ANALYSIS OF POWER TRANSFORMATOR CONDITIONS USING DGA METHOD USING ARTIFICIAL NEURAL NETWORK IN KRAKATAU ELECTRICAL POWER COMPANY
DOI:
https://doi.org/10.29121/ijetmr.v7.i6.2020.572Keywords:
Dissolved Gas Analysis (DGA), IEC Ratio Method, Artificial Neural Network (ANN)Abstract
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:
Motivation/Background: Using the neural network method in this study is expected to improve accuracy and improve the transformer analysis process. Transformer to make one effective and fast way for transformers.
Method: The IEC method is an effective method for implementing transformers. The way this method works is by comparing the concentration of solute, then the results are represented into nine kinds of conditions. However, this method has a weakness that is the length of time in the analysis process. Therefore, to overcome these deficiencies, this study uses the Artificial Neural Network (ANN) method with a comparison of the use of gas as its input and the condition transformer as its target.
Results: The results of the training give a regression value of 0.95216 and MSE (Mean Square Error) is worth 0.000216.
Conclusions: This study uses 460 data from existing data into 2 namely data for training that brings 341 data and data for testing to get 19 data. In this study using a neural network resolves the problem in this study. in this study obtained an accuracy of 94.4%, so this artificial neural network method has good potential to assist in this study.
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