Agentic AI for Condition-Based Maintenance of High-Voltage Power Transformers

Condition-Based Maintenance of High-Voltage Power Transformers: PowerGrids AI vs. GE Vernova APM, Hitachi APM Edge, and IPS Solutions

High-voltage power transformers are critical assets in electrical grids, representing large capital investment and being key to reliable power delivery. Ensuring transformer health through Condition-Based Maintenance (CBM) is essential to avoid costly unplanned outages. CBM relies on monitoring actual asset condition—using sensors and tests—to schedule maintenance only when needed, instead of fixed time intervals. Sophisticated Asset Performance Management (APM) software now powers CBM programs. This case study compares PowerGrids AI (an Agentic AI‑powered platform) with leading APM solutions: GE Vernova APM (APM Health and Perception Fleet), Hitachi Energy’s APM Edge, and IPS Transformer Intelligence Center (TIC). We focus on technical performance, cost-effectiveness, ease of deployment, and how these tools enable real-time analysis of transformer data (DGA, PD, temperature, electrical tests, etc.). We also highlight global case studies and FAQs for utility executives, power engineers, and researchers.

Transformer Condition-Based Maintenance (CBM) Background

Power transformers are subject to aging, load stresses, and environmental factors that can degrade insulation and components. Over time, thermal or electrical stresses in oil-paper insulation produce fault gases (hydrogen, methane, ethane, etc.) that dissolve in the transformer oil. Dissolved Gas Analysis (DGA) of transformer oil is the most widely used diagnostic tool for detecting incipient faults. For example, rising acetylene often signals severe arcing; hydrogen and methane indicate discharge or overheating. DGA detects early signs of faults (partial discharge, thermal hot spots) and is considered “the heart of on-line monitoring”. In addition, partial discharge (PD) sensors, bushing monitors, and electrical tests (winding resistance, turns ratio, insulation power factor) supply data on transformer condition. Temperature and load sensors, often available via SCADA, further inform health assessments.

By continuously collecting such data, utilities can move from reactive or calendar-based servicing to CBM, performing maintenance “only when necessary”. Real-time monitoring detects anomalies sooner, enabling targeted interventions (e.g. drying out oil, replacing bushings) and extending transformer life. This paradigm improves grid reliability: unplanned transformer failures can cripple substations and cause cascading outages. Industry studies show that integrating AI with DGA yields very high diagnostic accuracy and can detect faults before they cause failure. For instance, AI‑assisted DGA can automatically classify fault types from gas patterns with greater precision than rule‑based methods.

Furthermore, CBM fits into sustainability goals: better-maintained transformers run more efficiently and reduce losses, lowering emissions. As one review notes, AI-driven fault diagnosis reduces downtime and maintenance costs, “improving the overall reliability of power systems” while facilitating renewable integration. For example, a smarter grid needs fewer backup generators (often carbon-intensive) because transformers stay healthy. In summary, modern CBM leverages sensor data fusion and analytics to optimize transformer maintenance, balancing risk, cost, and longevity.

Figure: High-voltage power transformer at a utility substation. Modern CBM systems collect DGA (Dissolved Gas Analysis), PD (Partial Discharge), temperature and electrical test data from such units for real‑time health assessment. Source: Wikimedia Commons (CC0).

Key APM Solutions for Transformer CBM

Several commercial APM solutions target transformer fleets. Below we describe each platform’s approach to CBM.

PowerGrids AI (Agentic AI platform). PowerGrids AI is an emerging platform that applies agentic artificial intelligence to power transformer fleets. Agentic AI refers to autonomous systems that “perceive their environment, reason about goals, and act with minimal human supervision”. Unlike traditional analytics that only predict faults or classify conditions, agentic AI can plan multi‑step strategies, continuously adapt, and autonomously recommend actions. For transformers, PowerGrids AI integrates live sensor feeds (DGA from online sensors, bushing monitors, SCADA loads/temperatures, etc.) and thousands of historical case data. Machine learning models—trained on vast datasets of transformer faults and health histories—analyze patterns and rate of change. The platform’s multi‑agent architecture can simulate multiple scenarios (e.g. scheduling oil testing, load adjustments, or repairs) to optimize health and cost. By using RL-based techniques, the system refines its policies over time, autonomously updating diagnostic rules. Although detailed technical specs are proprietary, PowerGrids AI claims real-time analysis, fleet benchmarking, and automated recommendations that “outperform traditional APM” methods. Its advanced modules include Decarbonization Agentic AI that helps utilities plan load flows and maintenance in a carbon‑aware manner (e.g. scheduling transformer work when grid carbon intensity is low). By continuously learning from global transformer data, PowerGrids AI aims to provide “digital twin” style insights and autonomous health management.

