Performance Evaluation of an Adaptive-Network-Based Fuzzy Inference System Approach for Location of Faults on Transmission Lines Using Monte Carlo Simulation

This paper employs a wavelet multiresolution analysis (MRA) along with the adaptive-network-based fuzzy inference system to overcome the difficulties associated with conventional voltage- and current-based measurements for transmission-line fault location algorithms, due to the effect of factors such as fault inception angle, fault impedance, and fault distance. This proposed approach is different from conventional algorithms that are based on deterministic computations on a well-defined model to be protected, employing wavelet transform together with intelligent computational techniques, such as the fuzzy inference system (FIS), adaptive neurofuzzy inference system (ANFIS), and artificial neural network (ANN) in order to incorporate expert evaluation so as to extract important features from wavelet MRA coefficients for obtaining coherent conclusions regarding fault location. A comparative study establishes that the ANFIS approach has superiority over ANN- and FIS-based approaches for the location of line faults. In addition, the efficacy of the ANFIS is validated through the Monte Carlo simulation for incorporating the stochastic nature of fault occurrence in practical systems. Thus, this ANFIS-based digital relay can be used as an effective tool for real-time digital relaying purposes.