when moving to AI-enabled CBM. They can extend asset life (postpone a $10M replacement by several years, as in the European example) and cut lifecycle emissions by reusing existing transformers. Overall, agentic AI turns maintenance from a cost center into a strategic reliability asset.

  • Is this approach compatible with industry standards? Absolutely. Agentic AI systems are designed to comply with IEC/IEEE/CIGRE guidelines. For instance, they use IEC 60076 series rules for loading and IEC 60599 thresholds, and they implement CIGRE-recommended health-index frameworks. Moreover, this approach aligns with ISO 55000’s risk-based asset management principles. Utilities can thus adopt agentic AI within existing regulatory frameworks (NERC guidelines, EU reliability standards, etc.) and even use it to demonstrate enhanced compliance and performance.

  • What are practical steps to adopt this technology? Utilities should start by ensuring good data: upgrade key transformers with online monitors (DGA, PD, temperature, etc.) and integrate those feeds into their APM/SCADA systems. Then, deploying an AI-capable APM platform (many vendors offer such modules) will enable the agentic capabilities. It also involves training staff to trust AI alerts and adjust work processes accordingly. Pilot projects(as many have done) on a subset of critical transformers are recommended to fine-tune the system and quantify ROI before scaling up.

Conclusion

Agentic AI represents the next evolution of transformer maintenance: moving from static schedules to self-driving, data-informed upkeep. By fusing continuous monitoring with autonomous decision-making, utilities can achieve the promise of condition-based maintenance at full scale. The result is a future grid where transformers are watched 24/7 by intelligent agents – improving reliability, cutting costs, and supporting decarbonization objectives. As leading utilities and consortia are demonstrating, this approach is technically feasible and economically compelling. Executives and engineers should view agentic AI not as a futuristic dream, but as an emerging standard: the CIGRE and IEC guidance already points in this direction, and the successful case studies show it works. In the race to modernize the grid, agentic AI-powered CBM offers a clear pathway to smarter, greener, and more resilient transformer management.

Sources: Insights and data drawn from industry whitepapers, IEEE/IEC/CIGRÉ publications, utility reports, and technical magazines. Each citation corresponds to an open technical resource or report supporting the statements above.

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