Wu Shuyue, Zhang Heng, Wang Yuxuan, Luo Yiwen, He Jiaxuan, Yu Xiaotang, Zhang Yiyi, Liu Jiefeng, Shuang Feng
School of Electrical Engineering, Guangxi University, Nanning 530004, China.
Polymers (Basel). 2022 Apr 2;14(7):1449. doi: 10.3390/polym14071449.
The predictive model of aging indicator based on intelligent algorithms has become an auxiliary method for the aging condition of transformer polymer insulation. However, most of the current research on the concentration prediction of aging products focuses on dissolved gases in oil, and the concentration prediction of alcohols in oil is ignored. As new types of aging indicators, alcohols (methanol, ethanol) are becoming prevalent in the aging evaluation of transformer polymer insulation. To address this, this study proposes a prediction model for the concentration of alcohols based on a genetic-algorithm-optimized support vector machine (GA-SVM). Firstly, accelerated thermal aging experiments on oil-paper insulation are conducted, and the concentration of alcohols is measured. Then, the data of the past 4 days of aging are used as the input feature of SVM, and the GA algorithm is utilized to optimize the kernel function parameter and penalty factor of SVM. Moreover, the concentrations of methanol and ethanol are predicted, after which the prediction accuracy of other algorithms and GA-SVM are compared. Finally, an industrial software program for predicting the concentration of methanol and ethanol is established. The results show that the mean square errors () of methanol and ethanol concentration predictions of the model proposed in this paper are 0.008 and 0.003, respectively. The prediction model proposed in this paper can track changes in methanol and ethanol concentrations well, providing a theoretical basis for the field of alcohol concentration prediction in transformer oil.
基于智能算法的老化指标预测模型已成为评估变压器聚合物绝缘老化状态的一种辅助方法。然而，目前大多数关于老化产物浓度预测的研究都集中在油中的溶解气体上，而忽略了油中醇类的浓度预测。作为新型老化指标，醇类（甲醇、乙醇）在变压器聚合物绝缘老化评估中日益普遍。为此，本研究提出了一种基于遗传算法优化支持向量机（GA-SVM）的醇类浓度预测模型。首先，对油纸绝缘进行加速热老化实验，并测量醇类浓度。然后，将过去4天的老化数据作为支持向量机的输入特征，利用遗传算法优化支持向量机的核函数参数和惩罚因子。此外，对甲醇和乙醇的浓度进行预测，并将其他算法与GA-SVM的预测精度进行比较。最后，建立了预测甲醇和乙醇浓度的工业软件程序。结果表明，本文提出的模型对甲醇和乙醇浓度预测的均方误差分别为0.008和0.003。本文提出的预测模型能够很好地跟踪甲醇和乙醇浓度的变化，为变压器油中醇类浓度预测领域提供了理论依据。
