Research on the Prediction Method of Equipment Status in Power Transformation and Distribution Based on Grey Model

Abstract

With the continuous increase in the demand for electricity in our country, the reliability requirements of the power distribution network are gradually increasing, and the difficulty of predicting the state of key equipment in the power distribution network is also increasing. In response to this issue, this paper proposes a typical reliability prediction model for key equipment in the power distribution network based on an unbiased grey correlation model. Firstly, a grey model is established to model the reliability of typical key equipment in the power distribution network, and to conduct reliability analysis and life prediction. Secondly, an unbiased grey model is introduced to avoid the problem of the grey model leading to less than ideal life prediction accuracy when the growth rate of the original data sequence is high. Finally, taking the No. 1 main transformer of the 220 kV Lanshan substation under the jurisdiction of Ningxia Shizuishan Power Supply Company as an example, the oil chromatography data is cleaned using the KNN interpolation method and input into the prediction model to analyze and predict the failure rate during actual operation. The results show that this model has certain advantages in terms of life prediction accuracy and calculation time, and can play an auxiliary role in the process of predicting the state of key equipment in the power distribution network.