Transformer tap changing by data classification using artificial neural network

Abstract

Artificial neural network (ANN) can play a vital role in its application to on-load tap changer of distribution transformers since the input variation in practical field is enormous due to continuous changes in the power system conditions and neural network is capable of handling this variation due to its robust generalization capability. In this paper we use the two algorithms, namely, scaled conjugate gradient (SCG) and Bayesian regularization (BR) for training an ANN to control the automatic on load tap changer of two parallel transformers connected across the power network. Primarily five data sets are constructed considering voltage phase angle differences between the sources feeding the primary windings while the secondaries are connected in paralleling mode. For each data set, accuracy in ANN based tap changer decision is investigated and BR algorithm yields better results than SCG algorithm. In the second stage, another combined data set is formed by analyzing the trend of false responses in primary results. Results show significant improvement in performance when combined data set is used for training. With this data set, difference in performance between BR and SCG algorithms reduces, however BR still performs better with significantly high accuracy in tap changer operation. The results demonstrate the potential applicability of ANN in tap changer operation of parallel transformers.