ANALYSIS OF POWER TRANSFORMATOR CONDITIONS USING DGA METHOD USING ARTIFICIAL NEURAL NETWORK IN KRAKATAU ELECTRICAL POWER COMPANY

Authors

  • Hartono Department of Electrical Engineering, University of Sultan Ageng Tirtayasa, Indonesia
  • Y. Muharni Department of Electrical Engineering, University of Sultan Ageng Tirtayasa, Indonesia
  • C. Adipura Department of Electrical Engineering, Universitas Sultan Ageng Tirtayasa, Indonesia
  • W. Martiningsih Department of Electrical Engineering, Universitas Sultan Ageng Tirtayasa, Indonesia
  • M. Otong Department of Electrical Engineering, Universitas Sultan Ageng Tirtayasa, Indonesia
  • M. Irvan Department of Electrical Engineering, University of Sultan Ageng Tirtayasa, Indonesia

DOI:

https://doi.org/10.29121/ijetmr.v7.i6.2020.572

Keywords:

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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References

Gedam, A. V, Swami, P. P. S., & Thosar, A. (2015). A-Comparative-Analysis-of-DGA-Methods-For-The-Incipient-Fault-Diagnosis-in-Power-Transformer-Using-ANN-Approach.docx. 6(5), 387–391.

Sharma, N. K., Tiwari, P. K., & Sood, Y. R. (2011). Review of Artificial Intelligence Techniques Application to Dissolved Gas Analysis on Power Transformer. International Journal of Computer and Electrical Engineering, 3(4), 577–582. https://doi.org/10.7763/ijcee.2011.v3.383 DOI: https://doi.org/10.7763/IJCEE.2011.V3.383

Nourmohammadzadeh, A., & Hartmann, S. (2015). Fault Classification of a Centrifugal Pump in Normal and Noisy Environment with Artificial Neural Network and Support Vector. Fourth International Conference on Theory and Practice of Natural Computing, 1, 58–70. https://doi.org/10.1007/978-3-319-26841-5 DOI: https://doi.org/10.1007/978-3-319-26841-5

Hartono, & Kuo, M.-T. (2018). Design of Simulation forTransient Stability Analysisin Smart Grid by Using Critical Clearing Time Index. International Journal of Engineering and Technology, 10(3), 269–273. https://doi.org/10.7763/ijet.2018.v10.1072 DOI: https://doi.org/10.7763/IJET.2018.V10.1072

Moosavian, A., Ahmadi, H., Tabatabaeefar, A., & Khazaee, M. (2013). Comparison of two classifiers; K-nearest neighbor and artificial neural network, for fault diagnosis on a main engine journal-bearing. Shock and Vibration, 20(2), 263–272. https://doi.org/10.3233/SAV-2012-00742

Yoru, Y., Karakoc, T. H., & Hepbasli, A. (2009). Application of Artificial Neural Network (ANN) method to exergy analysis of thermodynamic systems. 8th International Conference on Machine Learning and Applications, ICMLA 2009, 715–718. https://doi.org/10.1109/ICMLA.2009.70 DOI: https://doi.org/10.1109/ICMLA.2009.70

Nagpal, T., & Brar, Y. S. (2014). Artificial neural network approaches for fault classification: comparison and performance. Neural Computing and Applications, 25(7–8), 1863–1870. https://doi.org/10.1007/s00521-014-1677-y DOI: https://doi.org/10.1007/s00521-014-1677-y

Ghoneim, S. S. M., & Taha, I. B. (2015). Artificial Neural Networks for Power Transformers Fault Diagnosis Based on IEC Code Using Dissolved Gas Analysis. International Journal of Control, Automation and Systems, 4(2), 18–21.

Saranya, S., Mageswari, U., Roy, N., & Sudha, R. (2013). Comparative Study of Various Dissolved Gas Analysis Methods to Diagnose Transformer Faults. International Journal of Engineering Research and Applications (IJERA), 3(3), 592–595. www.ijera.com

Pereira, F. H., Bezerra, F. E., Junior, S., Santos, J., Chabu, I., De Souza, G. F. M., Micerino, F., & Nabeta, S. I. (2018). Nonlinear autoregressive neural network models for prediction of transformer oil-dissolved gas concentrations. Energies, 11(7). https://doi.org/10.3390/en11071691 DOI: https://doi.org/10.3390/en11071691

Roland, U., & Eseosa, O. (2015). Artificial Neural Network Approach to Distribution Transformers Maintenance. International Journal of Scientific Research Engineering Technology (IJSRET), 1(4), 62–70.

Yu, S., Zhao, D., Chen, W., & Hou, H. (2016). Oil-immersed Power Transformer Internal Fault Diagnosis Research Based on Probabilistic Neural Network. Procedia Computer Science, 83(Wtisg), 1327–1331. https://doi.org/10.1016/j.procs.2016.04.276 DOI: https://doi.org/10.1016/j.procs.2016.04.276

Hartono, H., Marifa Ahmad, A., & Sadikin, M. (2018). Comparison methods of short-term electrical load forecasting. MATEC Web of Conferences, 218, 1–8. https://doi.org/10.1051/matecconf/201821801002 DOI: https://doi.org/10.1051/matecconf/201821801002

Muthi, A., Sumarto, S., & Saputra, W. S. (2018). Power Transformer Interruption Analysis Based on Dissolved Gas Analysis (DGA) using Artificial Neural Network. IOP Conference Series: Materials Science and Engineering, 384(1), 0–5. https://doi.org/10.1088/1757-899X/384/1/012073 DOI: https://doi.org/10.1088/1757-899X/384/1/012073

Rigatos, G., & Siano, P. (2016). Power transformers’ condition monitoring using neural modeling and the local statistical approach to fault diagnosis. International Journal of Electrical Power and Energy Systems, 80, 150–159. https://doi.org/10.1016/j.ijepes.2016.01.019 DOI: https://doi.org/10.1016/j.ijepes.2016.01.019

H. D. Mehta, R. M. P. (2014). A Review on Transformer Design Optimization and Performance Analysis Using Artificial Intelligence Techniques. International Journal of Science and Research (IJSR), 3(9), —. https://www.ijsr.net/archive/v3i9/U0VQMTQxOTg=.pdf

Adeolu, O., & Adejumobi, I. A. (2014). Breakdown Voltage Characteristics of Castor Oil as Alternative to Transformer Insulation Oil. 2(4), 31–37.

Al-Janabi, S., Rawat, S., Patel, A., & Al-Shourbaji, I. (2015). Design and evaluation of a hybrid system for detection and prediction of faults in electrical transformers. International Journal of Electrical Power and Energy Systems, 67, 324–335. https://doi.org/10.1016/j.ijepes.2014.12.005 DOI: https://doi.org/10.1016/j.ijepes.2014.12.005

G. K. M. A. P. C. Pravin S. Khade, "Artificial Neural Netwok Approach to Dissolved Gas Analysis for Interpretation of fault in power transformer," International journal Of Sctentific & Engineering Reasearch, no. 373-377.

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Published

2020-06-16

How to Cite

Hartono, Muharni, Adipura, Martiningsih, Otong, & Irvan, M. (2020). ANALYSIS OF POWER TRANSFORMATOR CONDITIONS USING DGA METHOD USING ARTIFICIAL NEURAL NETWORK IN KRAKATAU ELECTRICAL POWER COMPANY. International Journal of Engineering Technologies and Management Research, 7(6), 77–88. https://doi.org/10.29121/ijetmr.v7.i6.2020.572