A comprehensive micro-grid fault protection scheme based on S-transform and machine learning techniques

Abstract

This manuscript presents a novel micro-grid protection scheme based on S-transform (ST) and machine learning techniques. Initialisation of the proposed approach is done by extracting the current signals from the targeted buses of different feeders and pre-processing through ST to derive different needful differential features. The extracted features are further used as an input vector to the machine learning model to classify the fault events. The proposed micro-grid protection scheme is tested for different protection scenario, such as the type of fault (symmetrical, asymmetrical and high impedance fault), micro-grid structure (radial and mesh) and mode of operation (islanded and grid connected), etc. Three different machine learning models are tested and compared in this framework: naïve Bayes classifier (NBC), support vector machine (SVM) and extreme learning machine (ELM). The extensive simulated results from a standard IEC micro-grid model prove the effectiveness and reliability of proposed micro-grid protection scheme.