Research on Composite Insulator Degradation Diagnosis Method Based on Infrared Image Feature Parameters and GK-SVM
The degradation and local temperature rise of composite insulators have become a serious prob-lem in the operation of transmission systems. Infrared image can be used for identifying the heating of composite insulators,however,the complex factors affecting the heating of composite insulators and the lack of effective identification methods have hindered the effective application of infrared image. This research considered conditions such as wind speed,humidity,carbonization channel length,and moisture intrusion to establish a electricity-heat-flow multi-physical field simulation model for composite insulator carbonization channels. The factors affecting the heating of deteriorated composite insulators were system-atically studied,and a Granular Gaussian Kernel Support Vector Machine (GK-SVM) fault identification method based on environmental input parameters and infrared image characteristic parameters was proposed. Research had shown that voltage level,wind speed,humidity,carbonization channel length,and mositure intrusion all had an impact on the temperature at the defect location of composite insulators. Under the same conditions,the relative temperature rise at the defect location of 220 kV composite insula-tors was the highest,reaching 13.2 ℃. The length of carbonization channel defects and the level of mois-ture intrusion had a significant impact on the temperature at the defect site of composite insulators. When the defect length increased from 0 to 200 mm,the temperature at the defect site increased by 4.3 times relative to the environmental temperature rise. When the moisture intrusion level increased from 0 to 3,the temperature at the defect site increased by 0.7 times relative to the environmental temperature rise. Both humidity and wind speed had a negative impact on the temperature at the defect site. When the envi-ronmental humidity increased from 30％ to 80％,the temperature at the defect site decreased by 20.6％ relative to the environmental temperature rise. When the environmental wind speed increased from 0 to 6 m/s,the temperature at the defect site decreased by 24.2％ relative to the environmental temperature rise. Based on environmental input parameters and infrared image feature parameters,the composite insu-lator heating fault identification method using GK-SVM can achieve identification rate of 93.98％ and 94.67％ with false alarm rate of only 7.14％ and 5.33％ for faulty insulators in 110 kV and 220 kV trans-mission lines,respectively. The research results have important reference value for the application of diag-nosis method of deteriorated composite insulators.
万方数据
维普
