Managing Editors’ Note:
This article is Part 2 of a 2-part series by Bhaba Das and Anthony Urlich from Dynamic Ratings, Australia. When I first received this article from Bhaba, who is a valued member of our Technical Advisory Board, I planned to publish it in full. I believe it represents one of the best practical approaches to Mitigate Hidden Risks of Rapid Cyclic Loading with Condition Monitoring, so we are splitting it into Part 1 and Part 2, with the latter coming in May magazine.
What Happens Under Rapid Cyclic Loading
When transformers experience rapid load swings—for example, twice daily peaks in urban networks or dozens of cycles per day in EV applications—the following sequence unfolds:
- Heating Phase (Load Increase):
- Hot-spot temperatures rise quickly.
- Moisture solubility in oil increases.
- Paper, seeking new equilibrium, releases moisture into the oil.
- Cooling Phase (Load Reduction):
- Hot-spot temperatures fall.
- Oil can theoretically reabsorb less moisture.
- However, diffusion of water back into paper is slower than release.
- Net result: stepwise increase in average oil moisture content.
This phenomenon destabilizes the oil-paper system, leading to persistent elevation of oil moisture ppm. Elevation of moisture reduces the BDV. This is the hidden vulnerability of rapid cyclic loading. Field experience indicates that when paper insulation exceeds around 3% moisture and daily temperature swings reach ~20 °C, dielectric strength is severely compromised – a clear trigger for dielectric stress alarms and gassing phenomena [15].
When Does Cyclic Loading Become a Concern?
Based on different simulations, cyclic loading should be flagged when:
- Paper moisture > 3%.
- Temperature swings exceed 20 – 25 °C within few hours.
- Cycle frequency ≥ 1 per day (daily or sub-daily peaks).
- BDV < 40 kV under operating conditions.
The following decision matrix (Table 1) can act as a guide for asset managers –
| Paper moisture vs Temp Swing | Low (≤10 °C) | Moderate (10-20 °C) | High (>20 °C) |
| Dry (<2.0%) | Safe | Low concern | Caution |
| Moderate (2.0–3.0%) | Low concern | Caution | Concern |
| Wet (>3.0%) | Caution | Concern | High risk |
How condition Monitoring helps against hidden risk?
As shown in the earlier sections, rapid cyclic loading has emerged as a defining stressor in transformers connected to renewable generation, electric vehicle fast charging, and battery energy storage. While nameplate ratings provide a static view of capability, they do not capture the dynamic risks imposed by frequent load swings, rapid heating and cooling, and accelerated moisture migration. These hidden risks can only be revealed through online condition monitoring:
- Moisture Dynamics Made Visible:
One of the most critical hidden risks under cyclic load is moisture migration between solid insulation and oil. Daily or even hourly swings in hot-spot temperature can force moisture to leave paper insulation and dissolve into the oil, reducing dielectric strength at precisely the wrong moment. Offline moisture or Karl Fischer titration tests may suggest the transformer is “safe,” while real-time relative saturation (%RS) monitoring shows sudden transitions into high-risk regimes. By tracking this in real time, end users know periods in operation where the insulation is temporarily weakened.
Daily or even hourly swings in hot-spot temperature can force moisture to leave paper insulation and dissolve into the oil, reducing dielectric strength at precisely the wrong moment.
- Predicting Dielectric Stress Events
Condition monitoring combines %RS and temperature data into predictive breakdown voltage (BDV) models. These models warn operators when dielectric strength is temporarily depressed, enabling proactive decisions such as rescheduling peak demand or adjusting cooling operation. - Thermal Stress and Hot-Spot Awareness
Cyclic loading drives sharp temperature fluctuations that increase thermal gradients across windings. Repeated expansion and contraction stresses cellulose and can promote bubble formation under high load. Advanced condition monitoring, such as fibre optic sensors and real-time thermal modelling, provides continuous hot-spot temperature trends. This not only improves risk visibility but also allows operators to align load management with safe thermal envelopes. - Linking Condition to Asset Management
The true value of condition monitoring lies not only in detection but also in integration. Feeding real-time condition data into digital twins and asset health indices enables adaptive operating strategies. For example, transformer condition can directly inform dispatch schedules for charging stations or battery energy storage, ensuring that network flexibility is achieved without compromising insulation health.
The true value of condition monitoring lies not only in detection but also in integration. Feeding real-time condition data into digital twins and asset health indices enables adaptive operating strategies.
Conclusion
The growing penetration of renewable generation, battery energy storage, and electric vehicle fast charging has made rapid cyclic loading an unavoidable feature of modern power networks. While transformers were traditionally assessed against steady-state operating limits, this new duty cycle exposes hidden vulnerabilities in insulation, dielectric strength, and thermal stability that cannot be detected through offline sampling alone.
Condition monitoring provides the missing visibility. By continuously tracking parameters such as relative saturation, hot-spot temperature, and dissolved gases, operators gain a dynamic picture of how load swings affect insulation health in real time. Instead of being caught off guard by sudden dielectric weakness or accelerated ageing, utilities and asset owners can anticipate high-risk periods and adjust operation proactively.
