A probabilistic neural network assessment method for insulator pollution level based on discharge noise

Abstract

The insulators of a transmission line are exposed to air for a long time, a mount of dust particles will be deposited on the surface. When there rain, fog or high humidity, the surface of the insulator will discharge or even flashover to breakdown. For this, in this paper, the characteristic parameters of insulator discharge noise under different pollution levels, ambient temperature and relative humidity are extracted, and the influence of insulator discharge noise are analyzed respectively. Probabilistic neural network (PNN) model training samples are used to propose an insulator pollution level assessment method based on discharge noise and verify it. The results show that the pollution level has obvious influence on the characteristic parameters of insulator discharge noise. The higher the pollution level, the more obvious the discharge phenomenon and the greater the discharge intensity. When the relative humidity ranges from 70 % to 90 %, the greater the relative humidity, the greater the pollution discharge intensity of the insulator. The energy of the noise signal gradually concentrated in the 5.5 kHz to 8.3 kHz frequency band, and the pollution discharge in the 5.5 kHz to 16.5 kHz frequency band reflected in the low-frequency part of the information increased. Ambient temperature has no obvious effect on insulator discharge noise. The precision rate of the assessment method for pollution levels Ⅰ, Ⅱ, Ⅲ and Ⅳ are 91.5 %, 88.2 %, 86.5 % and 94.0 %, respectively.