Application of Artificial Neural Networks in Evaluation and Identification of Electrical Loss in Transformers According to the Energy Consumption

The paper describes a novel neural model to estimate electrical loss in transformer according to the energy consumption (load curve). The network acts as identifier of structural features on electrical loss process, so that output parameters can be estimated and generalized from an input parameter set. The model was trained and assessed through experimental data taking into account core loss, copper loss, active power, reactive power, residential load factor, commercial load factor and time. The results obtained in the simulations have shown that the developed technique can be used as an alternative tool to make the analysis of electrical losses on distribution transformer more appropriate regarding to energy consumption. Thus, this research has led to an improvement on the planning of the electrical system. Key-Words: Transformer, losses identification, neural networks, energy consumption, load factor, load demand.