Effective maintenance strategies of the grid components can help to reduce the distribution costs of electrical energy. Therefore the insulation condition of important components have to be determined. This can be done with the help of online or offline Partial Discharge (PD) measurements and reliable and efficient PD-defect identification systems. This approach discusses a system, which supports an on-site operator or an off-site expert to estimate the breakdown probability caused by an identified PD-defect. The algorithm in use combines a defect specific risk factor and the uncertainty of the PD-diagnosis. The defect specific risk depends on the aspects: PD-location, overvoltage dependencies, time dependencies and PD-defect properties. A procedure is described, how to integrate these aspects into a risk assessment estimation. The diagnosis confidence is calculated automatically by the defect identification system WinTED. For each run the breakdown probability concerning the identified PD-defect is calculated and a range of low, medium or high risk is indicated to the user. Diagnosis examples are given for different defect-types and it is shown how the defect specific risk and the diagnosis confidence are influencing the breakdown probability estimation.