A power transformer is one of the most valuable and essential components of an electrical power system. It operates continuously to transfer electrical energy between different voltage levels and supports the reliable distribution of electricity.
Although transformers are designed for long-term operation, their internal components are exposed to electrical, thermal and mechanical stresses. Over time, these stresses can affect insulation materials and may lead to abnormal operating conditions.
One interesting aspect of transformer maintenance is that the insulating oil can provide important clues about the condition of the equipment.
Dissolved Gas Analysis, commonly known as DGA, is a diagnostic technique used to examine gases dissolved in transformer insulating oil. By identifying the types and concentrations of these gases, engineers can obtain useful information about possible internal problems.
However, understanding DGA involves more than checking whether individual gas concentrations are within recommended limits. The relationship between different gases, their changes over time and the transformer's operating history can also provide valuable information.
This article explores the importance of DGA and why historical analysis can make it even more useful.
1. What Is Dissolved Gas Analysis (DGA)?
Dissolved Gas Analysis is a method used to identify and measure gases dissolved in the insulating oil of oil-filled transformers.
Under abnormal thermal or electrical conditions, insulating oil and solid insulation materials can decompose and produce gases. Some of these gases dissolve in the oil and can be detected through laboratory testing.
An oil sample is collected from the transformer and sent for analysis. The laboratory identifies the concentration of different gases, usually reporting the results in parts per million (ppm).
The resulting report gives engineers information that can support the assessment of transformer condition.
DGA is valuable because it can help identify possible developing problems without requiring the transformer to be opened for internal inspection.
2. Important Gases Found in Transformer Oil
Different gases may be associated with different types of thermal and electrical activity.
Some of the important gases examined during DGA include:
- Hydrogen (H₂): Hydrogen can be generated under several types of electrical and thermal stress. Its presence and behaviour may provide useful diagnostic information.
- Methane (CH₄): Methane can be produced during the decomposition of insulating oil, particularly under certain thermal conditions.
- Ethane (C₂H₆): Ethane is another hydrocarbon gas associated with the thermal decomposition of oil.
- Ethylene (C₂H₄): Ethylene is often considered when assessing higher-temperature thermal activity.
- Acetylene (C₂H₂): Acetylene is an important gas in transformer diagnostics because its presence can be associated with high-energy electrical discharges and arcing. Its interpretation requires particular attention to the operating context.
- Carbon Monoxide (CO): Carbon monoxide can be associated with the decomposition of cellulose-based insulation.
- Carbon Dioxide (CO₂): Carbon dioxide is also associated with paper insulation ageing and other degradation processes. No individual gas should be treated as a complete diagnosis. Engineers examine the overall gas pattern and apply recognised diagnostic methods.
3. Why Are These Gases Important?
The concentration and combination of gases can help engineers investigate possible abnormal conditions.
For example, an increase in certain hydrocarbon gases may indicate thermal activity. A change in hydrogen may suggest electrical activity or other abnormal processes.
The presence of acetylene may require investigation into possible high-energy discharge activity, depending on the circumstances.
However, gas interpretation is not always simple. Different fault conditions can produce overlapping gas patterns. A single result may not provide enough information to identify the exact source of a problem.
This is why DGA reports are interpreted using recognised standards, historical information and engineering experience.
4. Understanding the Role of IEEE Guidance
IEEE provides recognised guidance for transformer maintenance and dissolved gas analysis.
Such guidance helps engineers understand gas concentrations, diagnostic approaches and possible abnormal conditions.
Thresholds and diagnostic criteria are useful because they provide a consistent reference for evaluating transformer test results.
However, a general threshold does not describe the entire history of an individual transformer.
Imagine two transformers with the same latest acetylene reading. One transformer has shown a stable concentration over several years. The other has increased from an earlier value of zero.
Although their latest readings are identical, the history behind those readings is different.
This difference is important.
A general limit provides a reference point, while historical analysis provides information about the direction and speed of change.
Both forms of assessment are useful and should complement each other.
5. The Problem With Examining Individual Reports
In many maintenance workflows, DGA testing takes place periodically rather than continuously.
An engineer may receive a laboratory report after several months and compare the latest values against the applicable diagnostic criteria.
The previous reports may be stored separately in spreadsheets, documents or maintenance management systems.
When multiple transformers are involved, manually comparing all these records can become time-consuming.
