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• W2003164022 abstract "The development and the progress of science and technology of the power industry is faster and faster. Electric power cables are getting more and more widely used in the power system. It plays an extremely important role in industrial production and modern life. To overcome the problem that the kernel parameter and the punishment factor have great influence on the quality of Support Vector Machine (SVM) model, the Particle Swarm Optimization (PSO) is used to optimize the parameters, and then a kind of Hybrid Method Support Vector Machine (HMSVM) is established for fault recognition. Finally, the HMSVM is applied to the recognition of online power cable faults. It is experimentally proved that, the HMSVM is correct and effective for the fault recognition of the online power cable." @default.
• W2003164022 created "2016-06-24" @default.
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• W2003164022 date "2014-06-01" @default.
• W2003164022 modified "2023-09-30" @default.
• W2003164022 title "Nonlinear Inertia Convergence Classification Model of Online Power" @default.
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• W2003164022 doi "https://doi.org/10.1109/is3c.2014.170" @default.
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