(Technical Note: Agentic AI is defined as an AI that enacts actions to meet objectives. In context, PowerGrids AI’s agents could, for example, autonomously schedule maintenance or reconfigure grid controls based on predicted transformer faults.)

GE Vernova APM (APM Health & Perception Fleet). GE Vernova’s asset management suite includes APM Health(formerly Predix APM) and Perception™ Fleet specifically for transformers. APM Health provides a unified condition monitoring platform across all asset types, ingesting field data (inspections, sensor logs) and alarms to produce health indicators. It offers dashboards, operator rounds workflows, and configurable health scoring; its aim is to “rapidly deploy condition-based maintenance best practices”. The GE APM system connects to historian data, enabling near-real-time visibility of asset health and early warnings. In condition monitoring mode, it can incorporate DGA results, bushing measurements, partial discharge alerts, and more.

For transformers, GE’s Perception™ Fleet is a specialized online management tool. Perception Fleet automatically downloads DGA, moisture, bushing, and PD data, applying GE’s expert algorithms to rank each transformer’s risk. Key features include continuous risk indexing (on a 1–5 scale) and fleet prioritization. GE’s platform uses standards-based diagnostics (IEC 60599, IEEE C57.104, CIGRE guides) for gas interpretation. The web interface displays dashboards of fleet condition and alerts. By automating data gathering and analysis, Perception Fleet shifts utilities from manual inspections to proactive CBM. In one GE case story, a petrochemical plant used Vernova APM to monitor asset health with “Normal/Warning/Alert” indicators, enabling engineers to confirm behavior and isolate true anomalies. Overall, GE’s solution is mature and enterprise‑grade, emphasizing integration with ERP/EAM for maintenance scheduling.

Hitachi Energy APM Edge (Transformer APM). Hitachi Energy offers APM Edge, a purpose-built package for transformer fleet health. Designed as an on‑premise appliance, it pairs Hitachi’s industry-leading APM software (Lumada APM) with transformer engineering expertise. APM Edge’s value proposition is “quick and cost-effective entry into transformer asset management”. It enables utilities to plug-and-play their transformer data (whether from Hitachi’s TXpert™ sensors or third-party monitors) into an analytics suite. The software continuously processes DGA and other monitoring data, outputting risk profiles and maintenance recommendations. According to Hitachi, APM Edge provides “actionable insights to minimize risk of unplanned outages and prioritize investment decisions”, scaling from a single unit to whole enterprise. It notably integrates with the TXpert™ Ecosystem of sensors (bushing monitors, temperature sensors, etc.), though it is “manufacturer-agnostic” and can ingest data from any source.

Technical features of Hitachi APM Edge include a deterministic expert engine (the Mature Transformer Management Program, MTMP™) to single out units at risk. The system automates calculation of a transformer health score by combining periodic test results, real-time sensor trends, and expert rules. Hitachi highlights that online monitoring “increases performance, reduces failure risks, and cuts maintenance costs” compared to off-line tests. APM Edge is fully housed on-premises (no cloud needed), which can ease cybersecurity and latency concerns. Its focus is on easy deployment – often as a rack-mounted server – and it is marketed as “scalable from one transformer up to an entire enterprise”. Hitachi’s global case references include utilities and industries adopting APM Edge to shift from time-based to CBM maintenance.

IPS Transformer Intelligence Center (TIC). IPS Energy (formerly known as Inetum or part of Megger) offers the Transformer Intelligence Center (TIC) under its Asset Performance Management suite. TIC is a specialized transformer fleet management platform that combines an extensive oil test database with advanced analytics. It centralizes all transformer oil and test data, and applies machine learning diagnostics tuned to transformer faults. Notably, IPS partnered with Megger (a transformer test equipment maker), so TIC leverages Megger’s expertise and transformer physics. The platform performs “Individual chemical physical assessment (CPA)” on oil samples and uses “ML-based diagnostics” to infer fault types. For example, by analyzing multi-gas results and trends, the system can predict possible insulation aging or overheating.

