Beyond the Threshold: Why Trend Monitoring and Advanced Diagnostics Define Modern Transformer Asset Management

Beyond the Threshold: Why Trend Monitoring and Advanced Diagnostics Define Modern Transformer Asset Management

H. Ganganna,.

DEE/TRE/Kurnool, APTRANSCO.

R.S. Chanakya Vardhan Reddy,

AEE/MRT/Nandyal, APTRANSCO

Abstract-Traditional, threshold-based "pass/fail" maintenance philosophies for power transformers are fundamentally reactive, often failing to identify assets on a trajectory toward failure and thereby creating significant operational and financial risk. This paper analyzes two distinct, real-world case studies to demonstrate the strategic superiority of a predictive asset management paradigm founded on data trend analysis and the targeted use of advanced diagnostics. The first case study examines an aging 30-year-old transformer (Legacy PTR) whose end-of-life condition, first suggested by accelerating negative trends in routine electrical and oil tests, was definitively confirmed by Furan analysis (3 ppm 2-FAL). This diagnostic confirmation allowed for a planned decommissioning, averting a probable in-service catastrophic failure that superficial, symptom-based interventions would have missed. The second case study investigates a recently repaired transformer (Reborn PTR) that presented a deceptively healthy profile on standard metrics like Breakdown Voltage (BDV) following an oil filtration process. However, a more sensitive Interfacial Tension (IFT) analysis revealed severe, unaddressed chemical contamination, highlighting the profound danger of relying on an incomplete diagnostic picture. Synthesizing the lessons from these cases, this paper argues that a robust, modern asset management strategy must prioritize the analysis of degradation velocity (trends) over static absolute values and must strategically deploy advanced diagnostics to uncover root- cause conditions. This integrated approach enables proactive, risk-informed decisions that enhance grid reliability, improve safety, and optimize the total lifecycle cost of critical assets.

1. INTRODUCTION: THE FRAILTY OF A PASS/FAIL PHILOSOPHY

   In the management of high-value assets like power transformers, maintenance has long been anchored in a simple concept: the pass/fail threshold. This approach, while straightforward, is reactive and harbors significant risk by identifying assets already in distress rather than those trending toward failure. It creates an illusion of security where an asset can be deemed "in specification" one month, only to suffer a catastrophic failure the next. The critical

   information lost in this binary assessment is the rate of degradationa metric that provides a far more accurate indication of an asset's true condition.

   Modern power grids demand unprecedented reliability, making a more sophisticated, predictive maintenance strategy essential. This modern approach leverages data analytics and advanced diagnostics to shift the focus from "Is the asset healthy today?" to "Given its current rate of degradation, what is its quantifiable risk of failure?" This allows for the early detection of incipient faults, enabling planned, proactive interventions that are safer and more economical than emergency repairs.

   This paper dissects two real-world case studies that illustrate the limitations of a threshold-based mindset and the value of an integrated, trend-based diagnostic approach.

   • Case 1: The Legacy Power Transformer (PTR): An aging transformer whose gradual decline was visible in trended data, but whose irreversible end-of-life condition was only confirmed by advanced Furan analysis.

   • Case 2: The Reborn Power Transformer (PTR): A repaired transformer that appeared healthy based on routine tests post- filtration, yet harbored a critical chemical contamination issue revealed only by Interfacial Tension (IFT) analysis.

     Together, these cases demonstrate that robust asset management must be built on two principles: diligent monitoring of data trends to understand the velocity of degradation, and the strategic deployment of advanced diagnostics to uncover the root cause of deterioration.

2. CASE STUDY 1: END-OF-LIFE DIAGNOSIS OF AN AGING ASSET (LEGACY PTR)

   The case of the 10/16 MVA, 132kV transformer at the Legacy substation (Year of Manufacture: 1994) illustrates a gradual, measurable deterioration that, when analyzed correctly, painted a clear picture of an asset approaching its structural end-of-life.

