Abstract
This paper presents a methodology to estimate the distributed circuit topology and analyze the occurrence of faults at different locations in a distribution circuit. The experiments discussed here show the performance of a convolutional neural network (CNN) based circuit topology estimation model. The proposed approach uses a data based approach to estimate the circuit configuration with faulty and normal data. The results are compared with that of a standard Support Vector Machine (SVM). Furthermore fault classification using a SVM is analyzed. The effectiveness of the proposed method is tested using data obtained from power simulations on the modified IEEE 123 bus system using MATLAB Simulink
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