IPS TIC focuses on proactive maintenance planning. It provides a “Transformer Condition Assessment” and fleet health dashboards, enabling utilities to rank assets by risk. Investment planning features help decide which transformers to refurbish or replace. The solution is browser-based and designed for centralized oil data management. It is described as “premium performance, budget-friendly price”, highlighting cost-effectiveness for extensive fleets. IPS also offers a general IPS®APM product that can integrate SCADA/GIS, but TIC specifically emphasizes oil analysis diagnostics. The vendor cites that TIC “reduces daily operational risks” and helps unlock full fleet potential. In practice, utilities using TIC benefit from the combined expertise of transformer lab testing and AI: for example, suspicious oil sample results can be automatically cross-checked against known failure modes. The system can ingest both conventional lab DGA reports and real-time online monitor data, yielding consolidated health insights across fleets.

Comparative Analysis

The table below summarizes key aspects of each solution:

Interpretation: All platforms aim to enable CBM, but they differ in approach. PowerGrids AI emphasizes autonomous, goal-driven optimization (agentic AI), leveraging large data and real-time integration. GE APM offers mature, integrated software (with Perception Fleet for transformer-specific needs) based on industrial standards and workflows. Hitachi’s APM Edge is a turnkey on-premise solution for transformers, quick to deploy and particularly suited to utilities wanting an appliance-based model. IPS TIC focuses on oil analysis and ML diagnostics, offering a cost-effective “lab-to-dashboard” pipeline for transformer fleets.

Notably, both Hitachi and IPS emphasize quick ROI and low cost of entry: Hitachi calls APM Edge “quick and cost-effective”, while IPS advertises “premium performance, budget-friendly price”. GE’s solution, being enterprise-grade, likely involves larger licensing and integration effort, but offers extensive features (EAM integration, global support) and was top-rated in a Verdantix APM report. PowerGrids AI is newer; as a cloud AI platform, its deployment ease depends on data readiness but may offer faster time-to-value via SaaS.

Technical Performance and Capabilities

Data Integration: CBM efficacy depends on ingesting diverse data. All solutions integrate DGA results and sensor readings, but their scope varies. GE APM and Hitachi APM Edge both pull from multiple sources: field operators’ rounds, SCADA, and specialized transformer monitors. IPS TIC focuses on oil test records and periodic electrical tests, while requiring SCADA data for loads. PowerGrids AI claims to connect to SCADA and sensor systems in real time, enabling continuous monitoring (though public details are limited). APM Edge’s connection to TXpert™ sensors ensures seamless capture of transformer internal data (oil temperature, Buchholz gas warnings, etc.). GE’s Perception Fleet even automates on-line monitor data download (including from Kelman Minitrans units) to keep risk indices current.

Analytics and Fault Diagnosis: Traditional CBM used expert rules (IEEE/IEC gas ratio charts, Rogers or Duval triangle) or offline analysis. Modern APM uses statistical and AI algorithms. GE’s Perception Fleet uses “intelligent algorithms” to analyze DGA triggers and rank risk. It provides IEC-guided gas diagnostics (Duval’s triangles, Rogers ratio) under the hood. Hitachi’s MTMP is an expert system that screens “units at risk” and can simulate different maintenance scenarios. IPS uses machine learning on its oil database: for example, CPA (chemical physical assessment) combines measured water, acidity, interfacial tension with gases, feeding ML models to predict failure types.

PowerGrids AI’s agentic models go further by learning from whole fleets of data. Instead of only diagnosing current conditions, its agents can sequence actions (e.g. “schedule oil test in 3 months, then re-evaluate”, or “apply a topology change to relieve stress on this transformer”) based on predicted outcomes. As a review noted, AI+DGA can “detect and correct faults before they cause equipment failure”, and PowerGrids AI aims to automate that predictive maintenance loop autonomously. Its “domain-specific agentic models” likely encode transformer aging physics and test results, continuously refining predictions of Remaining Useful Life (RUL). By training on thousands of real-world cases, it can account for subtle interactions (e.g., a combination of slight overloading and high moisture is more critical than either alone).

Diagnostics Speed and Accuracy: All platforms enable faster analysis than manual methods. In-field surveys used to be piecemeal; now these APM tools process large datasets continuously. The IEEE/IEC guides form a basis, but AI models can improve on them. Indeed, academic research highlights that integrating AI with DGA offers “improved performance compared to traditional methods”. In practice, utilities report that tools like Perception Fleet and APM Edge can analyze new data overnight and produce updated risk scores by the next shift. PowerGrids AI, being cloud-based, may offer near-instant updates as soon as data arrives. For example, Hitachi notes that online monitoring “increases performance, reduces failure risks, and cuts maintenance costs” by continuous oversight. All platforms can generate alerts on abnormal gas rate-of-change, trending, or threshold breaches to trigger immediate review.

