Abstract:

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%.