POWER LOSSES ANALYSIS IN 3 PHASE DISTRIBUTION TRANSFORMER IN NORMAL LOAD AND OVER LOAD CONDITION USING ARTIFICIAL NEURAL NETWORKS TECHNIQUE
Keywords:
Distribution Transformer, Power Losses, Artificial Neural Network, TemperatureAbstract
This research paper presents power loss analysis in a 3-phase distribution transformer of 100 kVA 22 kV-230/400 V in normal load and overload conditions using a neural network technique. This method can analyze power loss in the transformer quicker and use fewer variables than the power loss calculation using various parameters obtained from transformer manufacturers. Seventy thousand sets of current tests ranging from 1% - 140% at temperatures of 35oC, 45oC, 55oC, 65oC and 75oC were experimentally measured, then power losses are calculated. Fifty-six thousand sets of those were used for training of the neural network to find the parameters and the other 14,000 sets were used for the input data to find power losses. In addition, the power losses obtained from the artificial neural network were compared with calculated power losses by using parameters from transformer manufacturers. The error percentage value no more than 1.06 was at a satisfactory level suggestingthat this method can be applied in the designing of electrical power loss test for transformers in the future.
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De souza, A. N., Da Silva, L. N., de Soura, C. F. L. N., and Zago, M. G. (2001). Using Artificial Neural Networks for Identification of Electrical Loosses in Transformers during the Manufacturing Phase. In Proceedings the 2002 Internationnal Joint Conference on Neural Network. V.2. pp. 1346-1350. Hong Kong.
Suttisinthong, N., and Pothisarn, C. (2014). Analysis of Electrical Losses in Transformers using Artificial Neural Networks. In Proceedings of the International MultiConference of Engineers and Computer Scientists. pp. 1-5. Hong kong.
Basheer, I. A., and Hajmeer, M. (2000). Artificial neural networks, fundamentals, computing, design, and application. Journal of Microbiological Methods, 43(1), 3-31.
Suechoey, B., Siriporananon, S., Pringsakul, N., and Thongrak, A. (2018). Implementation of Load Cycle Simulation for Studies of Loss Energy and Lifetime of Oil-Immersed Transformers. In Proceedings of the 10thInternational Conference on Sciences, Technology and Innovation for Sustainable Well-Being (STISWB 2018). pp. 202-207.
Siriporananon, S., and Suechoey, B. (2020). Power Losses Analysis in a Three-Phase Distribution Transformer Using Artificial Neural Networks. The ECTI Transactions on Electrical Engineering, Electronics & Communications, 18(2), 130-136.
Siriporananon, S., Suechoey, B., and Pringsakul, N. (2018). Performance testing of transformers used in distribution systems. In Proceedings of the 10th International Conference on Sciences, Technology and Innovation for Sustainable Well-Being (STISWB 2018). pp. 196-201.
IEC 60076-2. (2006). Power Transformer Part 2 : Temperature Rise.
Grainger, J. J., and Stevenson, W. D. (1994). Power System Analysis. McGraw-Hill International editions.
Provincial Electricity Authority Thailand (PEA). (2015). Three-Phase Transformer for 22 kV and 33 kV Distribution Systems with Ability to Withstand Short Circuit. Specification No. RTRN-035/2558.
Hagan, M. T., Demuth, H. B., and Beale, M. (1996). Neural Network Design. Boston. USA, PWS Publishing Company.
Demuth, H., and Beale, M. (1998). Neural Network Toolbox User’s Guide. The Mathwork Inc.
Souza, K. N., Castro, T. N., Pereira, T. M., Pontes, R. S. T., and Braga, A. P. S. (2011). Prediction of core losses on a three-phase transformer using neural network. IEEE International Symposium on Circuits and Systems (ISCAS), pp. 1105-1108.
Yadav, A. K., Azeem, A., Singh, A., Malik, H., and Rahi, O. P. (2011). Application Research Based on Artificial Neural Network (ANN) to Predict No-Load for Transformer’s Design. International Conference on Communication Systems and Network Technologies. pp. 180-183.
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