An Online Data-Efficient HV Cable Fault Localization Approach Via an AI-Enabled Recognition of Modal Wavefront in Sheath Current

Precise permanent fault localization is an important task for fast power restoration in underground HV cable systems. Among online fault localization methods, the fault localization accuracy strongly relies on the type and volume of measurement, and there is always a tradeoff between localization accuracy and measurement cost. To balance the tradeoff, a data-efficient HV cable fault localization framework is proposed in this paper. First, the fault characteristics of sheath currents are analyzed in modal mode, and compared with the conventional core conductor measurements. It is discerned that the sum of three phases sheath currents has similar characteristics, which can be measured by fewer sensors in a lower rating as the replacement of the conventional core conductor measurements. Second, the challenges of recognizing the wavefront arrival in the sheath are presented, and a Convolution Neural Network is introduced for the localization purpose. The proposed approach can realize high localization accuracy with low-cost measurement, and keep consistent performance under various scenarios through limited training datasets. A case study has been carried out using PSCAD/EMTDC platform to validate the effectiveness and feasibility of the proposed approach, followed by a discussion on the results.