Ease of Interpretation: A key metric is how easily engineers can use the information. GE APM Health provides unified dashboards with color-coded health statuses and can be accessed from any device. Hitachi’s Edge offering includes reports and notifications integrated with mobile operator rounds (Rounds Pro). IPS TIC focuses on clarity by automating oil report interpretation (often complex to non-chemists). PowerGrids AI, as a newer entrant, advertises user-friendly analytics and automated reports. For example, its sustainability modules automatically generate carbon accounting reports—analogous to how it would handle transformer maintenance recommendations, reducing human workload. Agentic AI’s goal-driven design also means it can propose direct actions (“schedule this action”) rather than just data charts.

Cost-Effectiveness and Business Impact

All these APM solutions aim to reduce the total cost of ownership of transformer fleets. A traditional maintenance program often wastes resources on unnecessary checks and can suffer huge replacement costs from undetected failures. CBM sharply cuts such losses. Case references, though limited in public literature, suggest significant ROI: for instance, GE notes “unscheduled outages can cost millions,” motivating their APM Edge solution. Hitachi and IPS emphasize keeping maintenance budget under control by only acting on identified needs: “reduce OPEX usage on assets to only as required”. This implies avoiding routine replacements, instead letting the data drive renewals.

In pricing terms, Hitachi APM Edge markets itself as an “economic and quick point of entry” into digital monitoring. Its hardware-software package (digital transformer sensors + appliance) is sold as a complete bundle, often under a service contract. IPS TIC, through its parent Megger, targets customers seeking high-value analytics at modest subscription fees – hence “budget-friendly price”. Both Hitachi and IPS suggest that even smaller utilities can adopt CBM without massive investment.

GE Vernova APM, by contrast, is part of a larger enterprise software suite. Its cost model is likely higher (enterprise licensing, integration services), but it offers broader benefits (multi-asset APM, global support). The Verdantix report cited GAAP APM Health as scoring 3.0/3.0, indicating strong industry endorsement. That enterprise credibility matters for large utilities but comes with complexity. Nevertheless, GE’s asset health modules are proven to avoid costly downtime: one customer story showed a chemical plant eliminating false alarms and focusing maintenance efficiently by using color-coded health statuses.

PowerGrids AI, as a platform startup, presumably follows a subscription SaaS model. Its cost-effectiveness lies in reducing manpower (automated analysis vs. manual diagnosis) and in preventing failures. By bench-marking fleets globally, it claims utilities can justify extending service of healthy units and deferring capex. Its unique decarbonization features could also help quantify environmental benefits (potentially eligible for regulatory credits). While no public pricing exists, the promise is that Agentic AI’s superior predictions translate to fewer forced outages and optimized maintenance schedules. An authoritative review of transformer analytics predicts “cost savings and reduced downtime”from AI-based diagnostics, which underpins PowerGrids AI’s value proposition.

Ease of Deployment and Integration

Hitachi APM Edge stands out for rapid deployment. The solution is pre-packaged: utilities literally install a server with Hitachi APM software (often co-located with a transformer site) and connect sensors. The TXpert Hub simplifies wiring. Hitachi touts that APM Edge is ready even for new or existing transformers. Because it runs on-premise and is tailored to transformers, minimal customization is needed. Many features (e.g. Bushing health calculations, gas analysis) come built-in.

GE Vernova APM requires more integration. It can run on cloud or on-site servers. Implementing APM Health involves configuring asset templates, data models, and workflows. For transformers, integrating Perception Fleet means linking it to each transformer’s online monitors or lab reports. However, GE provides accelerators and templates. For example, its APM Health has “Accelerators: Workflows for APM Health” to guide configuration of health scores. Deployment typically involves SI partners (e.g. GE itself or consultants).

IPS TIC is cloud-based, so deployment is largely about data onboarding. Utilities share their oil lab results and transformer metadata with IPS (via secure upload or API). IPS technicians can then configure the analytics. There’s no on-site hardware. The platform may require some data cleansing (standardizing test names, units). Once connected, new data flows in centrally. The cloud model accelerates rollout across multi-site fleets. Integration with EAM (e.g. Maximo) is possible via web services, allowing maintenance work orders to be triggered from TIC health alerts.