Looking forward, condition monitoring will evolve beyond parameter tracking into predictive and prescriptive asset management. Digital twins, enriched with online sensor data, will simulate transformer behaviour under various cyclic load scenarios, offering operators early warnings before limits are breached. Artificial intelligence will enable pattern recognition across large fleets, distinguishing normal operational scatter from fault precursors with greater confidence. Meanwhile, international working groups, such as those within CIGRE, are moving toward harmonised frameworks for interpreting online data in the context of new duty cycles.
In essence, condition monitoring transforms uncertainty into actionable insight. It allows transformers to safely support the fast-changing demands of the energy transition while extending service life and reducing unplanned outages. By combining real-time monitoring with predictive analytics and international best practice, the industry can ensure that the hidden risks of rapid cyclic loading remain under control in the grids of tomorrow.
References
[1] I. Alvarez Fernandez, et.al, “Transformer thermal ratings with evolving load profiles,” 27th International Conference on Electricity Distribution (CIRED 2023), pp. 2431-2435, Italy, 2023.
[2] Tapan Kumar Saha and Prithwiraj Purkait, “Transformer Insulation Materials and Ageing,” in Transformer Ageing: Monitoring and Estimation Techniques, Chapter 1, pp.1-33, Wiley-IEEE Press, 2017.
[3] O. Roizman, “Moisture equilibrium in transformer insulation systems: Mirage or reality? Part 1, Transformer Magazine, Vol 6, Issue 2, 2019.
[4] O. Roizman, “Moisture equilibrium in transformer insulation systems: Mirage or reality? Part 2, Transformer Magazine, Vol 6, Issue 3, 2019.
[5] A. Al-Abadi, A. Gamil and A. Sbravati, “Determination of Moisture Content during Dynamic Loading of Liquid-Filled Distribution Transformers,” 2024 IEEE Electrical Insulation Conference (EIC), Minneapolis, USA, pp. 134 138, 2024.
[6] A. Al-Abadi, J. Bobrowski and A. Gamil, “Enhanced Transformers Thermal and Moisture Distribution Modelling for On-Line Assessment of Insulations Condition,” 2025 IEEE International Conference on Dielectric Liquids (ICDL), Lodz, Poland, pp. 1-4, 2025.
[7] IEC 60156:2025, Insulating liquids – Determination of the breakdown
voltage at power frequency – Test method.
[8] ASTM D1816-12(2019), Standard Test Method for Dielectric Breakdown Voltage of Insulating Liquids Using VDE Electrodes.
[9] Vitaly Gurin and Marius Grisaru, “The statistical scatter of breakdown
voltages of transformer oil – Part I”, Transformer Magazine, Vol 11, Issue 4, 2024.
[10] IEC 60814:1997, Insulating liquids – Oil-impregnated paper and pressboard – Determination of water by automatic coulometric Karl Fischer titration.
[11] ASTM D1533-20, Standard Test Method for Water in Insulating Liquids by Coulometric Karl Fischer Titration.
[12] CIGRE Technical Brochure 349, Moisture equilibrium and moisture
migration within transformer insulation systems, WG A2.30, 2008.
[13] CIGRE Technical Brochure 741, Moisture measurement and assessment in transformer insulation – Evaluation of chemical methods and moisture capacitive sensors, WG D1.52, 2018.
[14] Vaisala White paper, “The Effect of Moisture on the Breakdown Voltage of Transformer Oil”, 2013.
[15] B. Sparling and J. Aubin, “Assessing Water Content in Insulating Paper of Power Transformers”, Electric Energy T&D Magazine, July 2007.
Dr. Bhaba P. Das is the Regional Manager (Asia Pacific) for Dynamic Ratings Australia, based in Wellington, New Zealand. He is a Senior Member of IEEE, Young Professional of IEC, Member CIGRE NZ A2 panel, Member of Engineering New Zealand. He has published 40+ technical articles in various peer reviewed international journals and magazines. He is a member of CIGRE working groups A2 D1.67 and CIGRE A2/C3.70 as well part of the Standards Australia EL008 Transformers Committee. He is also part of the advisory board for FAN project at University of Canterbury (NZ) and represents Dynamic Ratings at the Transformer Innovation Centre, University of Queensland, Australia. He has three patents in New Zealand & Australia related to condition monitoring. He has recieved the Hitachi Energy Global Transformer Excellence Awards in 2020, 2021 and 2023 and New Zealand Young Engineer of the Year 2017 by Electricity Association of NZ. He has previously worked at Hitachi Energy Transformers Business Unit in Singapore & ETEL Transformers Ltd in Auckland, New Zealand. He completed his PhD in Electrical Engineering from the University of Canterbury, New Zealand and Bachelors Degree in lectrical Engineering from University of Gauhati, Assam, India.
Anthony Ulrich is currently the Sales Manager (OAP) for Vaisala, based in Melbourne. Prior to this position, he was a Strategic Account Manager for Dynamic Ratings (Australia and New Zealand). Anthony has a background in Sales, Support and product management for different companies, including Vaisala, Thermo Fisher, dataTaker, and National Instruments. This allowed him to have a deep understanding data acquisition and measurement science from the customer and the supplier. Anthony graduated from LaTrobe University, Australia with a degree in computer science and instrumentation in 1997.
