Prediction Model for Pollution Flashover on Glass Insulator According to Acoustical Characteristics

doi:10.12204/j.issn.1000-7229.2021.05.015

Abstract

Abstract:

Insulator pollution flashover is a main disaster of electrical power system. A large number of artificial pollution tests are investigated under different contamination levels (different soluble contaminants densities or dust densities). According to experiment data, seven acoustic signal characteristics are extracted and analyzed. According to the conclusion, the general regression neural network (GRNN) model of risk degree prediction is established, in which the seven acoustic signal characteristics are as the inputs with the risk degrees used as outputs. It is found that the prediction accuracy is affected by soluble contaminants density mostly. The results show that the greater the soluble contaminants density, the smaller the acoustic signal characteristics’randomness, and the better prediction accuracy can be obtained. The conclusion of this paper provides reference for acoustic monitoring of insulators in different regions with different pollution levels.

Key words: contaminant discharge, insulator, acoustical signal, risk degree prediction, general regression neural network (GRNN)

CLC Number:

TM85

WANG Yuandong, SHI Wenjiang, HAN Xingbo, JIANG Xingliang, ZHANG Chao, ZHANG Zhijin. Prediction Model for Pollution Flashover on Glass Insulator According to Acoustical Characteristics[J]. ELECTRIC POWER CONSTRUCTION, 2021, 42(5): 138-144.

Figures/Tables 14

Fig.1 Appearance of glass insulator

Fig.2 The test circuit of multifunctional artificial climate chamber

Table 1 Pollution level of insulator strings mg/cm2

Fig.3 Acoustical signal of glass insulator under different risk levels

Table 2 Pollution level of insulator strings

Table 3 Acoustical characteristic values of insulator LXY4-160 under different SCD

Fig.4 Relation between average amplitude of acoustical signal and SCD

Fig.5 Relation between the biggest half cycle area of acoustical signal and SCD

Fig.6 Relation between average amplitude of acoustical signal and NSDD

Fig.7 Relation between the biggest half cycle area of acoustical signal and NSDD

Fig.8 Structure of GRNN network

Fig.9 Flow chart of GRNN model

Table 4 The GRNN predictions of insulators’ hazard degrees under different SCD

Fig.10 The actual values and GRNN predictions of insulators’ hazard degrees(ρNaCl=0.25 mg/cm2)

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