Machine Learning Algorithm for Modelling and Analysis of Faults in Secondary Power Distribution Networks

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Artificial intelligence-driven approach improves fault detection and classification in power networks, highlighting system reliability.

ANFIS achieved 99.7% accuracy in fault classification and 0.5% error in distance estimation.
Machine learning classifiers delivered high precision in fault detection across multiple load conditions.
Custom-designed sensing prototype collected voltage and current data under simulated fault scenarios.
Integrating advanced sensors can enhance real-time fault management and assist distribution utilities.

Udofia et al. (2025) studied this question.

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