The Duval Pentagon: Enhancing Transformer Fault Diagnosis
- Augusto Moser
- May 24
- 4 min read
Updated: May 25
Transformers are the backbone of electrical power systems, and their failure can cause widespread outages and costly repairs. Dissolved gas analysis (DGA) is a vital tool for spotting early signs of trouble by examining gases dissolved in the transformer's insulating oil. For years, the Duval Triangle method has been a go-to for interpreting DGA results, but the tool—the Duval Pentagon—offers a fresh perspective that complements and enhances this approach.
The Duval Triangle: A Quick Recap
The Duval Triangle method is a graphical way to diagnose transformer faults. It uses the relative percentages of three gases—methane (CH₄), ethylene (C₂H₄), and acetylene (C₂H₂)—plotted on a triangle (figure 1). The position of the point reveals the fault type, such as partial discharges (low-energy electrical issues), thermal faults (overheating), or arcing (high-energy electrical discharges). It’s simple and effective, but it only considers three of the many gases DGA can measure.
Introducing The Duval Pentagon 1 and 2
The Duval Pentagon steps up the game by incorporating five gases: hydrogen (H₂), methane (CH₄), ethane (C₂H₆), ethylene (C₂H₄), and acetylene (C₂H₂). These gases are plotted on a pentagon, with each gas assigned to a summit. The relative percentage of each gas (e.g., H₂ ppm divided by the total ppm of all five gases) is marked along its axis, from the center (0%) to the summit (40%). The magic happens when you calculate the centroid—the "center of mass" of these five points. This single point’s location within the pentagon indicates the fault type.
The gases are arranged around the pentagon in order of increasing energy needed to produce them, from H₂ to C₂H₂. This mirrors the logic of some Duval Triangles and helps link gas patterns to specific fault energies, making diagnosis more intuitive.
Duval Pentagon 1: The Basics
Duval Pentagon 1 is designed to identify six common fault types, plus an extra condition:
PD (Partial Discharges): Low-energy electrical discharges, like corona effects.
D1 (Low Energy Discharges): Moderate electrical issues, such as small sparks.
D2 (High Energy Discharges): Severe arcing or major electrical faults.
T3 (Thermal Faults > 700°C): Intense overheating.
T2 (Thermal Faults 300–700°C): Moderate-to-high heat issues.
T1 (Thermal Faults < 300°C): Mild overheating.
S (Stray Gassing): Gas release from the oil itself, not a fault—think of it as a benign quirk of some mineral oils.
This version aligns with the fault categories used in standards like IEC and IEEE, making it a familiar yet expanded tool. Its fault zones were defined using DGA data from about 180 transformers, where faults were confirmed by visual inspection, grounding its reliability in real-world evidence.
Duval Pentagon 2: A Deeper Dive
Duval Pentagon 2 takes a more detailed approach, focusing on three electrical faults and four advanced thermal categories:
PD, D1, D2: Same electrical faults as Pentagon 1.
T3-H (Thermal Faults in Oil Only): High-temperature issues (> 700°C) affecting only the oil.
C (Carbonization of Paper): Thermal faults (across T1, T2, T3 ranges) that damage the transformer’s paper insulation—a serious concern.
O (Overheating < 250°C): Low-level heat issues, often less urgent.
S (Stray Gassing): Same as in Pentagon 1.
Pentagon 2 shines in distinguishing thermal faults by their impact—whether they’re confined to oil or involve paper carbonization, which can signal a riskier situation. Like Pentagon 1, its zones are backed by visually confirmed DGA data, ensuring accuracy.
Note: It is not recommended to attempt fault identification using the method described above if all gas levels are below the values of IEEE Std C57.104-2019 Table 1.
Complementing The Duval Triangle
The Duval Pentagon doesn’t replace the Triangle—it enhances it. While the Triangle focuses on three gases, the Pentagon uses five, giving a broader view of the transformer’s health. You can use either method alone, but together, they’re a powerhouse for diagnosis, especially in tricky cases.
Imagine a transformer where the Triangle points to a thermal fault (say, T3) but the Pentagon suggests stray gassing (S). This mismatch could hint at a mix of conditions—perhaps some harmless gassing plus an overheating issue. The Triangle might emphasize C₂H₄ (linked to thermal faults), while the Pentagon’s inclusion of H₂ and C₂H₆ picks up on stray gassing. Comparing the two helps uncover these mixtures, guiding engineers to dig deeper.
The Pentagon’s sensitivity to hydrogen and ethane also makes it better at spotting stray gassing, which might be misread as a fault in the Triangle alone. This complementarity can prevent unnecessary repairs and refine maintenance decisions.
Why It Matters
The Duval Pentagon, in both its versions, brings a richer analysis to DGA by tapping into more data points. Pentagon 1 offers a broad, practical overview, while Pentagon 2 dives into thermal fault nuances—crucial for assessing severity. Validated with real transformer data, these tools are ready for the field.
By pairing the Pentagon with the Duval Triangle, engineers get a dual-lens view of transformer health, catching subtle or mixed faults that might slip through a single method. As power systems demand ever-greater reliability, the Duval Pentagon is a timely addition to the diagnostic toolkit, helping prevent failures and extend transformer life.
Our Solutions
The HV Assets Care Platform is a complete solution for data analysis and diagnostics. It includes all the recommended methods from the IEEE standard, including the Duval Triangle and the advanced Combined Duval Pentagon (as below figure), integrated in a asset management dashboard. In addition, provides a Health Index with individual scores to create an asset ranking. For more information, click here.
References
M. Duval and L. Lamarre, "The new Duval Pentagons available for DGA diagnosis in transformers filled with mineral and ester oils," 2017 IEEE Electrical Insulation Conference (EIC), Baltimore, MD, USA, 2017, pp. 279-281, doi: 10.1109/EIC.2017.8004683.
