Fault disturbances classification analysis using adaptive neuro-fuzzy inferences system

Abstract

This paper affords the use of neuro-fuzzy technique called the Adaptive Network–based Fuzzy Inference System (ANFIS) to highlight its ability to perform fault disturbances classification tasks using extracted features based on S-transforms methods. The ANFIS model with a five-layered architecture was trained using extracted features to classify signal data comprising various faults disturbances, namely, voltage sag, swell, impulsive, interruption, notch, and pure signal. Results obtained showed that the ANFIS model is very suitable and can generate excellent classification results provided that the right type and number of Membership Functions (MFs) are used in the classification task.