A new stochastic method based on Hidden Markov Models to transformer differential protection

This paper proposed a new classification method based on hidden Markov models (HMM) to discriminate between magnetizing inrush current and internal faults in transformers. To reduce training procedure time, K-means clustering algorithm is applied to dataset. Since the discrimination method is done with probabilistic characteristics of signals without application of any deterministic index, more reliable and accurate classification is achieved. Based on the proposed algorithm a high speed differential relaying could be performed in about half of a cycle. The suitable performance of this method is demonstrated by simulation of different fault types and switching conditions on a power transformer. All simulation results validate the proposed scheme accuracy. It provides a high operating sensitivity for internal faults and remains stable for inrush currents of the power transformers.