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

This paper proposes a new fault diagnosis approach based on combined wavelet transform and adaptive neuro-fuzzy inference system for fault section identification, classification and location in a series compensated transmission line. It performs an effective feature extraction approach based on norm entropy in order to obtain the features represented main frequency, harmonic and transient characteristics of the fault signals. The proposed method uses the samples of fault voltages and currents for one cycle duration from the inception of fault. The feasibility of the proposed method has been tested on a 400 kV, 300 km series compensated transmission line for all the ten types of faults using MATLAB/Simulink for a large data set of 23,436 fault cases comprising of all the 10 types of faults. Fault signals varying with fault resistance, fault inception angle, fault distance, load angle, percentage compensation level and source impedance are applied to the proposed algorithm. The results also indicate that the proposed method is robust to wide variation in system conditions and has higher fault diagnosis accuracy with regard to the other approaches in the literature for this problem.

► We developed an algorithm to diagnose the faults in the series compensated transmission lines.
► The main idea in this paper is to capture the effective features of the fault voltages and currents.
► The proposed methodology has been tested for a variety of fault conditions.
► Our algorithm diagnoses the faults more effectively.