With the gradual merger of automotive smart grid and intelligent transportation, the safety and stability of automobile sitcoms have been paid attention to, which poses a challenge to the maintenance and security of existing power equipment. Therefore, this paper presents a multi-feature fusion of electric vehicle smart grid OLTC fault characteristics selection. This method mainly relies on multi-feature fusion and improved gray wolf learning algorithm to optimize the characteristic selection method of the nuclear limit learning machine. Firstly, in view of the defects of the traditional OLTC fault characteristics which are relatively single and the diagnostic accuracy is low, this paper combines multi-scale weighted column entropy and energy correlation coefficients to construct high-dimensional features, then uses the analysis of nuclear main components to filter the initial matrix to form an initial subset, and finally uses IGWO to optimize the nuclear limit learning machine while selecting features. The test results prove the effectiveness of this method and can accelerate the integration of smart grid and intelligent transportation.
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