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Power Transformer Diagnostics and Insulation

Power Transformer Diagnostics and Insulation is a research topic within Electrical and Electronic Engineering. Science Explorer counts 23k research works in it since 1950. 7.8% of them reached the world's top 10% most cited for their field and year.

This cluster of papers focuses on the condition assessment and diagnosis of power transformers, with an emphasis on techniques such as dissolved gas analysis, frequency response analysis, and dielectric spectroscopy. The aging of insulation materials, use of vegetable oils, and asset management strategies are also key areas of research.

  • Dissolved Gas Analysis
  • Frequency Response Analysis
  • Insulation Condition Assessment
  • Transformer Fault Diagnosis
  • Dielectric Spectroscopy
  • Aging of Insulation
  • Power Transformer Asset Management
  • Oil Impregnated Paper
  • Vegetable Oils for Transformers
  • Diagnostic Techniques
Research works
23k
fractional, since 1950
In the world top 10%
1.8k
per year above
Top-10% rate
7.8%
share of its works in the world top 10%
Growth, 2013–17 → 2018–22
+43%
the tick is no change

Which countries lead Power Transformer Diagnostics and Insulation research?

By volume, China and India publish the most (1.7k and 441 works in 2022–2025).

By volume, 2022–2025

  1. 1 China 1.7k works
  2. 2 India 441 works
  3. 3 United States 247 works
  4. 4 Indonesia 110 works
  5. 5 Russia 108 works
  6. 6 ?? 106 works
  7. 7 Iran 99 works
  8. 8 Germany 96 works
  9. 9 Japan 91 works
  10. 10 Malaysia 88 works

How concentrated that is

The same countries as shares of everything the list above accounts for. A node where two countries do two thirds of the work and one spread evenly across twelve read alike as a ranking and not at all alike here.

China: 55.1%India: 14.3%United States: 8.0%Indonesia: 3.6%6 others listed: 19.0%55%largest
China1,700 · 55.1%India441 · 14.3%United States247 · 8.0%Indonesia110 · 3.6%6 others listed588 · 19.0%

Shares of the rows listed above, not of the whole node.

Which institutions lead Power Transformer Diagnostics and Insulation research?

By volume in 2022–2025, North China Electric Power University publishes the most Power Transformer Diagnostics and Insulation research, followed by Xi'an Jiaotong University and China Southern Power Grid (China).

Who are the leading researchers in Power Transformer Diagnostics and Insulation?

The most-cited researchers publishing on Power Transformer Diagnostics and Insulation include Frede Blaabjerg and Marco Liserre.

  1. 1 Frede Blaabjerg Denmark 12k citations
  2. 2 Marco Liserre Germany 4.6k citations

Ranked by citations received across their whole record, among researchers with at least three works on this topic.

Where is Power Transformer Diagnostics and Insulation research done?

The largest centres of Power Transformer Diagnostics and Insulation research in 2022–2025 are Beijing (China), Xi'an (China), Guangzhou (China) and Chongqing (China). Among places with at least 20 works in it, it is an unusually large share of all research in Xi'an, Palo Alto and Beijing.

Largest cities, 2022–2025

  1. 1 Beijing China 322 works
  2. 2 Xi'an China 150 works
  3. 3 Guangzhou China 125 works
  4. 4 Chongqing China 120 works
  5. 5 Shanghai China 115 works
  6. 6 Wuhan China 78 works
  7. 7 Chengdu China 58 works
  8. 8 Nanjing China 57 works
  9. 9 Harbin China 49 works
  10. 10 Tianjin China 48 works

Where it is the local speciality

  1. Xi'an29.2 works94×
  2. Palo AltoUS · 35.7 works25×
  3. Beijing38.6 works22×
← less than its size predictsmore →

Location quotient: how much more of its research is in Power Transformer Diagnostics and Insulation than the world average.

See Power Transformer Diagnostics and Insulation on the map

Where is the best place to study Power Transformer Diagnostics and Insulation?

Among universities, judged by research, Pandit Deendayal Energy University, Xi'an Jiaotong University and Université du Québec à Chicoutimi score highest, combining excellence, specialisation, size, growth and international reach. Research strength is one signal when choosing where to study; it does not measure teaching.

0%20%40%mean 12.29%fractional works in this node (log) →share in the world top 10% →Pandit Deendayal Energy University: 10, 44.7%Xi'an Jiaotong University: 108, 6.4%Université du Québec à Chicoutimi: 17, 31.5%Chongqing University: 87, 5.3%North China Electric Power University: 110, 2.7%National Institute of Technology Calicut: 10, 7.5%King Mongkut's Institute of Technology Ladkrabang: 13, 2.5%Wuhan University: 31, 9.1%Shandong University: 31, 5.6%Jadavpur University: 17, 7.6%Pandit Deendayal Ene…Université du Québec…Xi'an Jiaotong Unive…Chongqing University
above the meannear itbelow it

One dot per university in the table below. The upper left is the interesting corner: small places doing unusually strong work.

#UniversityScoreTop 10%SpecialisationWorksGrowth
1 Pandit Deendayal Energy UniversityIndia 63.744.7%21.0×10
2 Xi'an Jiaotong UniversityChina 62.06.4%15.9×108 +138.3%
3 Université du Québec à ChicoutimiCanada 60.031.5%70.9×17 +45.8%
4 Chongqing UniversityChina 53.95.3%18.7×87 +49.0%
5 North China Electric Power UniversityChina 51.52.7%38.9×110 +26.4%
6 National Institute of Technology CalicutIndia 47.47.5%17.3×10 +490.5%
7 King Mongkut's Institute of Technology LadkrabangThailand 47.12.5%24.1×13 +186.7%
8 Wuhan UniversityChina 45.69.1%6.1×31 +179.3%
9 Shandong UniversityChina 44.65.6%6.1×31 +282.2%
10 Jadavpur UniversityIndia 43.77.6%14.8×17 +72.6%

Universities only. Score blends excellence (30%), specialisation (25%), size (20%), growth (15%) and international reach (10%), 2015–2022; growth compares 2010–14 with 2015–19.

Is Power Transformer Diagnostics and Insulation research growing?

Output in 2018–2022 was 43% higher than in 2013–2017, peaking in 2025. The fastest-growing topics are Power Transformer Diagnostics and Insulation.

19801990200020102020
grewheldshrank

The same series as a ribbon — one cell per year, darker for more. The line above answers how much; this answers when.

Which topics inside it are moving

Growth and decline on one axis around a shared zero. Two lists side by side hide the thing that matters: whether the growth dwarfs the decline, or the other way round.