Dissolved Gas Analysis (DGA) IEC Ratio Method Artificial Neural Network
(ANN) Test method that can be done for transformer oil with DGA method. In identifying early transformer conditions, one of them is using IEC 60599 Standards. The artificial neural network training process used 341 data in the presence of nine conditions based on the IEC standard. The best network architecture configuration is a configuration with 3 neurons in the input layer, 10 neurons in the first hidden layer, 20 neurons in the second hidden layer, 20 neurons in the third hidden layer and 4 neurons in the output layer with the transfer logic. The results of the training give a regression value of 0.95216 and MSE (Mean Square Error) is worth 0.000216. Testing of artificial neural networks is done 19 first test data is performed to determine the number of transformer conditions that can be diagnosed by each method. From the test data obtained the accuracy value for artificial neural network models is 94.7%. The following will guide the structure of your abstract:
## 1. INTRODUCTIONOne form of
transformer maintenance is by conducting tests to determine the state of the
transformer. Tests carried out to test insulating oil in addition to
translucency testing and dielectric gain-loss testing, PLN also applies the DGA
(Dissolve Gas Analysis) test method. This test method is carried out to test
the condition of the insulating oil by taking insulating oil samples from the
transformer unit to determine the types of gas dissolved in the transformer
oil. The purpose of DGA testing is the transformer to be known, Therefore it is necessary to do an analysis for
abnormalities in the transformer by testing DGA (Dissolved Gas Analysis) so
that it can be known in advance about the likelihood of the transformer [15]. When the
transformer works in normal conditions, there are various kinds of gas produced
in small amounts, called C2H4, C2H2, CH4, N2 and O2. When a failure occurs in
the transformer, the concentration of gas produced will vary depending on the
type of failure in the transformer. The level of gas produced by the oil
transformer is used as an indication of the condition of the transformer. The
gases used in DGA analysis are H2 (Hydrogen), CH4 (Methane), C2H4 (Ethylene),
C2H6 (Ethane), C2H2 (Acetylene), CO (Carbon monoxide), and CO2 (Carbon dioxide [12]. The dissolved gas
(DGA) analysis method is an analysis of the condition of the transformer based
on the amount of dissolved gas in transformer oil, by extracting the gases from
oil samples taken from the transformer. The extracted gas is then added
according to each gas and is calculated in ppm units (parts per million). From
the results of this DGA test it can be known in advance about the failure of
the transformer that may arise. There are several DGA test standards that have
been determined by IEEE, including the Duval Triangle, Total Combustible Gas
(TDCG), Key Gas, Roger Ratio, Doernenburg Ratio and
IEC Ratio. From several
methods of data interpretation, DGA and test standards established by the IEEE,
then made here using one of the test standards namely IEC Ratio. The main
reason for using the IEC Ratio method is because this method is still rarely
used to do DGA analysis especially in Indonesia. However, the test standard for
DGA analysis also has drawbacks, the main drawback of the Ratio method is the
failure method for all data. To overcome this problem, we need a solution from the AI (Artificial Intelligence) method, one of which is ANN (Artificial Neural Network). ANN. Knowing the funds needed from the pattern and being able to acquire knowledge to buy nonlinear objects, requires quite a lot of data in the training process. But the expected method ANN is able to provide accurate and fast analysis results for reading transformers. ## 2. MATERIALS AND METHODSDissolved gas
analysis (DGA) is an analysis of the condition of the transformer which is
based on the amount of dissolved gas in transformer oil [2]. For several years the method of analyzing dissolved gases in oil has been
used as a transformer diagnosis tool. Analyzing dissolved gas content requires
several steps, namely taking oil samples, extracting gas, interpreting data and
drawing conclusions. Dissolved gas analysis is done by measuring the total
flammable gas content which is interpreted by various methods. Commonly used
methods are the key gas, the roger ratio method, and the Duvall triangle
method. Roger ratio
method is to compare the amount of different gases by dividing one gas with
another, this forms a ratio ratio between one gas
with another gas. This method uses a ratio of three gases, namely C2H2 / C2H4,
CH4 / H2 and C2H4 / C2H6. Roger ratio actually consists of 4 ratios namely C2H2
/ C2H4, CH4 / H2, C2H4 / C2H6 and C2H6 / CH4. However, the C2H6 / CH4 ratio
only indicates a limited temperature range from decomposition but does not help
in identifying further faults. It should be noted that the roger ratio method
is used for disturbance analysis rather than for detecting interference and
therefore interference must be detected using the Institute of Electrical and
Electronics Engineers (IEEE) limits.
