This paper uses the Concurrent Neuro-Fuzzy (CNF) technique to detect, locate, and classify faults in short and long power transmission lines. Results show that CNF improves fault prediction, saves time for technical teams, and enhances system reliability and efficiency.
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This paper compares Concurrent Neuro-Fuzzy and Decision Tree techniques for classifying and locating faults in a nine-phase transmission line system. Results show that these AI methods can effectively improve fault prediction and efficiency in future high-voltage, long-distance power systems.
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This paper designs and analyzes a nine-phase transmission line system using Matlab/Simulink. It addresses issues like environmental impact, frequent faults, and limited power capacity in three-phase systems. Results show that a nine-phase line can transmit up to 9.6 times more load than a three-phase line.
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