Research on Lifespan Prediction of Composite Insulators in a High Altitude Area Experimental Station

Abstract

In this paper, composite insulators of the same batch from Factory A, aged for 1–12 years in a high altitude area experimental station of Hunan province, were sampled. In order to investigate the changing law of lifespan prediction parameters with aging time, widely accepted testing methods, such as the static contact angle (CA) and hardness, were employed for composite insulators in accordance with previous research. Based on test results, lifespan prediction parameters were concluded and some parameters significantly correlated with aging time were filtered by means of correlation calculation. On this basis, a prediction method which can be used to determine the aging time of composite insulators was proposed based on a back propagation (BP) neural network. Test results indicate that parameters, including the static contact angle (θav), the relative content of Si (XSi) and O (XO) elements, and salt-fog flashover voltage (Uf), have significant correlation with aging time, and that these parameters can be used to evaluate the aging degree of composite insulators. In addition, due to the high accuracy in experimental verification, the method proposed in this paper can be used to predict the aging time of composite insulators from Factory A in high altitude areas in future research.

Mechanical Properties of High Temperature Vulcanized Silicone Rubber Aged in the Natural Environment

“…The remaining substances were inorganic fillers, including Al 2 O 3 formed after dehydration of aluminum trihydrate (ATH) and silica. Zhang et al [ 5 ] studied the influence of different factors in the natural environment on the hydrophobicity of sheds. Ultraviolet rays and rapid changes in temperature reduced hydrophobicity.…”

Research on Lifespan Prediction of Cross-Linked Polyethylene Material for XLPE Cables

“…Due to the multi-dimensional and non-linear relation between the lifespan prediction parameters and the operating time of XLPE cables, there are some deviations in using a traditional simplified statistical method to characterize the aging degree and predict the lifespan of XLPE cables compared with the actual results. A BP neural network, a system with self-learning, self-organization and self-adaptive ability, can be used to obtain the implied relationship between input and output data through learning and training the input and output data in its network model [18]. In this case, a BP neural network was employed in this paper to establish the multi-dimensional nonlinear relationship between four lifespan prediction parameters obtained in Section 3.1 and the operating time.…”

“…compared with the actual results. A BP neural network, a system with self-learning, self-organization and self-adaptive ability, can be used to obtain the implied relationship between input and output data through learning and training the input and output data in its network model [18]. In this case, a BP neural network was employed in this paper to establish the multi-dimensional nonlinear relationship between four lifespan prediction parameters obtained in Section 3.1 and the operating time.…”

Flashover voltage and external insulation design for EMUs’ insulators at high altitude with non‐uniform distributed pollution

“…In traction power supply systems, the electrical insulation level is mainly determined by the lightning shock withstand level [40][41][42][43], and insulators meet the lightning shock and industrial frequency fouling withstand voltages when the industrial frequency dry and wet withstand voltages are also generally met. If the lightning surge withstand voltage at an altitude of 3500 mm is taken as a reference, the 50-Hz withstand voltage U AC can be obtained at this time, U AC,3.5 .…”