Robotics & Machine Learning Daily News2024,Issue(Mar.21) :58-59.

New Machine Learning Findings from Sichuan University Described (Faulty Feeders Identification for Single-phase-to-ground Fault Based On Multi-features and Mach ine Learning)

Robotics & Machine Learning Daily News2024,Issue(Mar.21) :58-59.

New Machine Learning Findings from Sichuan University Described (Faulty Feeders Identification for Single-phase-to-ground Fault Based On Multi-features and Mach ine Learning)

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Abstract

By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Investigators publish new report on Ma chine Learning. According to news reporting originating from Chengdu, People’s R epublic of China, by NewsRx correspondents, research stated, “The identification of single-phase-to-ground (SPG) faults in power distribution networks is crucia l for ensuring the reliability of power supply. However, the traditional identif ication methods based on a single feature lack accuracy and robustness in comple x fault scenarios.”

Key words

Chengdu/People’s Republic of China/Asi a/Cyborgs/Emerging Technologies/Machine Learning/Sichuan University

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出版年

2024
Robotics & Machine Learning Daily News

Robotics & Machine Learning Daily News

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