Fault location for a series compensated transmission line based on wavelet transform and an adaptive neuro-fuzzy inference system

Fault diagnosis is a major area of investigation for power system and intelligent system applications. This paper proposes an efficient and practical algorithm based on using wavelet MRA coefficients for fault detection and classification, as well as accurate fault location. A three-phase transmission line with series compensation is simulated using MATLAB software. The line currents at both ends are processed using an online wavelet transform algorithm to obtain wavelet MRA for fault recognition. Directions and magnitudes of spikes in the wavelet coefficients are used for fault detection and classification. After identifying the fault section, the summation of the sixth level MRA coefficients of the currents are fed to adaptive neuro-fuzzy inference system (ANFIS) to obtain accurate fault location. The proposed scheme is able to detect all types of internal faults at different locations either before or after the series capacitor, at different inception angles, and at different fault resistances. It can also detect the faulty phase(s) and can differentiate between internal and external faults. The simulation results show that the proposed method has the characteristic of a simple and clear recognition process. We conclude that the algorithm is ready for series compensated transmission lines.