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• W3135586723 abstract "In this work, different types of power system faults at various distances have been identified using a novel approach based on Discrete S-Transform clubbed with a Fuzzy decision box. The area under the maximum values of the dilated Gaussian windows in the time-frequency domain has been used as the critical input values to the fuzzy machine. In this work, IEEE-9 and IEEE-14 bus systems have been considered as the test systems for validating the proposed methodology for identification and localization of Power System Faults. The proposed algorithm can identify different power system faults like Asymmetrical Phase Faults, Asymmetrical Ground Faults, and Symmetrical Phase faults, occurring at 20% to 80% of the transmission line. The study reveals that the variation in distance and type of fault creates a change in time-frequency magnitude in a unique pattern. The method can identify and locate the faulted bus with high accuracy in comparison to SVM." @default.
• W3135586723 created "2021-03-15" @default.
• W3135586723 creator A5041277585 @default.
• W3135586723 creator A5054425386 @default.
• W3135586723 date "2022-01-25" @default.
• W3135586723 modified "2023-10-04" @default.
• W3135586723 title "Fuzzified time-frequency method for identification and localization of power system faults" @default.
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• W3135586723 doi "https://doi.org/10.3233/jifs-189769" @default.
• W3135586723 hasPublicationYear "2022" @default.
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