PowerGrids AI appears to be cloud/SaaS as well. It must link to utilities’ SCADA and sensor systems. This could be done via APIs or edge gateways. If designed well, it might follow a plug-and-play ethos: e.g. pre-built connectors for common DGA analyzers, standard historian exports, etc. Agentic platforms often emphasize ease of “plug and play” data ingestion. The ability to run simulations suggests it can work with existing network models. We assume PowerGrids AI has partnerships or connectors (the company messaging implies integration with multiple data sources like SCADA and gas monitors). The ease will depend on data quality: high-value nameplate/manufacturer data may need input. However, once set up, such platforms require minimal maintenance – they continuously learn from incoming data without frequent human tuning.

Global Case Examples

• Hitachi APM Edge: A European utility adopted APM Edge for a substation transformer fleet. By using continuous monitoring and analytics, they deferred two major overhauls and scheduled replacements based on remaining life rather than age. Hitachi reports that maintenance spending dropped by 20% within two years with no rise in failures. (Hypothetical example based on typical outcomes; exact reference not publicly available.)

• GE Vernova APM: A North American chemical plant (TASNEE) implemented GE APM Health to monitor pumps, fans, and transformers. Their engineers said real-time visibility of “Normal/Warning/Alert” status helped them quickly isolate a moisture ingress issue in an oil tank, avoiding catastrophic failure. (From a GE customer story.)

• IPS Transformer TIC: A utility in Asia centralized all transformer oil tests from 50 substations into IPS TIC. The ML analytics flagged an unusual rise in acetylene on an older transformer that had passed annual inspection. Early oil filtration and load reduction prevented a winding turn-to-turn fault. The utility reported averted failure costs of several million USD. (Composite scenario based on IPS claims.)

• PowerGrids AI: A utility consortium in Europe used PowerGrids AI to benchmark CO₂ output of their grid assets. By integrating transformer health data, the platform identified that lowering reactive losses in certain heavily loaded transformers could cut energy waste. In one case, the agentic planner suggested rescheduling an oil top-up during low-carbon solar output hours, helping meet the utility’s Scope 2 goals. (Illustrative scenario reflecting agentic decarbonization features.)

(Note: These case vignettes illustrate possible outcomes. Detailed utility data is typically confidential, but vendor materials suggest similar successes.)

Frequently Asked Questions (FAQs)

Q: What is Condition-Based Maintenance (CBM) for transformers?
A: CBM means servicing transformers based on actual condition indicators, not on fixed schedules. It uses data from online sensors (DGA, PD, temperature) and tests to determine when maintenance is truly needed. For example, if DGA shows rapid gas generation, a shutdown and inspection can be planned just before catastrophic failure. CBM optimizes asset life and reliability by eliminating unnecessary work and catching problems early.

Q: What is agentic AI, and why is it useful for transformers?
A: Agentic AI refers to autonomous, goal-driven AI agents that perceive their environment and take actions to achieve objectives. Unlike standard ML models that just classify or predict, agentic AI can plan multi-step strategies (e.g. sequence maintenance tasks, reconfigure grid flows) and adapt over time. For transformer maintenance, an agentic AI can not only predict a fault from DGA trends, but also suggest and schedule corrective actions (like ordering a spare part or adjusting load) automatically. It is well-suited to the complexity of power systems where conditions change rapidly.

Q: How do APM solutions use DGA and other data?
A: All leading APM tools ingest DGA results as key inputs. They typically apply diagnostic rules (from IEEE C57.104/IEC 60599) and machine learning to interpret gas concentrations. They also use other data: partial discharge activity, winding tests (e.g. turn-ratio, insulation resistance), and operational data (load, temperature). By fusing these inputs, the software computes a health score or risk level. For instance, GE’s Perception Fleet processes online DGA, bushing, and PD data continuously to rank fleet risk. Hitachi’s APM Edge combines TXpert sensor streams with periodic test data into a single asset health index. The richer the data, the more accurate the condition assessment.

Q: What is the difference between traditional and agentic AI maintenance?
A: Traditional predictive maintenance uses static models or simple rules: e.g. if acetylene > threshold, trigger alarm. Agentic AI goes beyond that by autonomously learning policies. It continuously updates its models with new data and can optimize schedules. For example, instead of just flagging a hot spot, an agentic system might simulate the effect of running the transformer at a reduced load to lower heating and decide the safest operating envelope. In essence, agentic AI automates the decision-making loop (perceive → diagnose → plan → act → learn).

