首页|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)
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)
扫码查看
点击上方二维码区域，可以放大扫码查看
原文链接
NETL
NSTL
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.”
ChengduPeople’s Republic of ChinaAsi aCyborgsEmerging TechnologiesMachine LearningSichuan University
NETL
NSTL
