基于KPCA-SSA-ENN的变压器油界面张力预测

Prediction of interfacial tension of transformer oil based on KPCA-SSA-ENN

针对目前变压器油界面张力的传统检测方法检测时间长、成本高等问题,提出了基于多频超声检测技术和人工智能算法的界面张力预测方法。对选取的175组变压器油样进行圆环法界面张力检测和多频超声波检测,分析了多频超声波信号的幅频响应、相频响应和界面张力之间的相关性。通过核主成分分析(KPCA)预处理多频超声波数据,划分样本集为140组的训练集和35组的测试集,并建立麻雀搜索算法(SSA)优化Elman神经网络(ENN)的界面张力预测模型,预测平均相对误差为6.53%,预测准确率达到93.47%。

Aiming at the problems of long time of detection and high cost in traditional detection methods of interfacial tension of transformer oil,this paper proposes a novel prediction method of interfacial tension based on multi-frequency ultrasonic detection technology and an artificial intelligence algorithm.175 groups of transformer oil samples are measured through the ring interfacial tension method and multi-frequency ultrasonic detection,and the correlation between amplitude-frequency response,phase-frequency response and interfacial tension of multi-frequency ultrasonic signals is analyzed.The multi-frequency ultrasonic data are preprocessed by kernel principal component analysis(KPCA),and the sample set is divided into a training set with 140 groups and a test set with 35 groups.The sparrow search algorithm(SSA)is established to optimize the interfacial tension prediction model of Elman neural network(ENN).The average percentage error of the prediction is 6.53%,and the prediction accuracy reaches 93.47%.