A synergetic neural network-genetic scheme for optimal transformer construction

In this paper, a combined neural network and an evolutionary programming scheme is proposed to improve the quality of wound core distribution transformers in an industrial environment by exploiting information derived from both the construction and transformer design phase. In particular, the neural network architecture is responsible for predicting transformer iron losses prior to their assembly, based on several actual core measurements, transformer design parameters and the specific core assembling. A genetic algorithm is applied to estimate the optimal core arrangement, (i.e. the way of core assembling) that yields a set of three-phase transformers of minimal iron losses. The minimization is performed by exploiting information derived from the neural network model resulting in a synergetic neural network-genetic algorithm scheme. After the transformer construction, the prediction accuracy of the neural network model is evaluated. If accuracy is poor, a weight adaptation algorithm is applied to improve the prediction performance. For the weight updating, both the current and the previous network knowledge are taken into account. Application of the proposed neural network-genetic algorithm scheme to our industrial environment indicates a significant reduction in the variation between the actual and the designed transformer iron losses. This further leads to a reduction of the production cost since a smaller safety margin can be used for the transformer design.