An Extreme-Learning-Machine-Based Hyperspectral Detection Method of Insulator Pollution Degree

The on-line detection of insulator pollution degree of transmission lines is important to the prevention and control of flashover. This paper proposed a non-contact detection method of insulator pollution degree based on hyperspectral technique. Firstly, hyperspectral images of the samples with different pollution degrees were obtained by hyper-spectrometer. Secondly, after original hyperspectral images were corrected by black-and-white correction and multiplicative scatter correction, hyperspectral curves from the region of interest (ROI) of corrected images were obtained. Finally, a multiclassification model of extreme learning machine (ELM) was built to realize the pollution degree classification of test samples. The results show that the absorption peak, the position of reflection peak, amplitude and the change trend of the hyperspectral curve obviously change with different kinds of pollution on the surface of silicone rubber, whereas only the amplitude obviously changes with same kind of pollution on the surface of silicone rubber; and the ELM-classification model can accurately and rapidly classify the pollution degree, with the pollution degree classification accuracy of NaCl, CaSO
₄
and mixed NaCl-CaSO
₄
respectively reaching 95%, 97.5% and 97.5%; and finally The ELM model based on hyperspectral curves data of the artificial pollution samples can classify the surface of insulator umbrellas with different pollution degrees, and the classification accuracy of CaSO
₄
and mixed NaCl-CaSO
₄
samples respectively are 87.5% and 90%. Consequently, the results of this study prove that hyperspectral technique has considerable potential for the non-contact detection of insulator pollution degree.