Principal Research Results

Diagnosis of Transformer Condition based on Dissolved Gas Analysis
Data Using Multivariate Analysis
BackgroundIn general, Japanese electric power companies adopt a diagnosis criterion ＊ 1, which was established in 1999 by ElectricTechnology Research Association, as a standard diagnosis criterion of transformer condition based on dissolved gas analysis data ＊ 2.
However, an accurate dissolved gas analysis recently has been achieved and accurate dissolved gas analysis data is accumulated. The
measurement constraint, which should be satisfied to use the traditional diagnosis criterion, has been changed, now. Therefore, electricpower companies need to upgrade the diagnosis criterion of transformer condition using accumulated dissolved gas analysis data.

Objectives
Typical multivariate analysis methods will be applied to accumulated dissolved gas analysis data and the upgrading thediagnosis criterion of transformer condition will be realized.

Principal Results
1. Diagnosis results of transformer condition based on dissolved gas analysis data using typical multivariate analysis
Our experimental data were taken from 15 defective transformers and 100 normal transformers. These data were analyzed
by multivariate linear discriminate analysis, Maharanobis distance and Support Vector Machine with a linear kernel (hereafter linearSVM) (see Figure 1) and the following diagnosis results were obtained.
(1) In the case of diagnosis of transformer condition based on dissolved gas analysis data, the linear SVM achieved 0% misdiagnosis
rate in the experimental data (see Table 1).
(2) Leave-One-Out Cross Validation (see Figure 2) was carried out to evaluate the statistical performance of the linear SVM. Thelinear SVM achieved 0% averaged misdiagnosis rate.
2. Effective dissolved gas analysis data for the linear SVM
The linear SVM can evaluate the influence of each dissolved gas for the discriminant function of the diagnosis of transformer condition. This evaluation is realized by comparing each coefficient of dissolved gas in the discriminant function. Accordingly,
we could discover it is possible to diagnose transformer condition correctly using the dissolved gas analysis data of just C2H2 (acetylene) and C2H4 (ethylene).

Future Developments
We will plan to collect the dissolved gas analysis data of. oil-immersed transformers sufficiently and improve the diagnosis
accuracy of the linear SVM.
Main Researcher: Takashi Onoda, Ph. D.,
Senior Research Scientist, Mathematical Informatics Sector, System Engineering Research Laboratory
Reference
T. ONoda, et.al., 2007, “Transformer condition classification using multivariate analysis of dissolved gas analysis data”, CRIEPI
Report R07001 (in Japanese)＊1：The diganosis criterion was established by using the chemical knowledge and the maintenance data of transformers. The diagnosis criteronconsists of some rules. An example of these rules is that if C2H2 (acetylene) is bigger than 5 ppm, the transformer will be abnormal.
＊2：The dissolved gas analysis data consist of H2 (hydrogen), CH4 (methane), C2H6 (ethane), C2H4 (ethylene), C2H2 (acetylene), CO (carbonmonoxide) and CO2 (carbon dioxide).

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4. Power Delivery(a) Maharanobis distance(b) Linear discriminate analysis(c) Linear SVM

Fig.1 Typical multivariate analysis methods（a） The maharanobis distance assumes that data distribution is normal distribution. （b） The linear
discriminate analysis assumes that data distribution is normal distribution and all data distributions have samevariance and covariance.（c）The linear SVM assumes that the observed data can be classified definitely.

Table 1 Misclassification rate for each multivariate analysis method

NormalAbnormalcondition

condition

The number of data
Misclassification rate

Maharanobis distanceLineardiscriminate

analysis
Linear SVM

100

1550(50%)

0(0%)

0(0%)

6(40%)

0(0%)

0(0%)

T

12…

T

Leave-one-out cross-validation involves
using a single sample from the original
samples as the validation data, and theT-1 The disciminant function is…

Test dataTgenerated by these data.

T1

2…

The disciminant function isT-1
generated by these data.

Tremaining samples as the training data.
In our case, a single sample is extracted
from 115 samples as the validation data,
and the remaining samples are used as
the training data. This process is iterated
115 times.

Test data

Fig.2 Leave-One-Out Cross Validation

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