A covariance indices based method for fault detection and classification in a power transmission system during power swing

Abstract

This paper presents a new scheme based on a combination of the current signals covariance with the cumulative approach to identify faults in the power transmission system during the power swing conditions. Primarily, the covariance is used to extract the features which are useful to identify the fault from the current signals that measured at both terminals. The cumulative approach is used to enlarge the fault feature and then create a convenient index for detection and classification during the power swing. The proposed algorithm has been tested through different fault circumstances such as multiple fault locations, multiple fault resistances, and multiple fault inception time. Moreover, fault happened nearby the terminal, fault considering variable loading angles, sudden load change, power flow direction change, faults in the presence of series compensation and fault occurred in the presence of noise are also considered. The empirical results show that the approach proposed in this study has made a reasonable time response, where the fault could be detected within a few milliseconds after the fault inception. Additionally, the simple computation process depicting our proposal makes it more suitable and efficient for practical engineering applications.