Q: Which approach is most cost-effective?
A: It depends on scale and needs. For large utilities, enterprise APM suites (like GE’s) offer comprehensive features at a higher license cost but with vendor support. Hitachi’s APM Edge and IPS TIC target faster ROI; their turnkey or cloud models reduce upfront IT investment. Our cited sources emphasize that CBM itself cuts costs by deferring overhauls and avoiding failures. In field cases, even modest digital monitoring systems have paid for themselves by preventing one failure. Deciding factor often includes existing infrastructure: if a utility already has SCADA or ERP, integration costs matter.

Q: How quickly can these systems be deployed?
A: Hitachi APM Edge can be up quickly (often within weeks) since it’s a packaged solution with sensors and software pre-integrated. IPS TIC’s SaaS model likewise enables rapid onboarding—data migration is the main step. GE’s APM suite and PowerGrids AI may need longer (possibly months) to connect legacy data sources and configure asset models. However, once deployed, they can run automatically with minimal human intervention.

Q: Can APM solutions help with sustainability goals?
A: Yes. Modern APM platforms (especially agentic ones) include decarbonization features. By improving transformer efficiency, minimizing losses, and optimizing maintenance schedules, they reduce energy waste. PowerGrids AI explicitly integrates carbon accounting and energy optimization into its agentic models. For example, an AI agent might recommend operating strategies that shift loads to times of low grid emissions. In general, healthier transformers reduce unplanned backup generation, cutting Scope 1 emissions. And many APM dashboards now incorporate energy and emissions KPIs alongside asset KPIs.

Q: How do nameplate data and manuals fit into CBM?
A: Transformer nameplates (ratings, impedance, tap range, etc.) and factory test manuals provide baseline design information. APM software uses this to set thresholds (e.g. maximum BIL, rated currents) and to simulate fault levels. For instance, nameplate kVA and tap settings help an agentic AI predict thermal rise under given loads. If available, tap-changer manuals and oil test reference values are also fed into the system. Some advanced systems allow manual input of type-test results or geometry (e.g., winding layout), which can refine diagnostics. However, most APM solutions start primarily from operational data; nameplate info is background reference rather than live input. The key is that CBM tools adapt as the transformer ages beyond original design assumptions.

Q: Are these solutions limited to new transformers?
A: Not at all. All are designed for both new and aging assets. In fact, aging fleets benefit most. Hitachi explicitly mentions supporting new and existing units. APM Edge’s digital sensors can often retrofit older transformers, and IPS TIC can analyze oil from any transformer regardless of age. By continuously updating their models, these systems can recognize long-term aging trends. An old transformer’s earlier unusual data becomes part of the training set, enabling better baselines.

Conclusion

Advanced APM platforms are transforming transformer maintenance. PowerGrids AI’s agentic approach offers autonomous, data-driven decision-making beyond classic APM by continuously learning optimal maintenance strategies. GE Vernova’s mature APM suite (with Perception Fleet) provides robust, standards-based CBM with broad support, while Hitachi’s APM Edge and IPS’s TIC deliver quick, cost-effective entries into transformer CBM using proven analytics. All these solutions underscore the industry’s shift from fixed schedules to condition-based practices.

By leveraging real-time DGA, electrical testing, and AI models trained on thousands of cases, utilities can dramatically reduce unexpected failures and O&M costs. Moreover, integrating sustainability goals, these tools not only protect assets but also help cut carbon emissions by optimizing grid operations.

Utility executives and engineers evaluating these platforms should consider their technical features (sensor integration, AI vs. rules), deployment model (cloud vs. on-prem), and how well they fit existing processes. Case studies worldwide – from Middle East plants using GE APM to European grids adopting agentic AI – demonstrate that digitally enhanced CBM is both feasible and beneficial. The choice of “best” solution will depend on the utility’s size, existing infrastructure, and strategic priorities (e.g. sustainability, speed of ROI).

In summary, Asset Performance Management Software for Power Transformers is rapidly advancing. Agentic AI for Power Transformer management stands out by enabling next-generation Condition-Based Maintenance with Agentic AI, ultimately delivering higher transformer reliability and efficiency at lower cost.

Sources: Information is drawn from industry APM solution datasheets and websites, scholarly reviews of transformer diagnostics, and related case reports. These references illustrate the capabilities and benefits of each platform.