A covariance indices based method for fault detection and classification in a power transmission system during power swing. (February 2019)

• Record Type: Journal Article
• Authors: Musa, Mohammed H.H.; He, Zhengyou; Fu, Ling; Deng, Yujia
• Is Part Of: International journal of electrical power & energy systems. Volume 105(2019)
• Page Start: 581
• Page End: 591
• Publication Date: 2019-02
• Subjects: Covariance measure -- Cumulative approach -- Power swing -- Fault detection -- Faulted phase identification; Electrical engineering -- Periodicals; Electric power systems -- Periodicals; Électrotechnique -- Périodiques; Réseaux électriques (Énergie) -- Périodiques; Electric power systems; Electrical engineering; Periodicals; 621.3
• DOI: 10.1016/j.ijepes.2018.09.003
• Languages: English
• ISSNs: 0142-0615

Abstract

Highlights: The current signals are obtained from both the ends of the transmission lines. The covariance indices of the current signals are obtained. The fault detection and classification during the power swing is performed by using the covariance indices, where the faulted phases recorded much higher values while the healthy phases recorded zero. The proposed scheme has been tested successfully for various type and nature of faults. The variation of fault incidence angle, location, and resistance has not affected the performance of the proposed scheme. 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.