Novel location algorithm for single-line-to-ground faults in transmission line with distributed parameters

Precise fault location (FL) is definitely important for fast fault clearance and restoration of energy transmission. The single-terminal FL is mostly met in applications because of its easy implementation, but the accuracies of all-type impedance FL algorithms are not good because of the effects of fault resistance and variation of an opposite terminal equivalent system impedance. The recently developed and assembled FL algorithm presented in this study, combining the stability of the impedance FL and precision of the travelling waves FL, is an expected good solution. However, the key point to achieve accurate FL is to naturally couple these two algorithms. As is known, the distributed parameters model is also the research base of these two algorithms. Hence, the impedance FL algorithm in line with distributed parameters is proposed in this study. It is achieved on the discovery that the negative sequence current at relay location maintains precisely the same phase to the voltage at fault point. The proposed algorithm is immune to shunt capacitance because of modelling with distributed parameters, and is not disturbed by ground resistance when calculated at zero-crossing moment of voltage at fault point. Simulations and tests prove its good performance.

[1]  C. Christopoulos,et al.  A single ended fault location scheme , 2001 .

[2]  E. Dirks,et al.  Investigation of a hybrid travelling wave/impedance relay principle , 2002, IEEE CCECE2002. Canadian Conference on Electrical and Computer Engineering. Conference Proceedings (Cat. No.02CH37373).

[3]  D. Costello,et al.  Impedance-Based Fault Location Experience , 2006, 2006 IEEE Rural Electric Power Conference.

[4]  Wang Kehong,et al.  Optimizing solution of fault location , 2002, IEEE Power Engineering Society Summer Meeting,.

[5]  Javad Sadeh,et al.  A new and accurate fault location algorithm for combined transmission lines using Adaptive Network-Based Fuzzy Inference System , 2009 .