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• W2655606959 abstract "In smart grid power systems, fast and accurate fault detection and diagnosis (FDD) is vital for isolating faulty components and avoiding further complications. This paper introduces a new data fusion method based on ordered weighted averaging (OWA) operator for power smart grids. For this purpose, the discrete time data from circuit breakers (CB) is combined with continuous time data of recorders to enhance the reliability of the fault diagnosis approach. Radial basis functions (RBF) artificial neural network and wavelet transform (WT) are individually employed to identify the location of the fault from the continuous voltage of the buses. Then, a combination of these two methods along with the information from CBs are utilized into a unique framework by OWA operator to diagnose the faults at an early stage. IEEE standard 14 bus system is used to illustrate and validate the proposed method. Several phase to ground faults are injected into the simulation model to validate the diagnostic capability of the FDD system. Simulation results show a better performance of the fusion FDD system in comparison with three other methods." @default.
• W2655606959 created "2017-06-30" @default.
• W2655606959 creator A5038291093 @default.
• W2655606959 creator A5050932217 @default.
• W2655606959 date "2017-04-01" @default.
• W2655606959 modified "2023-09-22" @default.
• W2655606959 title "Data fusion for fault diagnosis in smart grid power systems" @default.
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• W2655606959 doi "https://doi.org/10.1109/ccece.2017.7946717" @default.
• W2655606959 hasPublicationYear "2017" @default.
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