New support vector machine-based digital relaying scheme for discrimination between power swing and fault

This study presents a new support vector machine (SVM)-based identification method which effectively discriminates between various types of fault and power swing conditions. Different power swing cases, fault cases and fault during power swing cases have been generated with varying fault and system parameters using PSCAD/EMTDC software package. The performance of the developed algorithm has been tested over 3510 testing dataset. Using three phase current samples for half cycle duration of each simulation case of post-fault/power swing conditions, an overall classification accuracy of 98.71% is achieved. The proposed scheme is capable to detect faults during power swing condition accurately as the current waveform/signatures for both the cases are entirely different. On the other hand, the conventional scheme do not provide effective discrimination in the said situation as they compare magnitude/phase of the current signals (require pre/post-processing of signals). In addition, conventional scheme gives poor accuracy during unknown system/unseen dataset whereas the proposed scheme provides promising accuracy (97.61%) in the said situation.