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• W2885202313 abstract "Sudden changes in loading or weak constitution of power network causes power swing which may aggravate miss-operation of protective elements. Consequently, it becomes utmost essential to rapidly and accurately distinguish between fault and power swing conditions to prevent instability in smart power grid equipped with compensation. This study demonstrates an effective disturbance classifier scheme for series compensated transmission line (SCTL) for discrimination during disparity in power grid contexts using sequence-space-based support vector machine (SVM) classifier. The test data sets are generated by performing extensive simulations in PSCAD software by varying system and fault context. SVM architecture has been trained and tested by generating feature vector using modified full cycle discrete Fourier transform in MATLAB. After proper extraction of features of the interest at the time of disturbance, a decision about power swing or fault has been carried out using SVM classifiers. Regulation and kernel function parameter have been tuned using ten-fold cross-validation applied on training set. The developed scheme is also evaluated for symmetrical fault detection during power swing and shows remarkable improvement in accuracy and speed for protection of SCTL in comparison to existing schemes." @default.
• W2885202313 created "2018-08-22" @default.
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• W2885202313 date "2018-11-01" @default.
• W2885202313 modified "2023-10-03" @default.
• W2885202313 title "Sequence‐space‐aided SVM classifier for disturbance detection in series compensated transmission line" @default.
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• W2885202313 doi "https://doi.org/10.1049/iet-smt.2018.5196" @default.
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