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Fault Location in Radial Distribution Networks Using ANN and Superimposed Components

The fault location is an important issue to be studied in the context of smart grids and microgrids. Thus, this article presents a methodology based on Artificial Neural Networks (ANNs) to locate areas under fault. To train and validate the ANNs, the superimposed components of the voltage and current measurements, acquired by a single meter at the substation, were used. The proposed strategy was tested utilizing the medium voltage distribution system benchmark from CIGRE, including the analysis of different ANN topologies. After several simulations via the PSCAD™/EMTDC™ software, the analysis demonstrated that the proposed methodology is promising and leads to a high level of accuracy in locating areas under fault.