A gradual increase in gas concentration may not receive sufficient attention if each report is reviewed independently.
For example, consider a hypothetical transformer with the following acetylene readings:
January: 0 ppm
March: 1 ppm
May: 2 ppm
July: 4 ppm
The July reading should not automatically be considered proof of a particular fault. Its significance depends on testing reliability, applicable criteria, other gas concentrations and operating conditions.
Nevertheless, the progression from 0 to 4 ppm is an observation worth investigating.
A system that remembers all four readings can make this progression easier to identify.
6. Why Historical DGA Analysis Matters
Historical analysis focuses on how a transformer's condition changes rather than considering only its latest measurement.
By storing previous reports, the system can help answer questions such as:
- Which gases have increased over time?
- Has the rate of change become faster?
- Did an unusual change occur after maintenance?
- Have similar patterns appeared previously?
- Is the latest result different from the transformer's normal operating history?
This approach provides more context for engineering decisions.
It also makes it possible to connect DGA results with other relevant information.
For example, a gas concentration may change after a tap-changer maintenance activity. The timing does not prove that the maintenance caused the change, but it provides an important detail for further investigation.
Historical information can therefore help engineers understand not only what a test report says, but also how the transformer's condition has developed over time.
7. How Our DGA Trend Agent Uses This Information
Our project, Transformer Health Historian – DGA Trend Agent, aims to combine DGA records with historical transformer information using an AI-powered agent.
The proposed system stores data associated with individual transformers.
When a new DGA report is added, the agent can retrieve previous readings for that transformer and compare the latest values with earlier results.
It can also consider available loading events and maintenance records.
The objective is to make historical analysis accessible through a simple conversational interface.
For example, an engineer could ask:
"What changes have occurred in the gas concentrations of transformer T-3?"
The agent could retrieve the available records and provide a summary of the changes.
Another question might be:
"Was there any maintenance activity before the increase in acetylene?"
The system could examine the stored maintenance history and identify relevant events.
This can reduce the effort involved in manually locating and comparing information.
8. A Practical Demonstration Scenario
Consider two transformers, T-1 and T-3.
The historical records for T-1 contain an increase in acetylene concentration before a previously documented failure in 2024.
T-3 has recently shown an increase from 0 to 4 ppm following tap-changer maintenance.
A conventional threshold-based response might report that the latest value is within a specified reference limit.
A memory-enabled agent could provide additional context:
"T-3's acetylene concentration increased from 0 to 4 ppm following the previous month's maintenance activity. A similar historical pattern was recorded for T-1 before its documented failure. This observation does not confirm the same fault in T-3, but it warrants engineering review and a suitable follow-up test."
This is an illustrative scenario showing how historical memory can add useful information to a standard DGA assessment. The comparison should only be used as evidence when supported by verified historical records.
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9. Limitations and Precautions
DGA is a powerful diagnostic tool, but it has limitations.
Laboratory results can be affected by sampling procedures, sample handling, testing methods and data quality.
Different transformers may have different operating conditions, and similar gas patterns do not necessarily indicate identical problems.
Historical comparisons must also consider the time interval between tests. A change over two weeks may have a different significance from the same change over two years.
An AI system must therefore avoid making unsupported fault predictions.
It should present observations, explain the evidence and support engineering review rather than replacing professional judgement.
The reliability of an AI-based monitoring system also depends on the quality and completeness of the historical data provided to it.
Conclution
Dissolved Gas Analysis plays an important role in transformer condition monitoring. It provides engineers with valuable information about the gases produced under different operating conditions and can help identify possible internal abnormalities.
However, the full value of DGA becomes clearer when results are examined over time.
A single report provides a snapshot. A collection of reports provides a history.
Through our Transformer Health Historian – DGA Trend Agent, we aim to make that history easier to access, compare and understand.
By combining DGA trends, operating events and maintenance information, we hope to explore a smarter approach to transformer monitoring in which engineers can make decisions with better historical context.
The future of transformer maintenance is not only about identifying abnormal readings. It is also about understanding how those readings change over time and using that knowledge to support more informed decisions.
Ultimately, better transformer maintenance decisions come not from isolated readings, but from understanding the story those readings reveal over time.
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Watch the demo:https://www.youtube.com/watch?v=PcSKZOFjHrc
GitHub:https://github.com/ayush-an-grampurohit/transformer-health-historian/tree/main
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