Prediction of Tap Changing Transformer Losses Minimization using Artificial Neural Network

Abstract

This research proposes a stable and efficient approach to estimating real power losses in tap-changing transformers using the Artificial Neural Network (ANN) technique. Power losses can have a substantial impact on the stability of the electrical supply to customers, especially when increased power demands cause uneven voltage profiles. Tap-changing transformers are crucial for adjusting output voltages to the desired levels. The suggested ANN algorithm was tested on an IEEE 30-bus system, and the findings show a high correlation between the training and testing outputs, with the correlation coefficient (R) approaching unity. This indicates the reliability and accuracy of the ANN model in determining the optimal tap setting for minimizing losses. The paper also underscores the significance of transmission loss and the associated costs. This indicates the reliability and accuracy of the ANN model in determining the optimal tap setting for minimizing losses. The paper also underscores the significance of transmission loss and the associated costs.