Sequence-Space-Aided Disturbance Classifier Scheme Based on Support Vector Machine

Abstract

Smart power grids are equipped with series compensation in order to improve the power transfer capability. Sudden changes in loading or weak constitution of series compensated transmission line network cause 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 research demonstrates an effective disturbance classifier scheme for series compensated transmission line 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. The developed scheme is also evaluated for symmetrical fault detection during power swing and shows remarkable improvement in accuracy and speed for protection of series compensated transmission line in comparison to existing schemes.