基于改进灰色模型的变压器绝缘故障预测

Transformer Insulation Fault Prediction Based on the Improved Grey Model

摘要: 在变压器的故障类型中，绝缘故障是最为常见的一种情形。对变压器的绝缘故障进行有效预测对电力系统的安全稳定运行具有重要意义。为提高传统灰色模型的预测精度，本文提出了一种基于改进灰色模型的变压器绝缘故障预测方法。该方法首先通过对变压器油中溶解气体的关联性进行分析判别是否适合绝缘故障预测，然后利用改进灰色模型对变压器油中溶解气体的浓度进行预测，最后根据变压器油中溶解气体的浓度判别绝缘故障类型。仿真实验结果表明，所选用的改进灰色模型较传统灰色模型具有更好的预测精度，为变压器绝缘故障预测提供了一种有效方法。

Abstract: One of the most common types of transformer failure is insulation fault. Effective prediction of transformer insulation failure is of great significance for the safe and stable operation of the power system. To improve the prediction accuracy of traditional grey models, this paper proposes a transformer insulation fault prediction method based on improved grey models. This method firstly determines the prediction of insulation fault by analyzing the correlation of the dissolved gas in the transformer oil, then uses the improved gray model to predict the concentration of dissolved gas in the transformer oil, and finally determines the insulation fault type of according to the concentration of the dissolved gas in the transformer oil. The simulation experiment results demonstrate that the selected improved gray model exhibits superior prediction accuracy compared to the traditional gray model, which provides an effective approach for transformer insulation fault prediction.