  Electrical fault classification is vital for ensuring the reliability and
safety of power systems. Accurate and efficient fault classification methods
are essential for timely and effective maintenance. In this paper, we propose a
novel approach for effective fault classification through Grassmann manifolds,
which is a non-Euclidean space that captures the intrinsic structure of
high-dimensional data and offers a robust framework for feature extraction. We
use simulated data for electrical distribution systems with various types of
electrical faults. The proposed method involves transforming the measurement
fault data into Grassmann manifold space using techniques from differential
geometry. This transformation aids in uncovering the underlying fault patterns
and reducing the computational complexity of subsequent classification steps.
To achieve fault classification, we employ a machine learning technique
optimized for the Grassmann manifold. The support vector machine classifier is
adapted to operate within the Grassmann manifold space, enabling effective
discrimination between different fault classes. The results illustrate the
efficacy of the proposed Grassmann manifold-based approach for electrical fault
classification which showcases its ability to accurately differentiate between
various fault types.
