Contamination Degree Prediction of Insulators Based on Hyperspectral Imaging Technology

Insulator image can be acquired by hyperspectral imaging technology in a non-contact way, and hyperspectral image has some advantages such as the properties of multi-band, and merging image and spectrum. For this reason, the paper proposes a method to predict contamination degree of insulators based on hyperspectral imaging technology. Firstly, the hyperspectral image in a band range of 400–1 000 nm is acquired by hyperspectral imaging system, followed by the black-and-white correction. Then, some reflectivity spectrum curves of region of interest (ROI) are extracted and further pre-processed by the methods such as the Savitzky-Golay smoothness, logarithm, or first derivative transformations. Finally, some labeled data of real samples are utilized to build support vector machines based insulator contamination degree prediction (SVM-ICDP) model and partial least squares regression based insulator contamination degree prediction (PLSR-ICDP) model, respectively. The experimental results show that when the first derivative transformation is selected as the pre-processing method, the performance of the ICDP model is superior to those of the others. More specifically, the accuracy of SVM-ICDP reaches 91.84%, and the root mean square error (RMSE) of PLSR-ICDP is 0.024 1.