Health Index Prediction of Underground Cable System using Artificial Neural Network

The application of machine learning (ML) towards the prediction of the insulation health condition of high voltage XLPE cable was emphasized in this work. Deterioration due to aging and partial discharge is the primary cause of cable insulation failure. However, replacement and maintenance of underground cable circuits during the period of excavation are very expensive. The information regarding the severity of the insulation level assists to make smarter informed decisions for system planning and repair prediction. In this work, the interpretation and recognition of the insulation health condition analysed with the help of an Artificial Neural Network (ANN). The classification based on the ANN requires a pre-processing of the input data obtained from the test results. The test result provided information about each sample's Partial Discharge (PD) magnitude, Aging, Neutral corrosion, Loading, Visual condition, etc. This work mainly focused on the application of deep-learning,i.e. multiclass classification of five different health index classes based on the acquired dataset.