   1. Symptom Analysis: A Clear Trend of Degradation

      Routine electrical testing in early 2025 revealed concerning trends when compared to data from the previous year, indicating an accelerating decline in the insulation system's health.

      • Tan Delta (Dissipation Factor): This metric quantifies dielectric losses in the insulation.The Tan Delta value between the high-voltage (HV) and low-voltage (LV) windings increased from 2.586% in January 2024 to 3.480% in March 2025a relative increase of nearly 35%. Such a significant rise over 14 months is a classic indicator of insulation deterioration.

      • Oil Quality: The moisture content was 44.7 parts per million (ppm), far exceeding the 30 ppm limit. Moisture aggressively attacks cellulose insulation via hydrolysis, breaking down its molecular structure. This was reflected in the Breakdown Voltage (BDV) of 38.3 kV, below the 40 kV acceptable threshold, confirming the oil's insulating property was compromised.

      • Insulation Resistance (IR): IR tests returned critically low values (e.g., 216 M between HV and LV windings at 30°C), well below the minimum safety levels for a 132kV transformer.

        The convergence of these deteriorating trends (Table 1) presented a multi-faceted case that the transformer was in a state of advanced decline.

   2. Diagnostic Crossroads: Symptom vs. Root Cause

      The initial diagnosis pointed to a severe moisture problem, and the standard remedial actionoil filtrationwas proposed. However, Dissolved Gas Analysis (DGA) data revealed clues suggesting a more sinister failure mode. The ratio of oxygen (O2) to nitrogen (N2) was less than 0.1, an abnormally low value for a free-breathing transformer. If moisture were from an atmospheric leak, one would expect corresponding air ingress, maintaining or increasing dissolved oxygen. The low oxygen level implied it was being consumed by an internal chemical process: the advanced degradation of the solid cellulose insulation itself. In this context, the high moisture was not the root cause but asymptom of the paper's decay.

      This realization exposed the inadequacy of the proposed filtration. Standard oil filtration is a physical process that removes free water but does nothing to restore the mechanical strength of damaged paper insulation. The intervention would have been futile, providing only a temporary, cosmetic improvement while leaving the transformer at unacceptable risk of catastrophic failure from its brittle, weakened insulation

   3. Definitive Evidence via Furan Analysis

      To resolve this uncertainty, an advanced Furan analysis was performed. Furanic compounds, particularly 2-furaldehyde (2-FAL), are generated almost exclusively from the chemical degradation of cellulose insulation. Their concentration in oil serves as a direct indicator of the cumulative, irreversible degradation of the paper.

      The mechanical integrity of insulation paper is mesured by its Degree of Polymerization (DP). New paper has a DP of 1000-1200; a DP value below 250 signifies the paper's end- of-life, having lost its mechanical strength. The Furan analysis revealed a 2-FAL value of 3 parts per million (ppm), an exceptionally high concentration that signals catastrophic degradation. Industry guidelines often consider levels above 1 ppm (1000 ppb) to represent "substantial and permanent damage".

      Table1: Legacy PTRElectricaland Oil ParameterTrends(2024-2025)

      Parameter	Test Terminals	09.01.2024	18.03.2025	Notes
      Tan Delta	HV winding – LV winding	2.586%	3.480%	Significant increase indicates worsening dielectric losses.
      Capacitance	HV winding – Body	4.755 nF	4.135 nF	Decrease suggests physical changes in insulation.
      Insulation Resistance	HV winding – LV winding	–	216 M	Critically low value at 30°C ambient temperature.
      Oil BDV	–	38.3 kV		Below standard limit of 40 kV.
      Oil Moisture	–	44.7 ppm		Exceeds maximum limit of 30 ppm.

