Method for detecting the pollution degree of naturally contaminated insulator based on hyperspectral characteristics

Abstract

Insulator pollution degree detection is of great significance for preventing a flashover. Equivalent salt deposit density, leakage current, and surface pollution layer conductivity are commonly used to describe insulator pollution degree; however, all these parameters have limitations in field application and real-time monitoring. Non-contact detection methods, such as infrared thermal imaging and ultraviolet imaging, only image insulators in a specific band, which makes the extracted features limited. Hyperspectral technology is the new comprehensive image data technology based on imaging spectroscopy that has the advantages of multiband high resolution. Therefore, a method based on hyperspectral image and spectral characteristics is proposed to fully characterize natural pollution information and accurately detect the pollution degree of insulators. The hyperspectral spectral line characteristics, image texture, and colour characteristic data of insulators were extracted and fused and then used to establish the pollution degree detection model on the basis of integrated learning classification algorithms. The results show that the model based on fusion data has an accuracy rate of 95.0%, which is more accurate than the model based on spectral line features only. Consequently, hyperspectral technology can realize the non-contact detection of pollution degree and provide some guidance for cleaning the contamination of external insulation.

Pollution morphology characteristics on a superhydrophobic surface and its pollution flashover voltage in DC electric field

“…So the voltage drop between arc electrodes in the pollution flashover model is very low, which can be ignored. At this moment, the pollution flashover model and the Obenaus model [2, 5] is the same, which is shown in Equation (). …”

“…Combining Equations ( 3) and ( 4), the pollution flashover model is shown in Equation (5). It can be seen from Equation ( 5) that when the amount of pollution on the coating surface is small (RðXÞ is small), it will lead to more discrete water droplets on the surface and form local arcs (k is large).…”

“…Insulators on ultra-high voltage (UHV) DC transmission lines are exposed to air for a long time, which are subjected to the influence of the surrounding strong electric field, haze, sand, and other harsh environments. A large amount of pollution deposit on insulator surfaces can lead to major accidents such as pollution flashover, power transmission interruption, and large-scale power failure in cities [3][4][5][6]. Therefore, it is of great value to study the pollution deposition law of the insulator surface under the DC environment and the corresponding prevention technology in view of the pollution problem of the insulator surface in UHV DC lines.…”

Classification of Contaminated Insulators Using k-Nearest Neighbors Based on Computer Vision

“…After evaluating the best configuration of the model, a benchmarking with the decision tree [68], ensemble [69][70][71], support vector machine (SVM) [72][73][74], and the multilayer perceptron models are presented. The pictures of the insulators were taken before the measurement of the NSDD, so that there was no influence from the operator on the contamination; if the contamination did not meet the requirement of IEC 60815 (Annex C) [52], the process was repeated from the beginning.…”

Classification of Contaminated Insulators Using k-Nearest Neighbors Based on Computer Vision

“…After evaluating the best configuration of the model, a benchmarking with the decision tree [48], ensemble [49,50] and support vector machine (SVM) [51][52][53] models is presented. The pictures of the insulators were taken before the measurement of the NSDD, so that there was no influence from the operator on the contamination, if the contamination did not meet the requirement of IEC 60815 (Annex C) [32] the process would be repeated since the start.…”