@article{Shahgholiyan_Rezaei_2024, title={Fault Location Scheme in Distribution Systems with Distributed Generations Using Neural Networks}, volume={4}, url={https://oiccpress.com/Majlesi-Journal-of-Electrical-Engineering/article/fault-location-scheme-in-distribution-systems-with-distributed-generations-using-neural-networks/}, DOI={10.1234/mjee.v4i2.333}, abstractNote={Nowadays using DG (distributed generation) in vast variety of cases has been more considerable due to its beneficial advantages, but interconnecting DG to radial distribution systems has some impact on the coordination of protection devices. The main point in the protection scheme is the diagnosis of fault locations, so producing a new method to identify fault location with high accuracy is necessary.This paper presents a novel approach to fault location identification with DG in distributed systems by the means of neural networks. According to this method using a distributed system as intentional islanding in necessary conditions is pos­s­­ible and reduces the ENS (Energy Not Supplied) of the net. Using separate NNs (neural networks) for each island (zone) will increase the accuracy of this method. Impl­e­m­entation results of this scheme on actual distributed systems has been simulated and reported.}, number={2}, journal={Majlesi Journal of Electrical Engineering}, publisher={OICC Press}, author={Shahgholiyan, Ghazanfar and Rezaei, MohammadHosein}, year={2024}, month={Feb.}, keywords={Distributed generation, Neural Networks., Fault Location, Distributed System} }