      Using established empirical formulas to estimate DP from the 2-FAL concentration:

      • Chengdong formula: DP= (1.51log (3))/0.0035295

      • De Pablo formula: DP=7100/(8.88+3) 598

      The Chengdong formula yields a DP of 295, alarmingly close to the end-of-life threshold of 250. Even the more conservative De Pablo estimate represents a loss of nearly half the paper's original strength. This high 2-FAL result was the "smoking gun," providing a direct, quantitative measure of the paper's health. The diagnosis was transformed from "a transformer with a moisture problem" to "a transformer at its structural end-of-life," making the decision to decommission the unit a clear and necessary action based on definitive scientific evidence.

3. CASE STUDY 2: UNMASKING LATENT RISK IN A REPAIRED ASSET (REBORN PTR)

   The case of the 20/31.5 MVA transformer at the Reborn substation reveals an insidious threat: hidden chemical contamination following a major repair. It is a cautionary tale about the dangers of relying on an incomplete set of diagnostic metrics

   1. The Contamination Event and the Illusion of Improvement

      Following repairs, new transformer oil of excellent quality was prepared. A sample taken on Jan 15, 2025 (Report A) showed a high Interfacial Tension (IFT) of 45.27 mN/m, negligible acidity, and superb

      dielectric properties.2 After this oil was filled into the transformer, its condition deteriorated dramatically. A sample taken on March 9, 2025 (Report B) revealed the IFT had plummeted to 16.74 mN/ma value indicative of severe contamination.

      IFT is a highly sensitive indicator of chemical health, measuring the tension at the oil-water boundary. Clean oil has a high IFT; as polar contaminants like acids and oxidation byproducts accumulate, the IFT drops. Values below 18 mN/m suggest the oil is badly deteriorated and at high risk of forming harmful sludge. This was corroborated by a twenty-fold increase in Tan Delta and a sharp drop in Resistivity, confirming the presence of conductive polar contaminants. The likely cause was the leaching of contaminants from residual

      byproducts of the previous failure or chemicals used during the repair process

      In response, a standard oil filtration was performed. A post-filtration sample on April 11, 2025 (Report C) showed that the water content was reduced and the BDV improved significantly to 77.8 kV. For an asset manager focused narrowly on moisture and

      BDV, this intervention would appear successful. However, this was an illusion. The filtration completely failed to address the core chemical problem. The IFT dropped further to 15.98 mN/m, and the Tan Delta

      worsened (Table 2).

      This outcome highlights the difference between oil reconditioning (filtration), a physical process that removes water and particles, and oil reclamation, a chemical process using adsorbents to remove dissolved polar contaminants. Filtration was the wrong tool for a chemical problem. The correct prescription was oil reclamation, which is designed to restore the oil's chemical health and IFT value

   2. Root Cause and Quality Control Implications

      The contamination source was almost certainly the transformer itself, pointing to a lapse in post-repair cleaning procedures. This case transcends a simple maintenance error and calls for more stringent post-repair quality assurance. A mandatory, comprehensive baseline oil testincluding the highly sensitive IFT measurementshould be required on a sample taken after the main oil fill but before the unit is energized. This would serve as a final quality gate.

      Furthermore, this case challenges the notion that a major repair "resets the clock." A post-repair transformer should be considered a unique asset class with an elevated initial risk profile. A prudent strategy is to place all such assets on an enhanced monitoring protocol for the first 6-12 months, with frequent testing of sensitive chemical indicators like IFT and acidity.

      Parameter	Report A (Storage Tank – Jan 2025)	Report B (PTR-3, Before Filtration – Mar 2025)	Report C (PTR-3, After Filtration – Apr 2025)	Standard Limit (IS:1866- 2017)
      Appearance & Colour	CLEAR	PALE YELLOW	PALE YELLOW	Clear, free from sediment & suspended matter
      IFT (mN/m)	45.27 (Ok)	16.74 (Not Ok)	15.98 (Not Ok)	>22.00
      Acidity (mg KOH/g)	0.0014 (Ok)	0.1159 (Ok)	Not Reported	<0.200
      Tan Delta @ 90°C	0.003466 (Ok)	0.069303 (Ok)	0.102288 (Ok)	<0.500
      Resistivity @ 90°C (Gm)	228.49 (Ok)	8.27 (Ok)	8.09 (Ok)	>0.20
      BDV (kV)	85.2 (Ok)	70.2 (Ok)	77.8 (Ok)	>40.00 (reconditioned) / >70 (after treat)
      Water Content (mg/kg)	7.6 (Ok)	12.8 (Ok)	7.7 (Ok)	<30.00 / <40 (new oil)

