In this study, the artificial neural networks method was used in order to determine the leakage induction coefficient using known constants of the transformer. It is important to reach the leakage reactance through guessing. For this purpose, two types of ANN models were used in the study and were compared to one another. These two ANN models are cascaded ANN model and the conventional model. Testing data were used to measure the efficiency of these two models. Testing data are the same for both models. When the models were compared to each other, it was concluded that Cascaded ANN model was more successful. However, it is a fact that both models produce estimation around 99%. The main reason why the ANN model is used in the study is to ensure a more practical and quicker attainment of leakage reactance or leakage induction coefficient by looking at the fixed and measurable values of the transformer than calculation method.
[1] L. Andriušienė, A. Kairys, P. Kostrauskas, “Mathematical Modeling of
External Characteristics of Power Transformers” , Electronics and
Electrical Engineering. – Kaunas: Technologija, No. 5, 40, 2002
[2] P. Raitsios, “Leakage field of a transformer under conventional and
superconducting condition”, Journal of Materials Processing
Technology, 108. 246-252. 2001.
[3] R. Doebbelin, C. Teichert, M. Benecke, and A. Lindemann,
“Computerized Calculation of Leakage Inductance Values of
Transformers”, Piers Online, Vol. 5, No. 8, 2009.
[4] M. A. Tsili, A. G. Kladas, S. Georgilakis, “Computer aided analysis and
design of power transformers”, Computers in Industry, 59, pp. 338–350,
2008
[5] A. G. Leal, J. A. Jardini, L. C. Magrini, S. U. Ahn, “Distribution
Transformer Losses Evaluation, A New Analytical Methodology and
Artificial Neural Network Approach”, IEEE Transactions on Power
System, Vol. 24, No. 2, MAY 2009.
[6] M. Tripathy, R. P. Maheshwari and H.K. Verma, “Probabilistic neuralnetwork-
based protection of power transformer”,IET Electr. Power
Appl. 1, (5), pp. 793–798, 2007.
[7] A. Stadler, M. Albach, S. Chromy, “The Optimization of High
Frequency Operated Transformers for Resonant Converters”, Dresden
EPE , 2005
[8] O. İkizli, Distribution, Losses and Warm up on Electrical Machines -
Elektrik Makinalarında Dağılma, Kayıplar ve Isınma, Library of
Istanbul Technical University, 1962, No 482, pp. 15-18
[9] M. Tsili, A. Kladas, P. Georgilakis, A. Souflaris, D. Paparigas, “
Numerical techniques for design and modeling of distribution
transformers”, Journal of Materials Processing Technology, 161. 320–
326, 2005.
[10] J. Kondoh, I. Ishii, “Fault Current Limiting Transformer With Variable
Reactance”, IEEE Transactions on applied superconductivity, Vol. 14,
No. 2, June 2004.
[11] Jan Reynders, “The prediction of fault currents in a large multi-winding
reactor transformer”, IEEE Bologna PowerTech Conference, June 23-
26, Bologna, Italy, 2003.
[12] L. Fausett, Fundamentals of Neural Networks: Architectures, Algorithms
and Applications, Prentice Hall. 1994.
[13] H. Yurtoglu, Yapay Sinir Ağları Metodolojisi ile Öngörü Modellemesi:
Bazı Makroekonomik Değişkenler için Türkiye Örneği, DPT Dissertion,
DPT:2683, pp 1-7. Feb. 2005
[14] H. S. Nogay, “Prediction of Internal Temperature in Stator Winding of
Three-Phase Induction Motors with ANN”, European Transactions on
Electrical Power. 20:1–9. DOI: 10.1002. 2010.
[15] T. Kohonen, “State Of The Art In Neural Computing”, IEEE First
International Conference on Neural Networks, 1. 79-90, 1987.
[16] Z. Aydogmus, “A neural network-based estimation of electric fields
along high voltage insulators”, Expert Systems with Applications, 36,
8705–8710. 2009.
[17] T.M. Hagan, H.B. Demuth, M. Beale, Neural Network Design, PWS
Publishing Company, Boston 2-44, 1996.
[18] B. Ciylan, “Determination of Output Parameters of a Thermoelectric
Module using Artificial Neural Networks”, Electronics and Electrical
Engineering. – Kaunas: Technologija, no:10, 116, 2011
[19] G.E.P Box., G. Jenkins, Time Series Analysis, Forecasting and Control,
Golden-Day, San Francisco, CA, 1970.
[20] M. Islam, A. Sattar, F. Amin, X. Yao, and K. Murase, “A New
Constructive Algorithm for Architectural and Functional Adaptation of
Artificial Neural Networks”, IEEE Transactions on Systems , Man. And
Cybernetics – Part B, Vol. 39, No 6, Dec. 2009.
[21] B. Jena, D. Swain, and A. Tyagi, “Application of Artificial Neural
Networks for Sea-Surface Wind-Speed Retrieval From IRS-P4”,
(MSMR), Brightness Temperature IEEE Geoscience and Remote
Sensing Letters, Vol. 7, No. 3, 567. July 2010.
[22] Khaled Shaban, Ayman El-Hag, Andrei Matveev, “ A Cascade of
Artificial Neural Networks to Predict Transformers Oil Parameters”,
IEEE Transactions on Dielectrics and Electrical Insulation ,Vol. 16, No.
2; April 2009.
[23] A. Kazemi, M. T. Hassanzadeh, A. Gholami, “Artificial Neural Network
for Insulator Leakage Currents Prediction from Environmental Data”,
2nd IEEE International Conference on Power and Energy (PECon 08),
Johor Baharu, Malaysia, December 1-3, 2008
[24] T.C. Akinci. “Short Term Wind Speed Forecasting with ANN in
Batman, Turkey” , Electronics and Electrical Engineering, Vol.107,
No.1, 2011.
