Freezing fraction and collision coefficient are the key parameters in the ice accretion model, which should be calculated to predict the icing trend for transmission lines. Here a mathematical relation between freezing fraction and collision coefficient was derived using the simplified head balance model of transmission lines icing. Combined with the empirical formula of the liquid water content in air, a calculation method of the freezing fraction and collision coefficient was proposed using the ice surface temperature of transmission lines and the icing mass growth rate. The method to measure the icing mass growth rate based on online monitoring data and icing experiments was proposed. The icing experiments were carried out in the low‐temperature ice chamber with the same parameters as the online monitoring environmental parameters. The values of the freezing fraction and collision coefficient were calculated by using the online monitoring data and icing experiments data respectively, the calculation results were compared. The maximum relative differences between the calculation results from the online monitoring data and the icing experiments data do not exceed 18%, which proves the validity of the calculation method of the freezing fraction and collision coefficient in ice accretion model of transmission lines.

Abstract:The hydrophobicity of composite insulators is a great significance to the safe and stable operation of transmission lines. In this paper, a recognition method of the hydrophobicity class (HC) of composite insulators based on features optimization was proposed. Through the spray method, many hydrophobic images of water droplets on the insulator surface at various hydrophobicity classes (HCs) were taken. After processing of the hydrophobic images, seven features were extracted: the number n, mean eccentricity E av and coverage rate k 1 of the water droplets, and the coverage rate k 2 , perimeter L max , shape factor f c , and eccentricity E max of the maximum water droplet. Then, the maximum value ∆x max , the minimum value ∆x min , and the average value ∆x av of the change rate of each feature value between adjacent HCs, and the volatility ∆s of each feature value, were used as the evaluation indexes for features optimization. After this features optimization, the five features that are most closely related to the HC were obtained. Lastly, a recognition model of the HC with the five features as input and the seven HCs as output was established. When compared with the spray method and the contact angle method, the correct rate of the proposed recognition method was 98.1% and 95.2%, respectively. The influence of subjective factors on the spray method was effectively overcome.