      Table 2: Comparative Oil Analysis of Reborn PTR:

4. DISCUSSION: A NEW PARADIGM FOR TRANSFORMER HEALTH ASSESSMENT

   The Legacy and Reborn cases necessitate a paradigm shift in maintenance philosophy, moving toward a dynamic, integrated, and predictive framework.

   1. The Primacy of Trending: From Static Limits to Degradation Velocity

      The true risk posed by an asset is often better described not by its absolute condition, but by the velocity of its decline. In the Legacy case, the alarming fact was not just the final Tan Delta value, but its 35% increase in just over a year. A rapid, accelerating increase is a powerful signal of an active problem.6 Similarly, the precipitous drop in IFT at Reborn was an unambiguous signal of an acute event.2 Asset management platforms must evolve beyond simple threshold alarms to incorporate algoriths that monitor the rate of change for key parameters, focusing resources on assets that are degrading the fastest.

   2. Strategic Deployment of Advanced Diagnostics Advanced tools like Furan and IFT analysis must be used proactively. Clear, policy-based triggers should be established for their deployment.

      • Furan Analysis Triggers: Mandate Furan testing based on age (e.g., >20 years), accelerating CO/CO2 generation in DGA, rapid increases in electrical test values like Tan Delta, or following significant thermal/electrical stress events.

      • IFT Testing Triggers: IFT should be a mandatory quality control test on all new oil batches and, critically, on oil sampled after a transformer has been filled post-repair but before energization. It should also be incorporated into routine annual testing as a sensitive early warning indicator.

   3. Towards a Holistic Health Index

      The ultimate goal is to move from analysing disparate data points to an integrated assessment of asset health. A "Transformer Health Index" provides a framework for this. Such an index is a weighted algorithm that ingests data from all sourcesDGA, oil quality (IFT, acidity), solid insulation health (2-FAL), and electrical tests (Tan Delta)and consolidates them into a single, understandable score.11 This allows for the rapid, data- driven ranking of an entire asset fleet, enabling the strategic prioritization of maintenance and capital replacement budgets.

5. CONCLUSION AND ACTIONABLE RECOMMENDATIONS

The analyses of the Legacy and Reborn transformers provide a clear mandate for change. The following strategic recommendations can build a more resilient and intelligent transformer management program.

1. Implement Trend-Based Alarming: Shift from a sole reliance on static alarm limits to a dual system that also incorporates dynamic alarms based on the rate of change of key health indicators. An accelerating degradation rate should trigger a high- priority alert.

2. Formalize a Two-Tiered Oil Maintenance Protocol: Differentiate between physical Reconditioning (Filtration) for moisture/particulate issues and chemical Reclamation for issues of low IFT or high acidity. Prescribe the correct treatment based on a complete diagnosis to avoid ineffective interventions.

3. Mandate Advanced Diagnostics at Critical Lifecycle Points: Integrate Furan analysis into standard protocols for aged or stressed assets. Mandate a comprehensive oil quality analysis, including IFT, as a final quality control gate for all transformers post-repair and pre-energization.

4. Invest in a Centralized Data Analytics Platform: To effectively implement trend-based alarming and health indexing, utilities must invest in a centralized platform capable of ingesting, storing, and analysing all forms of test data. Such a platform transforms raw data into the actionable intelligence needed to make defensible, data-driven decisions about maintenance and capital allocation.

   By embracing these principles, utilities can proactively identify and mitigate risks, ensuring a safer, more cost-effective, and more resilient power grid.

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