| Summary: | Transformer oil is an important insulating material in power transformers. The detection of transformer oil is an important means to ensure the normal operation of the power system, which is recognized by the public. Interfacial tension (IFT) is an important parameter to characterize the flow properties of liquids. Too low interfacial tension of transformer oil can cause major accidents in transformers. Therefore, it is of great practical significance to achieve effective detection of interfacial tension. This work proposes the measurement of the IFT of transformer oil using multi frequency ultrasonic and a support vector machine that was optimized by particle swarm optimization algorithm (PSO-SVM). 210 samples, which were detected by multi frequency ultrasonic and the Ring Method, were divided into training sets (200 samples) and test sets (10 samples). Then multi-frequency ultrasonic data is subjected to MDS dimensionality reduction to obtain 11 dimensional low-dimensional data which as the input of SVM, and the IFT, which was detected by the Ring Method, as the output of SVM. After that, a particle swarm optimization algorithm (PSO) was incorporated to optimize the parameters (C, g) for a support vector machine (SVM).Then, the SVM model with the optimized parameters (g=1.356 and C=2.831) was trained with the training sets, and the model was verified with the test sets. Results show that the model, which proposed in this article, that describes the nonlinear relationship between multi frequency ultrasonic data and the interfacial tension of transformer oil shows higher accuracy.
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