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- W2953839851 abstract "Abstract The Lissajous figure of voltage and current signals of a transmission line follows a distinct pattern during power swing, although it deviates from its trend when a fault occurs. This feature of Lissajous figure is utilized in this article for detection and classification of faults during power swing. Autoregression technique is applied to obtain the trend of Lissajous figure and the deviation of actual Lissajous figure from this trend is calculated using Euclidean norm. Quarter-cycle moving window sum of Euclidean norm is used to obtain fault detection index for detection of faults during power swing. For identification of faulty phase, fault features are also calculated for three phases and ground using fault signals of only half-cycle duration after fault. The proposed method is tested on a hybrid transmission line consisting of an overhead line and an underground cable, simulated in electromagnetic transient program (EMTP). It can detect faults quickly and classify them with high accuracy irrespective of fault type, fault location, fault resistance, load angle, fault inception time and swing frequency. The method performs satisfactorily even under noisy condition upto 30 dB Gaussian noise. Finally, its performance is compared with one conventional and two novel methods to establish its efficacy." @default.
- W2953839851 created "2019-07-12" @default.
- W2953839851 creator A5062964612 @default.
- W2953839851 date "2019-10-01" @default.
- W2953839851 modified "2023-09-26" @default.
- W2953839851 title "A new technique for detection and classification of faults during power swing" @default.
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- W2953839851 doi "https://doi.org/10.1016/j.epsr.2019.105920" @default.
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