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Characterization of Artificial Neural Network based Power Transformer Protection
Subject area: Science,Engineering and Technology · Area of research: Power Control System
Abstract
This work strives to develop a high-performance power transformer protection system using Artificial Neural Network (ANN). In achieving this, enhanced back propagation algorithm, the calculation ability of Artificial Neural Network (ANN) and its capability to accurately identify non-linear system were harnessed. A combination of two multi-layer neural networks was used in the implementation of the protection system. The first network was used in predicting the transformer oil temperature using the transformer load current as the input. The second ANN network is used in implementing the classification of the power transformer operating conditions which includes normal and fault conditions. This network has the oil temperature, oil level and dielectric strength parameters of the transformer as its input while after identification and classification gives out at its output the transformer dynamic operation condition. These outputs are Normal operation, Overheating, Dielectric degradation and Critical conditions. The critical condition through a trip logic trips off the power transformer. A total of 1500 data from a working 15MVA power transformer were used in the training of the networks. 70% of the data was used in training, 15% was used in validation while the remaining 15% was used in testing the performance. Both ANNs showed remarkable results. The ANN-1(TEMPEST) was able to predict temperature to an accuracy level of 99.99% while the ANN-2(CLASSFAULT) was able to give a 100% operating condition of the power transformer. These results attest to the fact that ANN could be used effectively and efficiently in the protection of power transformers.
Keywords
Power transformer; transformer parameter monitoring; artificial neural network; temperature prediction; fault classification; trip logic
References
[1] Cui X, Shin KG (1992). Application of neural networks to temperature control in thermal power plants. Engineering Applications of Artificial Intelligence, 5(6), 527–538.
[2] Grainger JJ, Stevenson WD Jr (1994). Power system analysis. McGraw-Hill, New York.
[3] Harlow JH (ed.) (2012). Electric power transformer engineering, 3rd ed. CRC Press, Boca Raton, FL.
[4] Haykin SS (1999). Neural networks: A comprehensive foundation, 2nd ed. Prentice Hall, Upper Saddle River, NJ.
[5] Hopfield JJ (1982). Neural networks and physical systems with emergent collective computational abilities. Proceedings of the National Academy of Sciences of the USA, 79(8), 2554–2558. https://doi.org/10.1073/pnas.79.8.2554
[6] Kundur P (2003). Power system stability and control. McGraw-Hill, New York.
[7] Pansini AJ (1999). Electrical transformers and power equipment, 3rd ed. Fairmont Press, Lilburn, GA.
[8] Rumelhart DE, Hinton GE, Williams RJ (1986). Learning representations by back-propagating errors. Nature, 323, 533–536. https://doi.org/10.1038/323533a0
[9] Weedy BM, Cory BJ, Jenkins N, Ekanayake JB, Strbac G (2012). Electric power systems, 5th ed. Wiley, Chichester.
How to cite this paper
@article{1724611,
author = {E. E. Ezema, Ibekwe E. B., Eze Ukamaka J.},
title = {Characterization of Artificial Neural Network based Power Transformer Protection},
journal = {Iconic Research And Engineering Journals},
year = {2026},
volume = {10},
number = {5},
pages = {1142-1150},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1724611.pdf},
abstract = {This work strives to develop a high-performance power transformer protection system using Artificial Neural Network (ANN). In achieving this, enhanced back propagation algorithm, the calculation ability of Artificial Neural Network (ANN) and its capability to accurately identify non-linear system were harnessed. A combination of two multi-layer neural networks was used in the implementation of the protection system. The first network was used in predicting the transformer oil temperature using the transformer load current as the input. The second ANN network is used in implementing the classification of the power transformer operating conditions which includes normal and fault conditions. This network has the oil temperature, oil level and dielectric strength parameters of the transformer as its input while after identification and classification gives out at its output the transformer dynamic operation condition. These outputs are Normal operation, Overheating, Dielectric degradation and Critical conditions. The critical condition through a trip logic trips off the power transformer. A total of 1500 data from a working 15MVA power transformer were used in the training of the networks. 70% of the data was used in training, 15% was used in validation while the remaining 15% was used in testing the performance. Both ANNs showed remarkable results. The ANN-1(TEMPEST) was able to predict temperature to an accuracy level of 99.99% while the ANN-2(CLASSFAULT) was able to give a 100% operating condition of the power transformer. These results attest to the fact that ANN could be used effectively and efficiently in the protection of power transformers.},
keywords = {Power transformer; transformer parameter monitoring; artificial neural network; temperature prediction; fault classification; trip logic},
month = {November},
}