The Effectiveness of Neural Networks in Losses Prediction for Three-Phase Distribution Transformer

Sarun Nakthanom

Abstract


The objectives of this research were to develop and evaluate the effectiveness of neural networks in losses prediction for three-phase distribution transformer. The research procedures are (1) Data collection; (2) Model’s creation; and (3) Training and testing of the Prediction Model. In this study, the value of effectiveness of losses prediction for three-phase distribution transformer (100 kVA 22000-400/230 V) was found using neural networks. The nodes in the hidden layer consisted of 3, 5 and 8 nodes with less variable and testing ranging from 1% - 100% at temperature of 30ºC for 10,000 sets. The 8,000 data sets were used to train, and the 2,000 data sets were used to test. The result of studying the effectiveness of losses prediction for three-phase distribution transformer found that the gradient values were equal to 31.1, 3.36 and 0.531. The mean squared error values were equal to 1.330, 3.610 and 0.966 respectively. Finally, the mean absolute percentage error values were equal to 0.1637, 0.798 and 1.437.


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References


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