Health Assessment of HV Cable Based on Data Correlation Analysis and Fuzzy Rules Joint Algorithm

In recent years, HV cable is increasingly considered as one of the optimal choices for power transmission and distribution in urban power grid. The monitoring data for HV cables becomes huge, and effective measures are urgently required to sort these big data and evaluate the health condition of HV cable. In this paper, an intelligent HV cable health condition evaluation method which combines correlation analysis and the fuzzy rules on the multi-source cable monitoring data was proposed. Firstly, four groups of correlation classification for data mining were carried out on 13 types of cable monitoring data collected from 22 practical HV cable routes, consequently 9 classes of characterized monitoring data were sorted through correlation analysis. Secondly, the statuses of all the cable routes routes were evaluated by making fuzzy rules according to the characterized data. Eventually, the scores indicating the health status of each cable route was obtained. The research results indicating that this joint algorithm could successfully distinguish HV cables in abnormal and severe heath conditions. The data mining and classification method can be used as a good reference for inspection and maintenance schedule for HV cables.