  Faults on electrical power lines could severely compromise both the
reliability and safety of power systems, leading to unstable power delivery and
increased outage risks. They pose significant safety hazards, necessitating
swift detection and mitigation to maintain electrical infrastructure integrity
and ensure continuous power supply. Hence, accurate detection and
categorization of electrical faults are pivotal for optimized power system
maintenance and operation. In this work, we propose a novel approach for
detecting and categorizing electrical faults using the Hessian locally linear
embedding (HLLE) technique and subsequent clustering with t-SNE (t-distributed
stochastic neighbor embedding) and Gaussian mixture model (GMM). First, we
employ HLLE to transform high-dimensional (HD) electrical data into
low-dimensional (LD) embedding coordinates. This technique effectively captures
the inherent variations and patterns in the data, enabling robust feature
extraction. Next, we perform the Mann-Whitney U test based on the feature space
of the embedding coordinates for fault detection. This statistical approach
allows us to detect electrical faults providing an efficient means of system
monitoring and control. Furthermore, to enhance fault categorization, we employ
t-SNE with GMM to cluster the detected faults into various categories. To
evaluate the performance of the proposed method, we conduct extensive
simulations on an electrical system integrated with solar farm. Our results
demonstrate that the proposed approach exhibits effective fault detection and
clustering across a range of fault types with different variations of the same
fault. Overall, this research presents an effective methodology for robust
fault detection and categorization in electrical systems, contributing to the
advancement of fault management practices and the prevention of system
failures.
