Deep Learning-Based Fault Location Framework in Power Distribution Grids Employing Convolutional Neural Network Based on Capsule Network

Power distribution grids (PDGs) are one of the main parts of electrical logistic chains with the task of transferring electricity to the consumers continually. Adverse weather conditions, equipment failure, and human disruption can bring about the PDGs to faulty situations leading to the inevitable interrupting power consumption which results in financial losses. Therefore, it is vital to locate the faulty spot accurately and quickly. In this paper, an automatic deep learning framework is implemented to locate faults in the PDGs with limited measurement requirements i. e. only the voltage at the substations. The Spectrogram time-frequency analysis is performed on the voltage signal to obtain more informative training data. A convolutional neural network (CNN) model is utilized and trained to identify the location of the fault in the distribution grid. To provide a more precise outcome, the capsule network is used. This approach determines the location of the faulty section using an offline databank and then estimates the exact faulty point using an online databank of multiple fault scenarios in that section. To evaluate the powerfulness of the proposed method, several simulations are performed on the IEEE 34-node feeder in MATLAB (2020b). For further verification of the proposed method's effectiveness, several laboratory tests are done as well. The results demonstrate that the proposed technique performs exceptionally well in terms of accuracy compared to other state-of-art counterparts, even when merely using the recorded voltage at the substations.