Remaining lifespan prediction of cross‐linked polyethylene material based on GM(1, N) grey models

Abstract

Cross‐linked polyethylene (XLPE) cables are a critical component of power transmission systems. As operating conditions sometimes exceed the thresholds set in discipline, some cables fail to reach their designed lifespan, and consequently evaluating the aging degree of cables is particularly necessary. Here ten cables with an operating duration between 1 and 11 years from adjacent lines were sampled. Investigations were performed through various aging evaluation methods. Then typical lifespan prediction indices were concluded from test results and filtrated through correlation calculation. Meanwhile, a lifespan prediction method based on GM (1, N) model was proposed to establish the equivalent relationship between operating time and these indices. The research results indicated that, lifespan prediction indices obtained were all significantly correlated with operating time, which can be used to characterize aging degree of XLPE cables. Besides, due to the high prediction accuracy of experimental verification, the GM (1, N) model was proved to be applicable to evaluate the aging degree quantitatively and determine the remaining lifespan (relative to the designed lifespan) of XLPE cables. In future research, this method can be used to formulate a reasonable and economic withdrawal strategy for XLPE cables operating in conditions that exceed the thresholds set in the discipline.

Electrical treeing behaviours in cross‐linked polyethylene cables after thermal ageing

“…Regarding reference [23], the absorption peak at 1720 cm −1 corresponds to a carbonyl group and can be used to determine the thermal ageing degree of XLPE cable insulation. In addition, the absorption peaks at 1051, 1087 and 1159 cm −1 represent Irganox 1035 antioxidant [24], a blocked phenol antioxidant belonging to the chain terminating donor (CB‐D) antioxidant.…”

“…In regards to ref. [24], there is a significant negative correlation between the crystallinity X c and relative permittivity ɛ r , indicating that an increase in X c would result in an increase in the Schottky Barrier φ , as well as a decrease in the trap density and the trapped energy level. In this case, the charge carriers trapped in deep trapping centres would decrease, decreasing electrical treeing initiation voltage.…”

Pricing and Replenishment Decision of Vegetable Products

“…To improve the accuracy of problem prediction, a BP neural network model needs to be combined. Therefore, this article constructs a combined mathematical model based on grey GM (1,1) [6] and BP neural network [7] for prediction.…”