A new method of dissolved gas analysis is proposed to improve the accuracy of transformer fault diagnosis. The slime mold optimized support vector machine (SMA-SVM), and logarithmic arctangent transform (LOG-ACT) are combined. On the one hand, the better global optimization performance of SMA is used to optimize SVM parameters to solve the difficulty of SVM parameter selection. On the other hand, corresponding transformations are carried out for different features: the logarithmic(LOG) transformation is carried out for the original DGA data to retain the order of magnitude information. The arctangent (ACT) transformation is carried out for the ratio features to improve the data structure. Therefore, the combination of data transformation and optimization model can improve the accuracy of diagnosis from two aspects of data structure and classification algorithm. The performance of the proposed method was compared with IEC three ratio method, artificial neural network, optimized artificial neural network, GA-SVM, and PSO-SVM. Experimental results using published data show that the proposed method can significantly improve the accuracy of transformer fault diagnosis. © 2022 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.

中文翻译：

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使用支持向量机和对数反正切变换的油浸式电力变压器故障诊断

为了提高变压器故障诊断的准确性，提出了一种溶解气体分析的新方法。黏菌优化支持向量机 (SMA-SVM) 和对数反正切变换 (LOG-ACT) 相结合。一方面，利用SMA较好的全局优化性能对SVM参数进行优化，解决了SVM参数选择的难点。另一方面，针对不同的特征进行相应的变换：对原始DGA数据进行对数（LOG）变换，保留数量级信息。对比率特征进行反正切（ACT）变换以改进数据结构。所以，数据变换与优化模型相结合，可以从数据结构和分类算法两个方面提高诊断的准确性。将所提方法的性能与 IEC 三比法、人工神经网络、优化人工神经网络、GA-SVM 和 PSO-SVM 进行了比较。使用公开数据的实验结果表明，该方法可以显着提高变压器故障诊断的准确性。© 2022 日本电气工程师学会。由 Wiley Periodicals LLC 出版。使用公开数据的实验结果表明，该方法可以显着提高变压器故障诊断的准确性。© 2022 日本电气工程师学会。由 Wiley Periodicals LLC 出版。使用公开数据的实验结果表明，该方法可以显着提高变压器故障诊断的准确性。© 2022 日本电气工程师学会。由 Wiley Periodicals LLC 出版。