Transformer diagnosis using probabilistic vibration models

Power transformers always vibrate while working. The vibration detected in the tank is different at different parts of the transformer, with different frequencies and amplitudes. The difference in the vibration pattern depends on the working conditions and the age of the transformer. Mechanical faults produce differences in this vibrational pattern caused by either, failures in the winding, in the core or both. This paper presents the construction of models that represent the normal behavior of the transformer vibration. Thus, a deviation in this normal behavior allows detection of mechanical faults. The models are probabilistic models obtained with vibration measurements taken around the transformer tank, and using automatic learning algorithms developed in the Artificial Intelligence community. The models are composed by Bayesian networks that represent the probabilistic relationships between all variables. When a new condition is inserted in the Bayesian network, inference algorithms are used to estimate on-line, a probability of abnormal behavior. This project is in collaboration with Prolec General Electric, the largest constructor of transformers in North America. Experiments were carried out at Prolec GE laboratories on power substation transformers (PST). A discussion of the experiments and their results are included in this paper.