A neural network-genetic algorithm scheme for optimal grouping of individual cores in three-phase distributed transformers

This paper presents an effective method to reduce the iron losses of wound core distribution transformers based on a combined neural network-genetic algorithm approach. The originality of the work is that it tackles the iron loss reduction problem during the transformer production phase, while previous works were concentrated on the design phase. More specifically, neural networks effectively use measurements taken at the first stages of core construction in order to predict the iron losses of the assembled transformers, while genetic algorithms are used to improve the grouping process of the individual cores by reducing the iron losses of assembled transformers. The proposed method has been tested on a real transformer manufacturing industry and has resulted in a significant cost reduction.