Prognosis of Underground Cable via Online Data-Driven Method With Field Data

In order to prevent unexpected electrical outage and to save on repair expenses, an online data-driven prognosis method for monitored underground cable is proposed. The method is nondestructive and nonintrusive. The field data of voltage and current were collected from an underground distribution cable lateral installed in a residential area. Potential useful features were selected based on the data-driven method using the training data. The rationality of selected features was verified in view of cable aging mechanisms; after that, the useful features were finally determined. The remaining life of cable was forecasted by predicting the time for the cumulative effect of the selected features to reach the threshold. The performance of the developed prognosis method was tested and evaluated using the field testing data. The standard deviation of the prognosis results at different arrival times is used as a source of uncertainty estimation (reverse of confidence estimation). The results demonstrate that the method can continually offer valuable remaining life prediction of the monitored underground cable as time goes on. When the predicted fault times approach the actual fault time, the standard deviation value is small. When the standard deviation value is small, there are steady forecast results and usually small residuals.