IEC is one of the
popular standards for determining transformer conditions based on the ratio of
five key gases H2, CH4, C2H4, C2H6, and C2H2 in this method of gas
constellation (R1 = C2H2 / C2H4, R2 = CH4 / H2, and R3 = C2H4 / C2H6) the code
of the ratio is used to determine a condition in the tansformator.
The combination of each gas ratio code is used to determine the condition of
the transformer after the gas with the code given in each condition. The combination
of individual code X1, X2 and X3 is an indicator of the possibility of failure.
Table 2 below shows the transformer failure codes based on the IEC 599 standard
of the individual codes X1, X2, and X3 shown in table 3 AND 4. These gas key
ratio coders can help facilitate the development of computational programming
that is easier to identify transformer failures. However, this IEC ratio method
in some cases, fails to identify the type of failure accurately (Shakeb A. Khan, 2014).
Neural Network
(NN) is a network of a collection of small processing units that are modeled
based on human neural networks. This NN is an adaptive system that can change
its structure to solve the problem of external or internal information flowing
through the network. The structure is very parallel, resulting in the ability
to self-regulate to represent information and solve problems quickly. In this paper a
new method for Artificial Neural Networks is applied to DGA for the interpretation
of initial errors in power transformers. Error interpretation can be found as a
multi-class classification problem. ANN automatically adjusts network
parameters, connection weights, and bias requirements of neural networks, to achieve
the best model based on the proposed evolution algorithm, which provides
solutions to complex classification problems, because the hidden relationship between
the type of error and dissolved gas can be recognized by ANN through training
process. ## 3. RESULTS AND DISCUSSIONSTo overcome these
deficiencies, this study uses an artificial neural network (ANN) method with a
ratio of gas as the input and condition of the transformer as the target. the
gas comparison ratio is R1, R2, R3 and has nine outputs which each detect the
state of the transformer. To simplify the ANN training process, the output of
each condition is changed to certain numbers so that it can be understood by
the ANN algorithm.
In this paper the
MATLAB software is used to build the ANN model. MLP neural networks are made
separately for the Rogers ratio method and the IEC ratio method. Logic, and
logic functions are used as transfer functions. Figure 2 shows an Artificial
Neural Network with five hidden layers. For the development of neural networks,
360 sample datasets are used. 341 datasets were used for training purposes and
19 datasets were used for testing purposes. To interact with MLP networks, a
GUI is created using MATLAB. It provides a user interface with the network. The
value of the gas produced due to error is given as network input using the GUI
as shown in figure 3. By using this panel, the method applied by ANN is
selected. The error type window displays the type of error.
Regression in the
preprocessing process, on targets with network output values 0-1. In this
regression plot shows the relationship between the actual data and the output
data from the Artificial Neural Network on the training data. The coefficient R
is 0.95216 close to 1, showing good results for the compatibility of the output
with the actual data. For general error analysis purposes, all errors
are categorized into nine error codes. Codes 0001 through 1001 are assigned to
this error as shown in the table.
The desired
result in this design is to be able to know the gas fault that occurs in the
transformer oil and can make it easier to analyze faults based on the gas
content in the transformer oil. The table is a comparison of gas data for
parameters R1, R2, R3 that are used to test artificial neural networks based on
conditions. It can be seen that in each condition it represents a cross fault.
Is the comparison
of target data with network output. Comparison between targets and ANN output
can be seen in the table.
To find out how
valid the results of the test can use the formula level of accuracy. . ## 4. CONCLUSIONS & RECOMMENDATIONSFrom the research that has been done can be concluded among other things: Based on the
conclusion of the experimental results, the artificial neural network model
with the 3 hidden layer network architecture is the most optimal, in the first
hidden layer, 10 neurons are arranged and the second and third are 20 neurons
using the transfer logic function, and the output layer 4 neurons with the
logic activation function. So that it has a correlation coefficient
(regression) of 0.95216 and MSE (Mean Square Error) is worth 0.000216. the
accuracy obtained is 94.4%. ## APPENDICES## SOURCES OF FUNDINGNone. ## CONFLICT OF INTERESTNone. ## ACKNOWLEDGMENTI give thanks to God Almighty, because of His blessings and grace, I was able to complete this thesis. This research writing is carried out in order to fulfill one of the requirements to obtain an reasearch in the Department of Electrical Engineering at the Sultan Ageng Tirtayasa University. I realize that, without the help and guidance of various parties, from the lecture period to when writing this research, discussing it was difficult for me to complete this research. ## REFERENCES
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