Locating faults in smart grids using neuro-fuzzy networks
Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics
Smart grids aim to move the energy industry into a new era of availability, quality, reliability, and efficiency of power at generation, transmission and distribution levels. Transmission lines, like the arteries of smart grids, play an important role in delivering high-quality power from the generation units to the consumers. However, because of their vast geographical spread, they are always exposed to different threats. This paper proposes a computational intelligence method for increasing the protection of the transmission lines in smart grids. Three-phase current measurements of only one side of the faulty transmission line have been collected and passed through a signal processing module to extract novel informative features from the transient current signals generated due to the fault occurrence. Obtained features are then fed to the fault location algorithms to construct predictive models including adaptive neuro-fuzzy inference systems (ANFIS), support vector regression (SVR), and artificial neural networks (ANN) to estimate the exact location of the fault. Multiple scenarios have been simulated on the IEEE 14-bus system, and the attained results validate the superiority of the ANFIS over the other methods.
Hassani, Hossein; Razavinfar, Roozbeh; and Saif, Mehrdad. (2019). Locating faults in smart grids using neuro-fuzzy networks. Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics, 2019-October, 3281-3286.