The reliability of multi-parameter insulation diagnosis

On the basis of statistical theory, a method to determine quantitatively the reliability of multi-parameter diagnosis and to optimize the algorithm of multi-parameter diagnosis is put forward. Moreover, as an example, this method is applied to the estimation of residual breakdown voltage of generator bars and the optimized multi-parameter diagnosis algorithm is determined on the basis of actual data. Comparing with experimental data, it shows that the result of multi-parameter prediction model is more accurate than that of single-parameter prediction model when choosing the appropriate parameters group, however the quantity of parameters is not the more the better. To choose appropriate parameters for assessing insulation condition is important.

I. Introduction

The Diagnosis of Insulation can not only reduce the failure rate of electrical equipment, but also extend its life and reduce the expense for investment in equipment renewal. However, as the current non-destructive diagnosis method can only provide not so accurate estimation for the life-time, multiple measurement methods or multi-parameters are expected to enhance the reliability of diagnosis. There are two possible advantages for multi-parameter diagnosis. First, multiple parameters may reflect different failure processes in the different parts of one power apparatus. Second, multiple parameters can reflect different failure mechanisms that may exist simultaneously in the aging process of single insulation structure. In the former case, the adoption of multiple measurement parameters always increases the reliability of diagnosis. However, in the latter case, the addition of one measurement parameter which is of large dispersion for estimation of life-time or remaining breakdown voltage, etc. may not lead to more accurate diagnosis.