No abstract

Insulation failures are among the most important problems affecting electric machines. Although standard procedures are widely used to assess the insulation condition of these machines, no alternative is available to allow the identification of the stress agents affecting the insulation system. Another limitation of the standard procedures is the fact that they are based on tests that require the interruption of the operation of the machine. This study presents a technique that allows the online assessment and protection of the insulation of low-and medium-voltage electric machines, along with the identification of the stress agents responsible for the degradation of the insulation. The proposed technique is based on the use of a highly sensitive current transformer, an algorithm to identify the most affected phase, and an artificial neural network to indicate the stress agent affecting the machine. The presented experimental results show that the proposed system can identify thermal degradation, moisture absorption and contamination by oil. The low current levels that can be measured by the developed system enable the application of the proposed technique to low-and medium-voltage machines.

This work presents a new approach to the on-line evaluation of the insulation of electric machines. Through the proposed system, it is possible to identify the stress agents causing the degradation of the insulation of low-and mediumvoltage machines. In addition, the estimation of the time-to-failure (TF) of the insulation is developed based on linear stochastic models autoregressive moving average and artificial neural networks. The identification of the stress agent and the estimation of the TF give a complete prognosis for the predictive monitoring of the machine insulation.