A wavelet-adaptive network based fuzzy inference system for location of faults in parallel transmission lines

In a power system, transmission line are exposed to the wrath of the nature, which makes it susceptible to the occurrence of faults. Such faults have to be cleared at the earliest to deliver utmost continuity of supply to the consumers. In this respect, the primary aim is to produce a methodology which supports numeric relays in precisely detecting, classifying and locating the fault in transmission lines within the minimum possible time. This paper uses a digital signal processing (DSP) based frequency domain approach to overcome the limitations of fault inception angle and fault location associated with conventional time domain relaying methodologies. The proposed methodology uses wavelet multi-resolution analysis to extract harmonic features from the current signal at the instant of occurrence of fault. It further aims at enhancing the performance of numeric relays in locating the faults in parallel transmission lines by using intelligent techniques. The paper proposes and validates a fault locating methodology using wavelet multi-resolution analysis and adaptive neuro-fuzzy inference system (ANFIS) on a two bus parallel transmission system using MATLAB. Various fault inception angles are considered in the case studies for proving the efficacy of the proposed methodology. This paper would support organizations developing numeric relays for accurate localization of transmission line faults.