Year 2016,
Volume: 4 Issue: 2, 89 - 96, 30.09.2016
[1] L. Andriušienė, A. Kairys, P. Kostrauskas, “Mathematical Modeling of
External Characteristics of Power Transformers” , Electronics and
Electrical Engineering. – Kaunas: Technologija, No. 5, 40, 2002
[2] P. Raitsios, “Leakage field of a transformer under conventional and
superconducting condition”, Journal of Materials Processing
Technology, 108. 246-252. 2001.
[3] R. Doebbelin, C. Teichert, M. Benecke, and A. Lindemann,
“Computerized Calculation of Leakage Inductance Values of
Transformers”, Piers Online, Vol. 5, No. 8, 2009.
[4] M. A. Tsili, A. G. Kladas, S. Georgilakis, “Computer aided analysis and
design of power transformers”, Computers in Industry, 59, pp. 338–350,
2008
[5] A. G. Leal, J. A. Jardini, L. C. Magrini, S. U. Ahn, “Distribution
Transformer Losses Evaluation, A New Analytical Methodology and
Artificial Neural Network Approach”, IEEE Transactions on Power
System, Vol. 24, No. 2, MAY 2009.
[6] M. Tripathy, R. P. Maheshwari and H.K. Verma, “Probabilistic neuralnetwork-
based protection of power transformer”,IET Electr. Power
Appl. 1, (5), pp. 793–798, 2007.
[7] A. Stadler, M. Albach, S. Chromy, “The Optimization of High
Frequency Operated Transformers for Resonant Converters”, Dresden
EPE , 2005
[8] O. İkizli, Distribution, Losses and Warm up on Electrical Machines -
Elektrik Makinalarında Dağılma, Kayıplar ve Isınma, Library of
Istanbul Technical University, 1962, No 482, pp. 15-18
[9] M. Tsili, A. Kladas, P. Georgilakis, A. Souflaris, D. Paparigas, “
Numerical techniques for design and modeling of distribution
transformers”, Journal of Materials Processing Technology, 161. 320–
326, 2005.
[10] J. Kondoh, I. Ishii, “Fault Current Limiting Transformer With Variable
Reactance”, IEEE Transactions on applied superconductivity, Vol. 14,
No. 2, June 2004.
[11] Jan Reynders, “The prediction of fault currents in a large multi-winding
reactor transformer”, IEEE Bologna PowerTech Conference, June 23-
26, Bologna, Italy, 2003.
[12] L. Fausett, Fundamentals of Neural Networks: Architectures, Algorithms
and Applications, Prentice Hall. 1994.
[13] H. Yurtoglu, Yapay Sinir Ağları Metodolojisi ile Öngörü Modellemesi:
Bazı Makroekonomik Değişkenler için Türkiye Örneği, DPT Dissertion,
DPT:2683, pp 1-7. Feb. 2005
[14] H. S. Nogay, “Prediction of Internal Temperature in Stator Winding of
Three-Phase Induction Motors with ANN”, European Transactions on
Electrical Power. 20:1–9. DOI: 10.1002. 2010.
[15] T. Kohonen, “State Of The Art In Neural Computing”, IEEE First
International Conference on Neural Networks, 1. 79-90, 1987.
[16] Z. Aydogmus, “A neural network-based estimation of electric fields
along high voltage insulators”, Expert Systems with Applications, 36,
8705–8710. 2009.
[17] T.M. Hagan, H.B. Demuth, M. Beale, Neural Network Design, PWS
Publishing Company, Boston 2-44, 1996.
[18] B. Ciylan, “Determination of Output Parameters of a Thermoelectric
Module using Artificial Neural Networks”, Electronics and Electrical
Engineering. – Kaunas: Technologija, no:10, 116, 2011
[19] G.E.P Box., G. Jenkins, Time Series Analysis, Forecasting and Control,
Golden-Day, San Francisco, CA, 1970.
[20] M. Islam, A. Sattar, F. Amin, X. Yao, and K. Murase, “A New
Constructive Algorithm for Architectural and Functional Adaptation of
Artificial Neural Networks”, IEEE Transactions on Systems , Man. And
Cybernetics – Part B, Vol. 39, No 6, Dec. 2009.
[21] B. Jena, D. Swain, and A. Tyagi, “Application of Artificial Neural
Networks for Sea-Surface Wind-Speed Retrieval From IRS-P4”,
(MSMR), Brightness Temperature IEEE Geoscience and Remote
Sensing Letters, Vol. 7, No. 3, 567. July 2010.
[22] Khaled Shaban, Ayman El-Hag, Andrei Matveev, “ A Cascade of
Artificial Neural Networks to Predict Transformers Oil Parameters”,
IEEE Transactions on Dielectrics and Electrical Insulation ,Vol. 16, No.
2; April 2009.
[23] A. Kazemi, M. T. Hassanzadeh, A. Gholami, “Artificial Neural Network
for Insulator Leakage Currents Prediction from Environmental Data”,
2nd IEEE International Conference on Power and Energy (PECon 08),
Johor Baharu, Malaysia, December 1-3, 2008
[24] T.C. Akinci. “Short Term Wind Speed Forecasting with ANN in
Batman, Turkey” , Electronics and Electrical Engineering, Vol.107,
No.1, 2011.
Nogay, S. (2016). Determination of leakage reactance in monophase transformers using by cascaded neural network. Balkan Journal of Electrical and Computer Engineering, 4(2), 89-96.
All articles published by BAJECE are licensed under the Creative Commons Attribution 4.0 International License. This permits anyone to copy, redistribute, remix, transmit and adapt the work provided the original work and source is appropriately cited.