Enhanced fault classification in underground cable systems: a three-step framework utilizing evolutionary optimization for signal tracking and parameter estimation.

This paper introduces a comprehensive three-step framework for fault classification in underground cable systems. The first step involves the implementation of the Cumulative Sum (CUSUM) technique to detect the fault instances, which will help in precisely tracking the affected portions. The second step focuses on parameter estimation techniques, followed by the third step, which incorporates classification techniques. The study employs a range of evolutionary optimization algorithms, including Ant Colony Optimization, Bacteria Foraging Optimization, Simulated Annealing, Genetic Algorithm, and Particle Swarm Optimization. These algorithms are effectively employed to track and estimate parameters like amplitude, phase, frequency, and damping factor associated with various types of permanent faults, such as L-G fault, L-L fault, L-L-G fault, and L-L-L-G fault generated in the underground cable. The optimized parameters extracted through these algorithms are utilized as features within a Support Vector Classifier for fault classification. The overall accuracy of the classifier using these features is reported to be 0.944, which is quite high and indicates a strong performance in classifying faults. The study includes a brief comparison of the performance of these bio-inspired algorithms. The effectiveness of the tracking algorithms is validated through the use of laboratory-tested current signals. In addition, the signals are subjected to varying signal-to-noise ratios to evaluate the robustness of the tracking algorithm. A comparative analysis is performed to estimate the accurate parameters and highlights the significant tracking ability of the Particle Swarm Optimization (PSO) algorithm, which outperforms other algorithms.The Root Mean Squared Percentage Error (RMSPE) for the L-G fault in PSO is 0.3082, showing superior performance compared to other algorithms. [ABSTRACT FROM AUTHOR]