Optimal distribution transformers assembly using an adaptable neural network-genetic algorithm scheme

The 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 presented in this paper is that it tackles the iron loss reduction problem during the transformer production phase, while previous works 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 iron losses of assembled transformers. The proposed method has been tested on a transformer manufacturing industry. The results demonstrate the feasibility and practicality of this approach. Significant reduction of transformer iron losses is observed in comparison to the current practice leading to important economic savings for the transformer manufacturer.