A data analytic approach for assessing XLPE cable insulation condition via resistance measurements

Abstract

The insulation resistance (IR) test has been widely conducted by electricity companies to assess cable insulation health status due to its ease of applicability. However, there exist cases in which medium-voltage power cables pass the IR test but fail in service after re-energization. So far, historical IR data obtained via measurements have been recorded, but have never been systematically studied to improve the accuracy of detecting unhealthy cables. This paper proposes a data analytic approach using historical IR data to identify patterns for distinguishing between healthy and unhealthy cables and assess the cable insulation condition. The proposed approach first leverages a two-parameter Weibull analysis to link the failure probability of the cables to their ages. Such analysis sheds light on the classification of the cables with respect to their age and material. Next, a diminishing method (DM) is used to set the critical IR values and provide maximum detection of the unhealthy cables with minimum misclassification of the healthy cables as unhealthy. Finally, a self-organizing-map-based support vector machine (SOM-SVM) is used to classify the cables as healthy or unhealthy. The hybrid DM-SOM-SVM approach is applied to the historical IR data of 22kV and 6.6kV cross-linked polyethylene (XLPE) cables. Compared to current industrial IR criteria for insulation condition diagnosis, the proposed approach allows detecting 18.5x and 1.8x more unhealthy 22kV and 6.6kV XLPE distribution cables, respectively.

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