This paper presents a complete differential protection system for power transformers, applying the Artificial Neural Network (ANN) theory. The proposed approach treat the classification of the protection system as a problem of pattern recognition and as an alternative method to the conventional algorithms. Several factors such as, for example, transformer energization and CT saturation can cause an inadequate operation of the protection relay. A complete protection system was developed, including an ANN-based device in substitution to harmonic filters in use in the conventional algorithm. This stage was carried out by a MLP Backpropagtion ANN to the signals classification. Some approaches concerning the reconstruction of the distorted signals caused by the CTs saturation are also proposed. This analysis was made by Elman recurrent ANNs used to reconstruct the distorted signals caused by CT saturation. These routines are added to the final protection algorithm. With the use of artificial intelligence tools in a complete power transformer protection algorithm, a very precise, fast and efficient solution was obtained, if compared to the conventional methods.
Differential Protection; Artificial Neural Networks; Power Transformers; Current Transformer Saturation
