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Insufficient transformer fault diagnosis rate has always been a key problem restricting the safety and low efficiency of power grid operation. To solve this problem, a transformer fault diagnosis method based on improved seagull optimization algorithm support vector machine (ISOA-SVM) was proposed. Firstly, the fault diagnosis model of dissolved gas analysis in oil based on SVM was constructed, and the data in oil was processed by kernel principal component analysis (KPCA). Secondly, the optimal kernel function parameters and penalty coefficient of SVM were found by ISOA. Finally, the data was normalized into the ISOA-SVM model for diagnosis, and the operational state of the transformer was judged. The results were compared with other algorithm optimization models. The simulation results show that the fault detection method of the model is significantly superior to other models in fault identification speed and accuracy, which helps to ensure the stable operation of the transformer. [ABSTRACT FROM AUTHOR]

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Copyright of Electric Power Automation Equipment / Dianli Zidonghua Shebei is the property of Electric Power Automation Equipment Press and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Electric Power Engineering Technology is the property of Editorial Department of Electric Power Engineering Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Electric Power Engineering Technology is the property of Editorial Department of Electric Power Engineering Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)